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22EC903 - WIRELESS SENSOR NETWORKS
Department: ECE
Batch/Year : 2023-2027 / III YEAR
Created by
1. Mr.Darwin Nesakumar A , Associate Professor/ECE/ RMKEC
2. Mr.Jyothi Prasad M , Assistant Professor/ECE/ RMDEC
3. Ms.Swetha Reddy , Assistant Professor/ECE/ RMKCET
TABLE OF CONTENTS
S.No | Contents | Page Number |
1 | Course Objectives | 7 |
2 | Pre Requisites | 8 |
3 | Syllabus | 9 |
4 | Course Outcomes | 10 |
5 | CO- PO/PSO Mapping | 12 |
6 | UNIT 5- DATA MANAGEMENT AND SECURITY | |
| 6.1 Lecture Plan | 13 |
| 6.2 Activity based learning | 14 |
| 6.3 Lecture Notes | 15 |
| 6.3.1. Data management in WSN | 15 |
| 6.3.2 Storage and indexing in sensor networks | 18 |
| 6.3.3. Query processing in sensor | 23 |
| 6.3.4 Data aggregation | 26 |
| 6.3.5 Directed diffusion | 28 |
| 6.3.6 Tiny aggregation | 30 |
| 6.3.7 Greedy aggregation | 36 |
| 6.3.8 Security Protocols | 40 |
| 6.3.9 Security in WSN | 50 |
S.No | Contents | Page Number |
| 6.4 Assignments | 54 |
| 6.5 Part A Q & A | 56 |
| 6.6 Part B Q | 61 |
| 6.7 Supportive online Certification courses | 63 |
| 6.8 Real time Applications in day to day life and to Industry | 64 |
| 6.9 Contents beyond the Syllabus | 67 |
7 | Assessment Schedule | 69 |
8 | Prescribed Text Books & Reference Books | 70 |
9 | Mini Project suggestions | 71 |
10 | GATE Questions | 72 |
TABLE OF CONTENTS
COURSE OBJECTIVE
PRE REQUISITES
Subject Name : Analog and Digital Communication
Subject Code : 22EC403
Semester : 3
Reason : For knowledge on communication concepts
Subject Name : Wireless Communication
Subject Code : 22EC909 Semester : 5
Reason For knowledge on wireless communication technologies
Subject Name : Computer Networks
Subject Code : 22EC503 Semester : 5
Reason : For knowledge on basics of Networks
Subject Name : Introduction to Internet of Things
Subject Code : 22EC901 Semester : 5
Reason : For knowledge on sensor nodes
Subject Name : Sensors and Actuator devices
Subject Code : 22EC907 Semester : 5
Reason : For knowledge on sensors
SYLLABUS
22EC903 WIRELESS SENSOR NETWORK L T P C
3 0 0 3
UNIT I OVERVIEW OF WIRELESS SENSOR NETWORKS 9
Challenges for Wireless Sensor Networks- Characteristics requirements-required mechanisms, WSN standards, Difference between mobile ad-hoc and sensor networks, Applications of sensor networks, Enabling Technologies for Wireless Sensor Networks.
UNIT II ARCHITECTURES 9
Single-Node Architecture - Hardware Components, Energy Consumption of Sensor Nodes, Operating Systems and Execution Environments, Network Architecture - Sensor Network Scenarios, Optimization Goals and Figures of Merit, Gateway Concepts. Physical Layer and Transceiver Design Considerations
UNIT III MAC AND ROUTING 9
MAC Protocols for Wireless Sensor Networks, Zigbee, Low Duty Cycle Protocols and Wakeup Concepts - S-MAC, Mediation device Protocol, Wakeup Radio Concepts, Address and Name Management, Assignment of MAC Addresses, Routing Protocols- Energy-Efficient Routing, Geographic Routing
UNIT IV INFRASTRUCTURE ESTABLISHMENT 9
Topology Control, Clustering, Time Synchronization, Localization and Positioning, Sensor Tasking and Control, Case study.
UNIT V DATA MANAGEMENT AND SECURITY 9
Data management in WSN, Storage and indexing in sensor networks, Query processing in sensor, Data aggregation, Directed diffusion, Tiny aggregation, greedy aggregation, Security Protocols, security in WSN
TOTAL: 45 PERIODS
COURSE OUTCOMES
On successful completion of this course, the student will be able to
CO1: Identify core components, standards, and applications of WSNs.
CO2: Explain architectural principles, energy constraints, and protocol operations in WSNs.
CO3: Apply MAC/routing protocols and topology strategies to optimize WSN performance.
CO4: Analyze trade-offs in synchronization, localization, and data aggregation techniques.
CO5: Design energy-efficient and secure WSN solutions for real-world constraints.
CO6: Evaluate infrastructure management and security protocols for resilience and scalability.
COURSE OUTCOMES WITH KNOWLEDGE LEVEL
CO. NO | COURSE OUTCOMES | K LEVEL |
CO1 | Identify core components, standards, and applications of WSNs. | K1 |
CO2 | Explain architectural principles, energy constraints, and protocol operations in WSNs. | K2 |
CO3 | Apply MAC/routing protocols and topology strategies to optimize WSN performance. | K3 |
CO4 | Analyze trade-offs in synchronization, localization, and data aggregation techniques. | K4 |
CO5 | Design energy-efficient and secure WSN solutions for real-world constraints. | K5 |
CO6 | Evaluate infrastructure management and security protocols for resilience and scalability. | K6 |
CO MAPPING MATRIX WITH PO & PSO
1: Slight (Low) 2: Moderate (Medium) 3: Substantial (High)
Cours e Outc o mes | Program Outcomes (POs) | Program Specific Outcomes (PSOs) | |||||||||||||
P O 1 | P O 2 | P O 3 | P O 4 | P O 5 | P O 6 | P O 7 | P O 8 | P O 9 | P O 1 0 | P O 1 1 | P O 1 2 | PS O 1 | PS O 2 | PS O 3 | |
CO1 | 3 | 1 | 1 | 1 | 3 | - | - | - | - | - | 1 | 1 | - | - | 3 |
CO2 | 3 | 1 | 1 | 1 | 3 | - | - | - | - | - | 1 | 1 | - | - | 3 |
CO3 | 3 | 1 | 1 | - | 3 | - | - | - | - | - | 1 | 1 | - | - | 3 |
CO4 | 3 | - | 1 | - | 3 | - | - | - | - | - | 1 | 1 | - | - | 3 |
CO5 | 3 | - | 1 | - | 3 | - | - | - | - | - | 1 | 1 | - | - | 3 |
CO6 | 3 | 1 | 1 | 1 | 3 | - | - | - | - | - | 1 | 1 | - | - | 3 |
S.No | Topic | Number of hours | Proposed Date | Actual Date | Pertaining CO | Taxonomy level | Mode of Delivery |
1 | Data management in WSN | 1 | | | CO5 | K1 Remember | Chalk and Talk |
2 | Storage and indexing in sensor networks | 1 | | | CO5 | K1 Remember | Chalk and Talk |
3 | Query Processing In Wireless Sensor Networks (WSN) | 1 | | | CO5 | K1 Remember | Chalk and Talk |
4 | Data aggregation | 1 | | | CO5 | K1 Remember | Chalk and Talk |
5 | Directed diffusion | 1 | | | CO5 | K2 Understand | Chalk and Talk |
6 | Tiny aggregation | 1 | | | CO5 | K1 Remember | Chalk and Talk |
7 | Greedy aggregation | 1 | | | CO5 | K2 Understand | Chalk and Talk |
8 | Security Protocols | 1 | | | CO6 | K2 Understand | Chalk and Talk |
9 | Security in WSN | 1 | | | CO6 | K2 Understand | Chalk and Talk |
LECTURE PLAN
UNIT 5 – DATA MANAGEMENT AND SECURITY
Total No. of Periods : 9
Reason
6.2 ACTIVITY BASED LEARNING
Activity 1:
Topic Name
Name of the Activity Description
: Security in WSN
: Fishbowl debate
: For example, the person on left takes one position on a topic for debate, the person on right takes the opposite position, and the person in the middle takes notes and decides which side is the most convincing and provides an argument for his or her choice.
Students can get an idea about Security in WSN using this activity.
Activity 2:
Topic Name
Name of the Activity
Description
: Directed diffusion
:Think‐Pair‐Share
: Routing can be done in different protocols in different approaches. Students write a response and then share it with a student nearby.
Students clarify their positions and discuss points of agreement and disagreement. The instructor can use several answers to illustrate important points or facilitate a whole class discussion.
Activity 3:
Topic Name
Name of the Activity Description
: Data aggregation
: Role Play
: How data aggregation is done in WSN can be explained to students through role play
UNIT V DATA MANAGEMENT AND SECURITY
Data management in WSN, Storage and indexing in sensor networks, Query processing in sensor, Data aggregation, Directed diffusion, Tiny aggregation, greedy aggregation, Security Protocols, security in WSN
Data Management Data management deals with data architectures; database management, including querying mechanisms; and data storage and warehousing. In a traditional environment (even in a traditional sensor network environment), data are collected to a centralized server for storage, against which queries are issued. In a more elaborate environment, particularly in support of true-real-time data querying, a mechanism can be deployed to support distributed data storage (possibly extending to clustering nodes) and to support distributed data querying. In particular, one is interested in multiresolution/multitiered data storage and retrieval. The data need to be indexed for efficient temporal and spatial searching; at the same time, one wants to be able easily to generate global values associated with variables or requirements of interest.
Data Acquisition
Data Preprocessing
Data Storage
Data Transmission
Data Aggregation
Data Querying
Data Analysis
Data Security
Applications
Challenges
Future Directions
2. STORAGE AND INDEXING IN SENSOR NETWORKS
In most communication networks, naming of nodes for low-level communication leverages topological information. An example of this is the Internet (point-to-point communication model) where IP addresses are assigned to each node, and these serve as unique node identifiers in IP routing. Such a naming scheme is not very efficient in a sensor network scenario, since the identity of individual sensor nodes is not as important as the data associated with them. Data-centric models for sensor networks allow the sensor data itself (as contrasted to sensor nodes) be named, based on attributes such as event type or geographic location.
In particular, data-centric routing and data-centric storage is energy efficient in sensor networks. Data-Centric Storage (DCS) is a data-dissemination paradigm for sensor networks. In DCS, data is stored, according to event type, at corresponding sensor-net nodes. All data of a certain event type (e.g. temperature measurements) are stored at the same node. A significant benefit of DCS is that queries for data of a certain type can be sent directly to the node storing data of that type, rather than flooding the queries throughout the network (unlike data-centric routing proposals). DCS is based on the low level routing functionality provided by the GPSR (Greedy Perimeter Stateless Routing) geographic routing algorithm, and on distributed hash-table functionality provided by peer-to-peer lookup algorithms. DCS offers reduced total network load and very good network usage.
Replication of control and data information in a DCS framework is the primary mechanism for reducing data retrieval traffic and increasing resilience to node failures. The storage of data of a particular type occurs at one of several replica nodes in the network assigned to this type, and storage of control and summary information pertaining to this type at geographically distributed monitor nodes in the network. By increasing the number of nodes where data can be stored for each event-type, as well as maintaining summary and control information at several nodes, the average cost of storing data and querying data is decreased.
Resilient Data-Centric Storage (R-DCS) is a method of achieving scalability and resilience by replicating data at strategic locations in the sensor network. R-DCS outperforms other schemes in terms of scaling to a large number of nodes and a large number of queries. In the case where nodes in the sensor network are unreliable and experience random failures, R-DCS does not experience a dramatic increase in the number of messages sent as the node failure rate is increased. It also maintains a high query success rate in this scenario. Hence this scheme realizes graceful performance degradation in the presence of node failures.
Wireless sensor networks have certain unique features which must be accounted for in any data dissemination methodology designed for such networks. Sensor devices have significantly higher processing capabilities and storage capabilities than available bandwidth (this is in contrast to wired networks, where an explosion of available bandwidth has led to a drastic reduction in its relative cost). The reason for this difference is that sensors typically have limited battery life.
Hence they must use low-power (and consequently, low-bandwidth) wireless communication techniques to conserve battery power. In a typical scenario, it has been estimated that 3000 instructions could be executed for the same energy cost of sending a bit 100 meters by radio. This framework encourages the use of computational techniques to reduce the total communication overhead in the network.
The gateway through which sensor networks communicate with the external world (e.g. a monitoring terminal or the Internet) is an access point. ‘Access path’ refers to the set of data paths from the sensor nodes to the access point. The access points usually have a higher communication load than other sensor-net nodes. In a high-traffic scenario, such access points can become a bottleneck in the sensor-net (hotspot). The peak amount of traffic flowing through these access points ought to be minimized. The energy constraints of wireless sensor networks are more efficiently achieved by using an attribute-based naming system rather than topological naming schemes (e.g. IP). Such attributes could be predefined to reduce the overhead during communication. For example, all sensor data in an environmental sensing network can be classified as being of types temperature, pressure and humidity. All such data can be named by including these predefined event attributes in the data itself. When an event occurs, the sensors record and store the event data locally, and name this data based on its attributes. The low-level output from sensors (observations) is named, based on the attributes of the associated data. This data can be handled in a number of different ways. The three methods involve substantially different assumptions and cost–benefit trade-offs.
• External Storage (ES) in which all event data is stored at an external storage point for processing;
• Local Storage (LS) in which all event information is stored locally (at the detecting node), and
• Data-Centric Storage (DCS) in which all event data is stored by event-type within the sensornet at designated nodes.
Queries are used to retrieve event information from the sensornet. It isimportant to consider the ratio of query traffic to event-detection traffic while designing a sensor network. Each of the approaches has different relative costs for query and event traffic. Hence, depending on the function of a sensor network, one of the approaches may be more useful than the others.
DCS uses a Distributed Hash-Table (DHT) and offers the following interface:
• The Put(dataName, dataValue) primitive to store the value of the data corresponding to a certain event at the sensor-net node corresponding to the dataName (which serves as the key in the DHT and is typically based on the event type). The name of the data is typically based on the relevant event type.
• The Get(dataName) primitive to retrieve the value of the data stored at the node corresponding to the given dataName. DCS uses the GPSR (Greedy Perimeter Stateless Routing) geographic routing algorithm for low-level routing. It then builds a DHT on top of GPSR. Unlike the commonly used shortest path technique, geographic routing uses the relationship between geographic position and connectivity in a wireless network. Since GPSR needs knowledge about the geographic coordinates of sensornet nodes to route messages, it is assumed that these nodes know their location through the use of localization methodologies. GPSR uses greedy forwarding to forward packets to nodes that are always progressively closer to the destination. The sender includes the approximate coordinates of the final destination while sending the packet. In regions where such a greedy path does not exist, GPSR recovers by forwarding in perimeter mode, wherein a packet traverses successively closer faces of a planar subgraph of the network connectivity graph, until reaching a node closer to the destination, where greedy forwarding resumes. If N is the number of nodes in the network, then to go from one random location to another requires O(√N) packet transmissions. In contrast, a flooding algorithm sends a packet to the entire sensor-net and requires O(N) packet transmissions.
DHT is used to hash the name of a certain event to a key (dataName) which is a location somewhere within the boundaries of the sensor-net. The put(dataName, dataValue) primitive sends a packet with the given payload into the sensor-net which is routed towards the location dataName. The get(dataName) primitive is routed to the node closest to the dataName location, which then transmits a packet to the node originating the query with the corresponding data. In a sensor-net with completely stationary and reliable nodes, this approach is sufficient.
In order to make DCS resilient to node failures and mobility, there are certain extensions to the basic scheme. The storage node for an event type periodically routes a refresh message to all nodes which had transmitted event data to this node. Regular GPSR routing returns these refresh messages to the storage node along the network perimeter. In the intermittent time interval, the nodes in the sensor network could be displaced from their original locations. If a new node is closer to the location of the original storage node than the original storage node itself, then this new node will become the storage node. Timer based algorithms ensure that in case a storage node dies in this fashion, the new storage node automatically starts generating refresh messages. This process is called the Perimeter Refresh Protocol and is used to accomplish replication of (key, value) pairs and their consistent placement at the appropriate home nodes when the network topology changes. To protect against node failure, all nodes that receive a refresh message, will cache the data contained in it (local replication).
The Structured Replication in DCS (SR-DCS) scheme achieves load balancing in the network. SR-DCS uses a hierarchical decomposition of the key space and associates each event-type e with a hierarchy depth d. It hashes each event type to a root location. For a hierarchy depth d, it then computes (4d − 1) images of the root. When an event occurs, it is stored at the closest image node. Queries are routed to all image nodes, starting at the root and continuing through the hierarchy. SR-DCS significantly improves the scalability of DCS, and is useful for frequently detected events. However, that SR-DCS does not involve actual replication of data. It only stores one copy of any event-data at the closest image node. If all nodes in a certain location fail simultaneously (clustered failures), SR-DCS might not be able to recover the data stored at these nodes. The root node is a single point-of-failure in the sense that if the root for event-type e fails, one might not be able to issue any queries for event data corresponding to this type.
The extensions to DCS are used to achieve the following:
• minimize query-retrieval traffic hence saving energy consumption;
• increase data availability, ensuring that event information is not lost even with multiple node failures.
The original version of DCS has all events of the same type stored in one sensor-net node. It is evident that if there are too many events of a particular type, then this storage node will become a bottleneck point (hotspot) in the network. The data-dissemination scheme, Resilient Data-Centric Storage (R-DCS), overcomes these issues by a two-level replication strategy (control and data).
In R-DCS, the coordinate space of the sensornet field is partitioned into Z zones. The set of available zones is denoted as zj : j = 1,... Z. This zoning can be done on the basis of geographical boundaries, as shown in Figure 9.9. The zones can contain sensor nodes operating in three possible modes:
(1) Monitor mode Each zone has one monitor node for each event-type.
The monitor node stores and exchanges information in the form of a monitoring map for each event-type. The monitoring map includes control and summary information in the following fields:
• list of zones containing replica nodes (for forwarding event data and queries);
• list of zones containing monitor nodes (for facilitating map exchange); event summaries (for facilitating summary-mode queries). The exact nature of event summaries depends on the event-type. For example, in a sensornet designed for temperature monitoring, the summary information could contain the number of events detected and the average temperature reading for each zone.
• Bloom filters (for enabling attribute-based queries). Event-data is organized in the form of a set of attributes and their values. In the temperature monitoring case, for example, these attributes could be event-time and temperature. A user might want to access all temperature measurements conducted between 10 a.m. and 11 a.m., or all temperature readings between 50 and 70. Bloom filters offer an efficient way to support attribute-based queries. Note that this field is optional – it is required only to support attribute-based queries.
(2) Replica mode Each zone has at most one replica node for each event type.
The replica node, if present, is always the same as the monitor node. In addition to performing the functions of a monitor node, the replica node actually stores event data for the given event type.
(3) Normal mode All nodes that are not monitor or replica nodes operate in this mode. A normal node may originate or forward (i.e. route) event-data, but is not involved in storing any event data or control information.
3. QUERY PROCESSING IN WIRELESS SENSOR NETWORKS (WSNS)
1. Sensor Nodes and Data Collection
2. Query Processing Architecture
3. Types of Queries
4. Query Execution
5. Challenges
6. Applications
4. DATA AGGREGATION IN WSN
What is Data Aggregation?
Data aggregation refers to the process of collecting and aggregating data from various sensor nodes in the network to reduce the amount of data that needs to be transmitted to the base station. The main goal is to eliminate redundant data transmissions and thus save energy.
Why is Data Aggregation Important?
Energy Efficiency: Sensor nodes are typically battery-powered. Transmitting data consumes a significant amount of energy. By aggregating data, the number of transmissions is reduced, conserving energy.
Bandwidth Utilization: Aggregating data reduces the amount of data being sent through the network, leading to better utilization of the available bandwidth.
Scalability: It helps manage large-scale networks by reducing data traffic, making the network more scalable.
Data Aggregation Techniques
Tree-Based Aggregation: A common technique where sensor nodes are organized into a tree structure. Data is aggregated as it moves from the leaf nodes to the root. This can be efficient, but a single point of failure (the root) can be a downside.
Cluster-Based Aggregation: Nodes are grouped into clusters. Each cluster has a leader (cluster head) that aggregates data from its members and sends it to the base station.
LEACH (Low-Energy Adaptive Clustering Hierarchy) is a popular protocol for this.
Centralized Aggregation: All data is sent to a central node (base station), which performs the aggregation. This can be simple but is not energy-efficient because it requires all nodes to transmit their data.
In Network Aggregation:
Data is aggregated as it travels through the network. Each node aggregates the data it receives before forwarding it. This distributes the load and saves energy.
Aggregation Functions
Average: Calculates the average of the collected data, which is useful for monitoring environmental parameters like temperature and humidity.
Sum: Sums up the collected data. This can be useful in applications like counting the number of events detected by the sensors.
Min/Max: Finds the minimum or maximum value. For instance, it can be used to detect the highest temperature in a region.
Median: Determines the median value of the data set, which can be useful in eliminating outliers.
Count: Counts the number of data points, useful in event detection scenarios.
Protocols Supporting Data Aggregation
LEACH (Low-Energy Adaptive Clustering Hierarchy): Uses cluster-based aggregation. Each node decides independently whether it will become a cluster head.
PEGASIS (Power-Efficient GAthering in Sensor Information Systems): Forms chains of nodes. Each node aggregates data from its neighbor and passes it along the chain until it reaches the base station.
Directed Diffusion: Uses data-centric routing. Nodes aggregate data based on the interest received from the base station, reducing redundant transmissions.
Challenges in Data Aggregation
Data Integrity: Ensuring that data is not altered maliciously during aggregation.
Latency: Aggregation can introduce delays as data needs to be processed at intermediate nodes.
Fault Tolerance: Handling node failures gracefully without losing critical data.
Applications of Data Aggregation
Environmental Monitoring: Aggregating temperature, humidity, and soil moisture data in agriculture to optimize irrigation.
Health Monitoring: Aggregating patient vitals in healthcare to provide a comprehensive health status.
Industrial Monitoring: Aggregating sensor data in factories for predictive maintenance and operational efficiency.
5. DIRECTED DIFFUSION
Directed diffusion is a data-centric routing protocol for information gathering and dissemination in WSNs. The main objective of the protocol is to achieve substantial energy savings in order to extend the lifetime of the network. To achieve this objective, directed diffusion keeps interactions between nodes, in terms of message exchanges, localized within a limited network vicinity. Using localized interaction, direct diffusion can still realize robust multipath delivery and adapt to a minimal subset of network paths. This unique feature of the protocol, combined with the ability of the nodes to aggregate response to queries, results into significant energy savings.
The main elements of direct diffusion include interests, data messages, gradients, and reinforcements. Directed diffusion uses a publish-and-subscribe information model in which an inquirer expresses an interest using attribute–value pairs. An interest can be viewed as a query or an interrogation that specifies what the inquirer wants. shows an example that illustrates how an interest in hummingbirds can be expressed using a set of attribute value pairs. Sensor nodes, which can service the interest, reply with the corresponding data.
For each active sensing task, the data sink periodically broadcasts an interest message to each neighbor. The message propagates throughout the sensor network as an interest for named data. The main purpose of this exploratory interest message is to determine if there exist sensor nodes that can service the sought-after interest.
All sensor nodes maintain an interest cache. Each entry of the interest cache corresponds to a different interest. The cache entry contains several fields, including a timestamp field, multiple gradient fields for each neighbor, and a duration field. The timestamp field contains the timestamp of the last matching interest received. Each gradient field specifies both the data rate and the direction in which data are to be sent. The value of the data rate is derived from the interval attribute of the interest.
The duration field indicates the approximate lifetime of the interest. The value of the duration is derived from the timestamp of the attribute. Figure illustrates interest propagation in a WSN. A gradient can be thought of as a reply link pointing toward the neighboring node from which the interest is received. The diffusion of interests across the entire network, coupled with the establishment of gradients at the network nodes, allows the discovery and establishment of paths between the data sinks that are interested in the named data and the nodes that can serve the data. A sensor node that detects an event searches its interest cache for an entry matching the interest. If a match is identified, the node first computes the highest event rate requested among all its out going gradients. It then sets its sensing subsystem to sample the events at this highest rate. The node then sends out an event description to each neighbor for which it has a gradient. A neighboring node that receives a data searches for a matching interest entry in its cache. If no match is found, the node drops the data message with no further action. If such a match exists, and the data message received does not have a matching data cache entry, the node adds the message to the data cache and sends the data message to the neighboring nodes.
Upon receiving an interest, a node checks its interest cache to determine if an entry exists in its cache for this interest. If such an entry does not exist, the receiving node creates a new cache entry. The node then uses the information contained in the interest to instantiate the parameters of the newly created interest field. Furthermore, the entry is set to contain a single gradient field, with the event rate specified, pointing toward the neighboring node from which the interest is received. If a match exists between the interest received and a cache entry, the node updates the timestamp and duration fields of the matching entry. If the entry contains no gradient for the sender of the interest, the node adds a gradient with the value specified in the interest message. If the matching interest entry contains a gradient for the interest sender, the node simply updates the timestamp and duration fields. A gradient is removed from its interest entry when it expires. Figure 6.13 shows the initial gradient setup.
During the gradient setup phase, a sink establishes multiple paths. The sink can use these paths to higher-quality events by increasing its data rate. This is achieved through a path reinforcement process. The sink may choose to reinforce one or several particular neighbors. To achieve this, the sink resends the original interest message, at a higher data rate, across the paths selected, thereby reinforcing the source nodes on the paths to send data more frequently. The path performing most often can then be retained while negatively reinforcing the remaining paths.
Negative reinforcement can be achieved by timing out all high-data-rate gradients in the network, except for those that are explicitly reinforced. Figure 6.14 shows data delivery along a reinforced path. Link failures caused by environmental factors affecting the communications channel, as well as node failures or performance degradation caused by node energy dissipation or complete depletion, can be repaired in directed diffusion. These failures are typically detected by reduced rate or data loss. When a path between a sensing node and the data sink fails, an alternative path, which is sending at lower rates, can be identified and reinforced. Lossy links can also be negatively reinforced by either sending interests with the exploratory data rate or simply by letting the neighbor’s cache expire over time. Directed diffusion has the potential for significant energy savings. Its localized interactions allow it to achieve relatively high performance over unoptimized paths.
Furthermore, the resulting diffusion mechanisms are stable under a range of network dynamics. Its data-centric approach obliterates the need for node addressing. The directed diffusion paradigm, however, is tightly coupled into a semantically driven query-on-demand data model. This may limit its use to applications that fit such a data model, where the interest-matching process can be achieved efficiently and unambiguously.
6. TINY AGGREGATION
What is Tiny Aggregation?
Tiny Aggregation is a technique designed to perform data aggregation in low-power, distributed wireless sensor networks efficiently1. It allows users to express simple, declarative queries that are distributed and executed across the network2.
Key Features of Tiny Aggregation
How Does Tiny Aggregation Work?
Benefits of Tiny Aggregation
Applications of Tiny Aggregation
Challenges
Aggregation Tree Construction
Fault Tolerance and Redundancy
Load Balancing
Synchronization
Optimization Techniques
Query Optimization
Energy Harvesting
Security Measures
Advanced Aggregation Algorithms
Performance Metrics
Data Fusion
Redundancy Elimination
Spatio-Temporal Data Aggregation
Aggregation Schemes
Adaptive Aggregation
Data Quality and Accuracy
Protocol Examples
Case Studies and Real-World Implementations
Performance Evaluation
Future Directions
7. GREEDY AGGREGATION
Greedy Aggregation is a technique used in wireless sensor networks (WSNs) to improve energy efficiency and reduce data transmission overhead. Here's a detailed explanation:
What is Greedy Aggregation?
Greedy Aggregation involves constructing a tree-like structure where data is aggregated at intermediate nodes as it travels towards the base station. Each node in the network makes a local decision to forward data to the closest node in the tree, minimizing the number of transmissions and conserving energy.
Key Features of Greedy Aggregation
How Does Greedy Aggregation Work?
Benefits of Greedy Aggregation
Challenges and Considerations
Applications
• Environmental Monitoring: Aggregating data from various sensors to monitor environmental conditions like temperature, humidity, and soil moisture.
• Healthcare: Collecting and aggregating patient vitals to provide comprehensive health monitoring.
• Industrial Monitoring: Aggregating sensor data in factories to monitor machinery and predict maintenance needs.
Advanced Techniques
• Trust-Assisted Greedy Aggregation: Incorporating trust mechanisms to ensure data integrity and security during aggregation6.
• Congestion-Aware Aggregation:
Tree Construction in Detail
• Incremental Construction: Greedy Aggregation constructs the tree incrementally. Each node independently decides its parent based on proximity, which is often measured by the number of hops or geographical distance. This local decision-making minimizes the complexity and overhead of tree construction.
Tree Maintenance
• Dynamic Environment Adaptation: In a dynamic environment where nodes can join, leave, or move, the aggregation tree needs to be adaptive. Nodes periodically update their parent selection based on current network conditions, ensuring optimal paths.
• Failure Handling: If a node fails, its children must quickly reselect new parents to maintain the tree structure. Protocols include mechanisms for detecting node failures and initiating parent reselection.
Path Optimization
• Energy-Aware Path Selection: Nodes may choose paths that optimize energy consumption rather than purely based on proximity. This can involve selecting slightly longer paths if they offer better energy efficiency.
• Congestion-Aware Routing: Nodes may also consider network congestion when selecting paths, aiming to balance the load and reduce latency.
In-Network Aggregation Mechanisms
• Hierarchical Aggregation: In hierarchical networks, data is aggregated at multiple levels. For instance, data might first be aggregated within clusters, then at higher levels, reducing the data volume progressively as it moves towards the base station.
• Distributed Processing: Aggregation functions (like average, sum, min, max) are distributed across the nodes. Each node performs partial aggregation on the data it receives before forwarding it.
Advanced Aggregation Functions
• Complex Aggregation: Beyond basic functions, complex aggregation functions can be implemented. For example, data fusion algorithms that combine inputs from multiple sensor types to create richer information.
• Adaptive Algorithms: Aggregation algorithms can adapt to changing network conditions, such as varying data rates or node density, optimizing performance dynamically.
Security and Reliability
• Secure Aggregation: Ensuring data security during aggregation involves encrypting data packets and authenticating nodes to prevent unauthorized access or tampering. Secure aggregation protocols ensure that the integrity and confidentiality of data are maintained even as it is aggregated.
• Anomaly Detection: Nodes may include mechanisms to detect anomalies, such as sudden spikes in readings, which could indicate either environmental changes or malicious tampering.
Real-World Implementations and Protocols
• TAG (Tiny Aggregation): This is a widely cited protocol that allows users to write SQL-like queries, which are then distributed and executed across the sensor network. It exemplifies the principles of greedy aggregation.
• Synopsis Diffusion: This technique combines multi-path routing and in-network aggregation, distributing data aggregation across multiple paths to improve robustness and accuracy.
Performance Metrics
• Energy Efficiency: The primary metric for evaluating greedy aggregation is energy consumption, measured by the lifetime of sensor nodes and overall network longevity.
• Latency: The time taken for data to be aggregated and transmitted to the base station. Lower latency is critical for applications requiring real-time data.
• Data Accuracy: The accuracy of aggregated data compared to raw sensor readings. Efficient aggregation should maintain high data accuracy while reducing data volume.
Case Studies
Future Directions
8. SECURITY PROTOCOLS
Small sensor devices are inexpensive, low-power devices. They have limited computational and communication resources. The sensors form a self-organizing wireless network in a multi-hop routing topology. Typical applications may periodically transmit sensor readings for processing.
The network consists of nodes (small battery-powered devices) that communicate with a more powerful base station, which in turn is connected to an outside network. The energy source on the devices is a small battery. Communication over radio is the most energy-consuming function performed by these devices, so that the communications overhead needs to be minimized.
The limited energy supplies create limits for security, hence security needs to limit consumption of processor power. However, limited power supply limits the lifetime of keys. Base stations differ from nodes in having longer lived energy supplies and having additional communications connections to outside networks. These constraints make it impractical to use secure algorithms designed for powerful workstations. For example, the working memory of a sensor node is insufficient even to hold the variables (of sufficient length to ensure security) that are required in asymmetric cryptographic algorithms, let alone perform operations with them.
Asymmetric digital signatures for authentication are impractical for sensor networks for a number of reasons, such as, long signatures with a high communication overhead of 50–1000 bytes per packet, a very high overhead to create and verify the signature. Also, symmetric solutions for broadcast authentication are impractical: an improved k-time signature scheme requires over 300 bytes per packet. TESLA protocol adapted for sensor networks to become practical for broadcast authentication is called μTESLA.
Adding security to a highly resource-constrained sensor network is feasible. The security building blocks facilitate the implementation of a security solution for a sensor network by using an authenticated routing protocol and a two-party key agreement protocol. The choice of cryptographic primitives and the security protocols in the sensor networks is affected by the severe hardware and energy constraints. A general security infrastructure that is applicable to a variety of sensor networks needs to define the system architecture and the trust requirements.
Generally, the sensor nodes communicate using RF (Radio Frequency), thus broadcast is the fundamental communication primitive. The baseline protocols account for this property, which affects the trust assumptions, and is exploited to minimize energy usage.
The sensor network forms around one or more base stations, which interface the sensor network to the outside network. The sensor nodes establish a routing forest, with a base station at the root of every tree. Periodic transmission of beacons allows nodes to create a routing topology. Each node can forward a message towards a base station, recognize packets addressed to it, and handle message broadcasts. The base station accesses individual nodes using source routing. The base station has capabilities similar to the network nodes, except that it has enough battery power to surpass the lifetime of all sensor nodes, sufficient memory to store cryptographic keys, and means for communicating with outside networks.
In the sensor applications there is limited local exchange and data processing. The communication patterns within the network fall into three categories:
• node to base station communication, e.g. sensor readings;
• base station to node communication, e.g. specific requests;
• base station to all nodes, e.g. routing beacons, queries or reprogramming of the entire network.
The security goal is primarily to address these communication patterns, and to adapt the baseline protocols to other communication patterns, i.e. node to node or node broadcast.
The sensor networks may be deployed in untrusted locations. While it may be possible to guarantee the integrity of each node through dedicated secure microcontrollers, such an architecture may be too restrictive and does not generalize to the majority of sensor networks. Perrig et al.
assume that individual sensors are untrusted. The SPINS key setup prevents compromising of one node spreading to other nodes. Basic wireless communication is not secure. Because it is broadcast, any adversary can eavesdrop on the traffic, and inject new messages or replay and change old messages. Hence, SPINS does not place any trust assumptions on the communication infrastructure, except that messages are delivered to the destination with nonzero probability. Since the base station is the gateway for the nodes to communicate with the outside world, compromising the base station can render the entire sensor network useless. Thus the base stations are a necessary part of the trusted computing base. All sensor nodes trust the base station: at creation time, each node is given a master key which is shared with the base station. All other keys are derived from this key. Each node trusts itself and, in particular, the local clock is trusted to be accurate, i.e. to have a small drift. This is necessary for the authenticated broadcast protocol.
Sensor Network Security Requirements
Message authentication is important for many applications in sensor networks. Within the building sensor network, authentication is necessary for many administrative tasks (e.g. network reprogramming or controlling sensor node duty cycle). At the same time, an adversary can easily inject messages, so the receiver needs to make sure that the data used in any decision-making process originates from the correct source. Informally, data authentication allows a receiver to verify that the data really was sent by the claimed sender.
In the case of two-party communication, data authentication can be achieved through a purely symmetric mechanism: the sender and the receiver share a secret key to compute a Message Authentication Code (MAC) for all communicated data. When a message with a correct MAC arrives, the receiver knows that it must have been originated by the sender. This style of authentication cannot be applied to a broadcast setting without placing much stronger trust assumptions on the network nodes. If one sender wants to send authentic data to mutually untrusted receivers, using a symmetric MAC is insecure. A receiver knows the MAC key, and hence could impersonate the sender and forge messages to other receivers. Hence, an asymmetric mechanism is needed to achieve authenticated broadcast. Authenticated broadcast can also be constructed from symmetric primitives, and asymmetry be introduced with delayed key disclosure and one-way function key chains. In communication, data integrity ensures the receiver that the received data is not altered in transit by an adversary. In SPINS, data integrity is achieved through data authentication, which is a stronger property.
Since all the sensor networks stream some forms of time-varying measurement, and they are guaranteed confidentiality and authentication, we also must ensure that each message is fresh. Informally, data freshness implies that the data is recent, and it ensures that no adversary has replayed old messages.
Two types of freshness are defined: weak freshness, which provides partial message ordering, but carries no delay information, and strong freshness, which provides a total order on a request–response pair, and allows for delay estimation. Weak freshness is required by sensor measurements, while strong freshness is useful for time synchronization within the network.
The following notation is used to describe security protocols and cryptographic operations:
• A, B are principals, such as communicating nodes;
• NA is a nonce generated by A (a nonce is an unpredictable bit string, usually used to achieve freshness);
• M1|M2 denotes the concatenation of messages M1 and M2;
• KAB denotes the secret (symmetric) key which is shared between A and B;
{M}KAB is the encryption of message M with the symmetric key shared by A and B;
• {M}(KAB,IV), denotes the encryption of message M, with key KAB, and the initialization vector IV which is used in encryption modes such as cipher-block chaining (CBC), output feedback mode (OFB), or counter mode (CTR);
• secure channel is a channel that offers confidentiality, data authentication, integrity, and freshness. Security requirements are achieved by using two security building blocks:
SNEP and μTESLA. SNEP provides data confidentiality, two-party data authentication, integrity, and freshness. μTESLA provides authentication for data broadcast. The security for both mechanisms is bootstrapped with a shared secret key between each node and the base station. The trust to node to-node interactions can be extended from the node-to-base-station trust. SNEP has low communication overhead since it only adds bytes per message. SNEP, like many cryptographic protocols, uses a counter, but trans mitting the counter value is avoided by keeping state at both end points. SNEP achieves semantic security, a strong security property that prevents eavesdroppers from inferring the message content from the encrypted message.
The same simple and efficient protocol also gives us data authentication, replay protection, and weak message freshness. Data confidentiality is one of the most basic security primitives and it is used in almost every security protocol. A simple form of confidentiality can be achieved through encryption, but pure encryption is not sufficient. Another important security property is semantic security, which ensures that an eavesdropper has no information about the plain text, even if it sees multiple encryptions of the same plain text. For example, even if an attacker has an encryption of a 0 bit and an encryption of a 1 bit, it will not help it distinguish whether a new encryption is an encryption of 0 or 1. The basic technique for achieving this is randomization: before encrypting the message with a chaining encryption function (i.e. DES-CBC (Data Encryption Standard – Cipher Block Chaining)), the sender precedes the message with a random bit string. This prevents the attacker from inferring the plain text of encrypted messages if it knows plain text-cipher text pairs encrypted with the same key. However, sending the randomized data over the RF channel requires more energy. A cryptographic mechanism achieves semantic security with no additional transmission overhead. A shared counter is used between the sender and the receiver for the block cipher in counter mode (CTR). Since the communicating parties share the counter and increment it after each block, the counter does not need to be sent with the message. To achieve two-party authentication and data integrity, a message authentication code (MAC) is used.
The combination of these mechanisms forms the Sensor Network Encryption Protocol (SNEP). The encrypted data has the following format: E ={D}(Kencr,C), where D is the data, the encryption key is Kencr, and the counter is C. The MAC is M = MAC(Kmac,C|E). The keys Kencr and Kmac are derived
from the master secret key K. The complete message that A sends to B is:
A → B : {D}(Kencr,C), MAC(Kmac,C|{D}(Kencr,C)).
SNEP offers the following properties:
• Semantic security: Since the counter value is incremented after each message, the same message is encrypted differently each time. The counter value is long enough never to repeat within the lifetime of the node.
• Data authentication: If the MAC verifies correctly, a receiver can be assured that the message originated from the claimed sender.
• Replay protection: The counter value in the MAC prevents replaying of old messages. Note that if the counter were not present in the MAC, an adversary could easily replay messages.
• Weak freshness: If the message verified correctly, a receiver knows that the message must have been sent after the previous message it received correctly (that had a lower counter value). This enforces a message ordering and yields weak freshness.
• Low communication overhead: The counter state is kept at each end point and does not need to be sent in each message. (In case the MAC does not match, the receiver can try out a fixed, small number of counter increments to recover from message loss. In case the optimistic resynchronization fails, the two parties engage in a counter exchange protocol, which uses the strong freshness protocol).
Plain SNEP only provides weak data freshness, because it only enforces a sending order on the messages within node B, but no absolute assurance to node A that a message was created by B in response to an event in node A. Node A achieves strong data freshness for a response from node B through a nonce NA (which is a random number sufficiently long such that it is unpredictable). Node A generates NA randomly and sends it along with a request message RA to node B. The simplest way to achieve strong freshness is for B to return the nonce with the response message RB in an authenticated protocol. However, instead of returning the nonce to the sender, the process can be optimized by using the nonce implicitly in the MAC computation.
Authenticated broadcast
Asymmetric digital signatures for authentication are impractical for multiple reasons. They require long signatures with a high communication overhead of 50–1000 bytes per packet, and a very high overhead to create and verify the signature. One-time signature schemes that are based on symmetric
cryptography (one-way functions without trap doors) have a high overhead:
Gennaro and Rohatgi’s broadcast signature based on Lamport’s one-time signature requires over 1 kbyte of authentication information per packet, and Rohatgi’s improved k-time signature scheme requires over 300 bytes per packet. TESLA protocol provides efficient authenticated broadcast. However, TESLA is not designed for such limited computing environments as are encountered in sensor networks. TESLA authenticates the initial packet with a digital signature, which is too expensive to compute on sensor nodes since even fitting the code into the memory is a major challenge. For the same reason, one-time signatures are a challenge for use on sensor nodes. Standard TESLA has an overhead of approximately 24 bytes per packet. For networks connecting workstations this is usually not significant. Sensor nodes, however, send very small messages that are around 30 bytes long. It is simply impractical to disclose the TESLA key for the previous intervals with every packet: with 64-bit keys and MACs, the TESLA-related part of the packet would constitute over 50 % of the packet. The one-way key chain does not fit into the memory of a sensor node, so pure TESLA is not practical for a node to broadcast authenticated data. The μTESLA solves the following inadequacies of TESLA in sensor net-works:
• TESLA authenticates the initial packet with a digital signature, which is too expensive for sensor nodes. μTESLA uses only symmetric mechanisms. • Disclosing a key in each packet requires too much energy for sending and receiving. μTESLA discloses the key once per epoch.
• It is expensive to store a one-way key chain in a sensor node. Μtesla restricts the number of authenticated senders.
The μTESLA is discussed for the case where the base station broad-casts authenticated information to the nodes, and the case where a node is the sender.
μTESLA requires that the base station and nodes are loosely time synchronized, and each node knows an upper bound on the maximum synchronization error. To send an authenticated packet, the base station simply computes a MAC on the packet with a key that is secret at that point in time. When a node gets a packet, it can verify that the corresponding MAC key has not yet been disclosed by the base station (based on its loosely synchronized clock, its maximum synchronization error, and the time schedule at which keys are disclosed). Since a receiving node is assured that the MAC key is known only by the base station, the receiving node is assured that no adversary could have altered the packet in transit. The node stores the packet in a buffer, and at the time of key disclosure, the base station broadcasts the verification key to all receivers. When a node receives the disclosed key, it can easily verify the correctness of the key. If the key is correct, the node can now use it to authenticate the packet stored in its buffer.
Each MAC key is one in a key chain, generated by a public one-way function F. To generate the oneway key chain, the sender chooses the last key Kn of the chain randomly, and repeatedly applies F to compute all other keys: Ki = F(Ki+1). Each node can easily perform time synchronization and retrieve an authenticated key of the key chain for the commitment in a secure and authenticated manner, using the SNEP building block.
Figure shows an example of μTESLA. Each key of the key chain corresponds to a time interval and all packets sent within one time interval are authenticated with the same key. The time until keys of a particular interval are disclosed is two time intervals in this example. The receiver node is assumed to be loosely time synchronized and knows K0 (a commitment to the key chain) in an authenticated way. Packets P1 and P2 sent in interval one, contain a MAC with key K1. Packet P3 has a MAC using key K2. So far, the receiver cannot authenticate any packets yet. Let us assume that packets P4, P5, and P6 are all lost, as well as the packet that discloses key K1, so the receiver can still not authenticate P1, P2, or P3. In interval four, the base station broadcasts key K2, which the node authenticates by verifying K0 = F(F(K2)), and hence knows also K1 = F(K2), so it can authenticate packets P1, P2 with K1, and P3 with K2.
Instead of adding a disclosed key to each data packet, the key disclosure is independent of the packets broadcast, and is tied to time intervals. Within the context of μTESLA, the sender broadcasts the current key periodically in a special packet.
μTESLA has multiple phases: sender set-up, sending authenticated packets, bootstrapping new receivers, and authenticating packets. For simplicity, μTESLA is explained for the case where the base station broadcasts authenticated information, and the case where nodes send authenticated broadcasts.
During the sender set-up, the sender first generates a sequence of secretkeys (or a key chain). To generate a one-way key chain of length, the sender chooses the last key Kn randomly, and generates the remaining values by successively applying a one-way function F [e.g. a cryptographic hash function such as MD5 (Message Digest 5)]: Kj = F(Kj+1). Because F is a one-way function, anybody can compute forward, e.g. compute K0,...,Kj given Kj+1, but nobody can compute backward, e.g. compute Kj+1 given only
K0,...,Kj, due to the one-way generator function. This is similar to the S/Key (Secret Key) one-time password system.
During broadcasting of authenticated packets, the time is divided into intervals and the sender associates each key of the one-way key chain with one time interval. In time interval t, the sender uses the key of the current interval, Kt, to compute the MAC of packets in that interval. The sender will then reveal key Kt after a delay of δ intervals after the end of the time interval t. The key disclosure time delay δ is of the order of a few time intervals, as long as it is greater than any reasonable round-trip time between the sender and the receivers.
During bootstrapping of a new receiver, the important property of the one-way key chain is that once the receiver has an authenticated key of the chain, subsequent keys of the chain are self-authenticating, which means that the receiver can easily and efficiently authenticate subsequent keys of the one-way key chain using the one authenticated key. For example, if a receiver has an authenticated value Ki of the key chain, it can easily authenticate Ki+1, by verifying Ki = F(Ki+1). Therefore, to bootstrap μTESLA, each receiver needs to have one authentic key of the one-way key chain as a commitment to the entire chain. Another requirement of μTESLA is that the sender and receiver are loosely time synchronized, and that the receiver knows the key disclosure schedule of the keys of the one-way key chain. Both the loose time synchronization, as well as the authenticated key chain commitment, can be established with a mechanism that provides strong freshness and point-to-point authentication. A receiver sends a nonce in the request message to the sender. The sender replies with a message containing its current time TS (for time synchronization), a key Ki of the one-way key chain used in a past interval i (the commitment to the key chain), and the starting time Ti of interval i, the duration Tint of a time interval, and the disclosure delay δ (the last three values describe the key disclosure schedule).
Since the confidentiality is not needed, the sender does not need to encrypt the data. The MAC uses the secret key shared by the node and base station to authenticate the data, the nonce NM allows the node to verify freshness. Instead of using a digital signature scheme as in μTESLA, the node-to-base-station authenticated channel is used to bootstrap the authenticated broadcast.
During authenticating of the broadcast packets, when a receiver receives the packets with the MAC, it needs to ensure that the packet could not have been spoofed by an adversary. The threat is that the adversary already knows the disclosed key of a time interval and so it could forge the packet since it knows the key used to compute the MAC. Hence the receiver needs to be sure that the sender has not yet disclosed the key that corresponds to an incoming packet, implying that no adversary could have forged the contents. This is called the security condition, which receivers check for all incoming packets. Therefore, the sender and receivers need to be loosely time synchronized and the receivers need to know the key disclosure schedule. If the incoming packet satisfies the security condition, the receiver stores the packet (it can only verify it once the corresponding key is disclosed). If the security condition is violated (the packet had an unusually long delay), the receiver needs to drop the packet, since an adversary might have altered it.
As soon as the node receives a key Kj of a previous time interval, it authenticates the key by checking that it matches the last authentic key for which it knows Ki, using a small number of applications of the one-way function F : Ki = Fj−I(Kj). If the check is successful, the new key Kj is authentic and the receiver can authenticate all packets that were sent within the time intervals i to j. The receiver also replaces the stored Ki with Kj.
When the nodes broadcast authenticated data, there are additional new problems. Since the node is severely memory limited, it cannot store the keys of a one-way key chain. Moreover, recomputing each key from the initial generating key Kn is computationally expensive. Another issue is that the node might not share a key with each receiver, hence sending out the authenticated commitment to the key chain would involve an expensive node-to-node key agreement. Broadcasting the disclosed keys to all receivers can also be expensive for the node and drain precious battery energy. The two viable approaches for addressing this problem are as follows:
• The node broadcasts the data through the base station. It uses SNEP to send the data in an authenticated way to the base station, which subsequently broadcasts it.
• The node broadcasts the data. However, the base station keeps the one-way key chain and sends keys to the broadcasting node as needed. To conserve energy for the broadcasting node, the base station can also broadcast the disclosed keys, and/or perform the initial bootstrapping procedure for new receivers.
Applications
Secure protocols can be built out of the SPINS secure building blocks with an authenticated routing application, and a two-party key agreement protocol. Using the μTESLA protocol, a lightweight, authenticated, dedicated (ad hoc) routing protocol builds an authenticated routing topology. Ad-hoc routing does not offer authenticated routing messages, hence, it is potentially easy for a malicious user to take over the network by injecting erroneous, replaying old, or advertising incorrect routing information. The authenticated routing scheme assumes bidirectional communication channels, i.e. if node A hears node B, then node B hears node A. The route discovery depends on periodic broadcast of beacons. Every node, upon reception of a beacon packet, checks whether or not it has already received a beacon (which is a normal packet with a globally unique sender ID (identifier) and current time at base station, protected by a MAC to ensure integrity and that the data is authentic) in the current epoch. (Epoch means the interval of a routing updates.) If a node hears the beacon within the epoch, it does not take any further action. Otherwise, the node accepts the sender of the beacon as its parent to route towards the base station. Additionally, the node would repeat the beacon with the sender ID changed to itself. This route discovery resembles a distributed, breadth first search algorithm, and produces a routing topology. However, in the above algorithm, the route discovery depends only on the receipt of a route packet, not on its contents. It is easy for any node to claim to be a valid base station.
The μTESLA key disclosure packets can easily function as routing beacons. Only the sources of authenticated beacons are accepted as valid parents. Reception of a μTESLA packet guarantees that that packet originated at the base station, and that it is fresh. For each time interval, the parent is accepted as the first node sending a packet that is later successfully authenticated. Combining μTESLA key disclosure with the distribution of routing beacons allows us to charge the costs of the to transmission of the keys to network maintenance, rather than to the encryption system.
This scheme leads to a lightweight authenticated routing protocol. Since each node accepts only the first authenticated packet as the one to use in routing, it is impossible for an attacker to re-route arbitrary links within the sensor network. Furthermore, each node can easily verify that the parent forwarded the message: by our assumption of bidirectional connectivity, if the parent of a node forwarded the message, the node must have heard that.
The authenticated routing scheme above is just one way to build authenticated ad-hoc routing protocol using μTESLA. In protocols where base stations are not involved in route construction, μTESLA can still be used for security. In these cases, the initiating node will temporarily act as base station and beacons authenticated route updates. However, the node here will need to have significantly more memory resource than the sensor nodes explored here in order to store the key chain. A convenient method to bootstrap secure connections is public-key cryptography protocols for symmetric-key set-up. Unfortunately, resource- constrained sensor nodes prevent us from using computationally expensive public-key cryptography. Therefore, the protocols are used solely from symmetric-key algorithms. Hence symmetric protocol that uses the base station is applied as a trusted agent for key set-up. Assume that node A wants to establish a shared secret session key SKAB with node B. Since A and B do not share any secrets, they need to use a trusted third party S, which is the base station in our case. In our trust set-up, both A and B share a secret key with the base station, KAS and KBS, respectively.
9. SECURITY IN WSN
Application messages are exchanged through the network, and the mobile code is sent from node to node. Because the security of mobile code greatly affects the security of the network, protection of the messages containing mobile code is an important part of communication security scheme.
The possible threats to a network if communication security is compromised are as follows:
(1) Insertion of malicious code is the most dangerous attack that can occur. Malicious code injected into the network could spread to all nodes, potentially destroying the whole network or, even worse, taking over the network on behalf of an adversary. A seized sensor network can either send false observations about the environment to a legitimate user or send observations about the monitored area to a malicious user.
(2) Interception of the messages containing the physical locations of sensor nodes allows an attacker to locate the nodes and destroy them. The significance of hiding the location information from an attacker lies in the fact that the sensor nodes have small dimensions and their location cannot be trivially traced. Thus, it is important to hide the locations of the nodes. In the case of static nodes, the location information does not age and must be protected through the lifetime of the network.
(3) Besides the locations of sensor nodes, an adversary can observe the application specific content of messages including message IDs, time stamps and other fields. Confidentiality of those fields in our example application is less important than confidentiality of location information, because the application-specific data does not contain sensitive information, and the lifetime of such data is significantly shorter.
(4) An adversary can inject false messages that give incorrect information about the environment to the user. Such messages also consume the scarce energy resources of the nodes. This type of attack is called sleep deprivation torture. and consequently the energy consumption, should relate to the sensitivity of the encrypted information. Following the taxonomy of the types of data in the network, three security levels are defined:
• security level I is reserved for mobile code, the most sensitive information sent through the network;
• security level II is dedicated to the location information conveyed in messages;
•
security level III mechanism is applied to the application specific information.
The strength of the encryption for each of the security levels corresponds to the sensitivity of the encrypted information. Therefore, the encryption applied at level I is stronger than the encryption applied at level II, while the encryption on level II is stronger than the one applied at level III. Different security levels are implemented either by using various algorithms or by using the same algorithm with adjustable parameters that change its strength and corresponding computational overhead. Using one algorithm with adjustable parameters has the advantage of occupying less memory space. RC6 (symmetric block cipher) is suitable for modification of its security strength because it has an adjustable parameter (number of rounds) that directly affects its strength. The overhead for the RC6 encryption algorithm increases with the strength of the encryption measured by the number of rounds.
The multicast model of communication inherent for the sensor network architecture suggests deployment of group keys. Otherwise, if each pair of nodes would require a key or a pair of keys, communication between the nodes would have to be unicast based. This would significantly increase the number of messages. Since the addition of security in a sensor network must not require the change of the whole sensor network architecture, group keys are utilized. All nodes in the network share an initial set of master keys, and the number of keys depends on the estimated lifetime of the network. The longer the lifetime, the more keys are needed in order to expose less material for a known cipher text attack. The alternative approach, where the keys are established dynamically and propagated through the network, is not acceptable. A protocol that guarantees that all nodes received a key is required. Such a requirement is not feasible in a network where the nodes do not keep track of their neighbors. One of the keys from the list of master keys is active at any moment.
The algorithm for the selection of a particular key is based on a pseudo- random generator running at each node with the same seed. Periodically and synchronously on each node, a new random number is generated and used to provide and index an entry in the table of available master keys. This entry contains the active master key. The keys for three levels of security corresponding to the three types of data are then derived from the active master key.
In security level I, the messages containing mobile code are less frequent than messages that the application instances on different nodes exchange. This allows us to use strong encryption in spite of the resulting overhead. For information protected at this security level, nodes use the current master key. The set of master keys, the corresponding pseudo-random number generator, and a seed are credentials that a potential user must have in order to access the network. Once the user obtains those credentials, he/she can insert any code into the network. If a malicious user breaks the encryption on this level using a brute force attack, he/she can insert harmful code into the network.
In security level II (data that contains locations of sensor nodes) a security mechanism is provided that isolates parts of the network, so that breach of security in one part of the network does not affect the rest of the network. According to the assumptions about the applications expected to run in sensor networks, the locations of sensor nodes are likely to be included in the majority of messages. Thus, the overhead that corresponds to the encryption of the location information significantly influences the overall security overhead in the network. This must be taken into account when the strength of the encryption at this level is determined. Since the protection level is lower for the location information than for mobile code, the probability that the key for level II can be broken is higher. Having the key, an adversary could potentially locate all nodes in the network. To constrain the damage to only one part of the network, the following security mechanism is proposed.
Sensor nodes use location-based keys for level II encryption. The location-based keys enable separation between the regions where the location of nodes are compromised and those areas where nodes continue to operate safely. The area covered by a sensor network is divided into cells. Nodes within one cell share a common location-based key, which is a function of a fixed location in the cell and the current master key. Between the cells, there is a bordering region whose width is equal to the transmission range. Nodes belonging to those regions have the keys for all adjacent cells. This ensures that two nodes within a transmission range from each other have a common key. The dimensions of the cells must be big enough for the localized nature of the algorithms in the network to ensure that the traffic among the cells is relatively low, compared with overall traffic. The areas can be of an arbitrary shape with the only requirement that the whole sensor terrain is covered.
A division of the area in uniformly sized cells is the most appropriate solution, because it allows a fast and easy way for a node to determine its cell membership. The network is divided into hexagonal cells, since it ensures that the gateway nodes have at most three keys. Part of the bootstrapping mechanism for sensor nodes is the process of determining their cell membership. In this process, the notion of extended cell is used. An extended cell is a hexagonal cell having the same center as the original cell, and the distance between its sides and the sides of the original cell is equal to the transmission range of the sensor nodes. The extended cell contains the original cell and corresponding bordering regions. Figure 7.2 shows three neighboring cells and their corresponding extended cells. Each node compares its location against each extended cell and determines whether it is in an extended cell or not. If a node is within the extended cell of Cx, it will have the key of Cx,KCx. The nodes within the bordering regions (shaded areas) have multiple keys. For example, the nodes that are adjacent to cells C1 and C2 have two keys: KC1 and KC2, respectively.
In security level III, the application specific data is encrypted using a weaker encryption than the one used for the two other types of data. The weaker encryption requires a lower computational overhead for application specific data. Additionally, the high frequency of messages with application specific data prevents the use of stronger and resource consuming encryption. Therefore, an encryption algorithm demanding fewer computational resources is applied with a corresponding decrease in the strength of security. The key used for the encryption of level III information is derived from the current master key. The MD5 (Message Digest 5) hash function accepts the master key and generates a key for level III. Since the master key is periodically changed, the corresponding key at this level follows those changes.
In the security scheme, the security levels are based on private key cryptography utilizing group keys. Applications and system software access the security API as a part of the middleware defined by the sensor network architecture. Since data contain some confidential information, the content of all messages in the network is encrypted. The sensor nodes in the network are assumed to be allowed to access the content of any message.
The deployment of security mechanisms in a sensor network creates additional overhead. The latency increases due to the execution of the security related procedures, and the consumed energy directly decreases the lifetime of the network. To minimize the security related costs, the security overhead, The major assumptions of security schemes are that the sensor nodes are perfectly time synchronized and have exact knowledge of their location. It is realistic for the nodes to be synchronized up to microseconds.
6.4 ASSIGNMENT
5
Group 1
➡️ Focus: Basic understanding & recall (K1, K2)
Group 2
➡️ Focus: Understanding & comparison (K2, K3)
Group 3
Group 4
Group 5
➡️ Focus: Research & innovation (K5, K6 – Create, Evaluate)
E- Books, Video, PPT & Quiz References
E- Books Reference
Videoreference:
https://www.youtube.com/watch?v=U_cl7QqCF3c
https://www.youtube.com/watch?v=ERDngp3BZYk
Q.No | Questions | K Level | CO |
1. | What is a Wireless Sensor Network (WSN)? A Wireless Sensor Network (WSN) is a network composed of spatially distributed autonomous sensors that monitor physical or environmental conditions, such as temperature, sound, pressure, etc., and cooperatively pass their data through the network to a main location for processing. | K2 | CO5 |
2. | What is data aggregation in WSN? Data aggregation in WSN refers to the process of collecting and summarizing data from multiple sensor nodes to reduce the amount of data transmitted over the network, thereby saving energy and bandwidth. This is crucial for efficient data management in sensor networks | K3 | CO5 |
3. | Explain the concept of directed diffusion. Directed diffusion is a data-centric routing protocol used in WSNs that enables efficient data dissemination by allowing nodes to communicate based on the data they are interested in, rather than their physical location. It promotes data aggregation and reduces redundant transmissions | K2 | CO5 |
4 | What is tiny aggregation? Tiny aggregation is a technique used in WSNs to perform data aggregation at the sensor nodes themselves, minimizing the amount of data sent to the sink node. This method helps in conserving energy and improving the overall efficiency of the network | K2 | CO5 |
5 | Define greedy aggregation. Greedy aggregation is a method where sensor nodes make local decisions to forward data to the nearest node that is closer to the sink. This approach reduces the number of transmissions and conserves energy by minimizing the distance data must travel | K2 | CO5 |
6.5 Part A Q & A
Q.No | Questions | K Level | CO |
6. | What are the main challenges in data management for WSNs? The main challenges in data management for WSNs include energy efficiency, data redundancy, data accuracy, and the ability to handle dynamic network topologies. These challenges necessitate effective data aggregation and query processing techniques | K2 | CO5 |
7. | How does storage work in sensor networks? Storage in sensor networks involves caching data at intermediate nodes to reduce the need for repeated transmissions. This can enhance data retrieval efficiency and minimize energy consumption | K2 | CO5 |
8. | What is query processing in WSN? Query processing in WSN involves interpreting and executing queries sent from the sink node to the sensor nodes. It includes filtering, aggregating, and returning relevant data based on the query parameters | K2 | CO5 |
9. | What role do security protocols play in WSN? Security protocols in WSN are essential for protecting the integrity, confidentiality, and availability of the data being transmitted. They help prevent unauthorized access and ensure that the data collected by sensors is secure from potential attacks | K2 | CO6 |
10. | What is the significance of indexing in sensor networks? Indexing in sensor networks is crucial for efficient data retrieval and query processing. It allows for quick access to relevant data by organizing it in a structured manner, facilitating faster responses to queries | K3 | CO5 |
6.5 Part A Q & A
Q.No | Questions | K Level | CO |
11. | Describe the concept of data-centric routing. Data-centric routing is a method where the routing decisions are based on the content of the data rather than the source or destination addresses. This approach is particularly useful in WSNs for optimizing data transmission and aggregation | K2 | CO5 |
12. | What is the purpose of caching in WSN? Caching in WSN serves to temporarily store data at intermediate nodes, which can reduce the number of transmissions required to retrieve data and improve the overall efficiency of the network | K3 | CO5 |
13. | Explain the term 'sensor data analytics.' Sensor data analytics refers to the techniques and methods used to analyze the data collected from sensor networks. This includes processing, aggregating, and interpreting the data to derive meaningful insights | K2 | CO5 |
14. | What are the benefits of data aggregation? The benefits of data aggregation include reduced energy consumption, decreased bandwidth usage, improved data accuracy, and enhanced network lifetime. By summarizing data, it minimizes the amount of information that needs to be transmitted | K2 | CO5 |
6.5 PART A – QUESTIONS AND ANSWERS
PART A – QUESTIONS AND ANSWERS
S. NO. | QUESTIONS | K LEVEL | CO |
15 | What is the role of a sink node in WSN? The sink node in a WSN acts as the central point for data collection and processing. It receives aggregated data from sensor nodes and may also send queries to the network to retrieve specific information | K2 | CO5 |
16 | How does energy efficiency impact WSN performance? Energy efficiency is critical in WSN performance as sensor nodes are typically battery-powered. Efficient data management techniques, such as aggregation and optimized routing, can significantly extend the network's operational lifetime | K2 | CO5 |
17 | What is a security attack in WSN? A security attack in WSN refers to any malicious activity aimed at compromising the integrity, confidentiality, or availability of the data or the network itself. Common attacks include eavesdropping, data tampering, and denial of service | K2 | CO6 |
18 | How does network topology affect WSN performance?�Network topology influences connectivity, data transmission efficiency, and energy consumption, impacting overall network performance. | K3 | CO5 |
19 | What is a cluster-based approach in WSN?�A cluster-based approach groups sensor nodes into clusters to optimize communication and energy usage, with a cluster head managing data within the group. � | K2 | CO5 |
20 | Define "network lifetime."�Network lifetime refers to the duration for which the WSN remains operational and can effectively collect and transmit data. | K2 | CO5 |
PART A – QUESTIONS AND ANSWERS
S. NO. | QUESTIONS | K LEVEL | CO |
21 | What is the role of middleware in WSN?�Middleware provides an abstraction layer that simplifies application development by managing data storage, processing, and communication. | K3 | CO6 |
22 | Explain the concept of "fault tolerance" in WSN.�Fault tolerance is the ability of a WSN to continue functioning correctly even when some sensor nodes fail or malfunction. | K3 | CO6 |
23 | What is the significance of time synchronization in WSN?�Time synchronization ensures that sensor nodes have a consistent time reference, which is crucial for accurate data collection and event correlation. | K3 | CO6 |
24 | How does mobility affect WSN performance?�Mobility can enhance coverage but may also lead to increased energy consumption and communication overhead as nodes frequently change positions. � | K3 | CO5 |
25 | What are the main applications of WSN?�Applications include environmental monitoring, military surveillance, smart homes, and industrial automation. | K3 | CO5 |
PART – B Q
Q.No | Questions | K Level | CO |
1. | What are the primary challenges in data management within wireless sensor networks? | K2 | CO5 |
2. | How do the characteristics of sensor data influence data management strategies in WSN? | K3 | CO5 |
3. | Discuss the role of data management in enhancing the efficiency of wireless sensor networks. | K2 | CO5 |
4. | What techniques are commonly used for data storage in sensor networks, and how do they differ from traditional database systems? | K2 | CO5 |
5. | Explain the importance of indexing in sensor networks and the impact it has on query performance. | K2 | CO5 |
6. | How does multi-resolution storage contribute to the efficiency of data retrieval in sensor networks? | K2 | CO5 |
7. | What are the key components of a query processing architecture in wireless sensor networks? | K3 | CO5 |
8. | Discuss the impact of network density on query processing and data aggregation in sensor networks. | K3 | CO5 |
9. | How do different query types affect the design of query processing systems in WSN? | K2 | CO5 |
10 | What are the main objectives of data aggregation in wireless sensor networks? | K2 | CO5 |
11 | Compare and contrast directed diffusion and greedy aggregation as methods for data aggregation in WSN | K2 | CO5 |
12 | How does data aggregation contribute to energy efficiency in sensor networks? | K2 | CO5 |
13 | What is directed diffusion, and how does it facilitate data communication in sensor networks? | K2 | CO5 |
14 | Discuss the advantages and limitations of using directed diffusion for data aggregation in WSN. | K2 | CO5 |
15 | How does directed diffusion handle the challenges of data redundancy and energy consumption? | K2 | CO5 |
PART – B Q
Q.No | Questions | K Level | CO |
16 | What is tiny aggregation, and how does it differ from traditional data aggregation techniques? | | CO5 |
17 | Explain the significance of tiny aggregation in resource-constrained environments like sensor networks. | | CO5 |
18 | Describe the greedy aggregation approach and its effectiveness in optimizing data transmission in WSN. | K2 | CO5 |
19 | What are the trade-offs associated with using greedy aggregation in terms of data accuracy and energy consumption? | K3 | CO5 |
20 | How does greedy aggregation compare to other aggregation techniques in terms of scalability and performance? | K2 | CO5 |
21 | What are the primary security challenges faced by wireless sensor networks? | K2 | CO6 |
22 | Discuss the role of security protocols in ensuring data integrity and confidentiality in WSN. | K2 | CO6 |
23 | How do key management schemes contribute to the overall security of wireless sensor networks? | K2 | CO6 |
SUPPORTIVE ONLINE CERTIFICATION COURSES
Nptel Course
Course Name: Wireless Ad Hoc and Sensor Networks
Course Link :https://onlinecourses.nptel.ac.in/noc25_cs74/preview
Udemy Course
Course Name: Wireless Networking fundamentals
Course Link: https://www.udemy.com/course/wireless-networking-fundamentals/
Course Name: General Networking – wireless, routers, firewalls, Cisco NAS
Course Link: https://www.udemy.com/course/general-networking/
REAL TIME APPLICATIONS IN DAY TO DAY LIFE AND TO INDUSTRY
Smart Homes
In smart home environments, WSNs are utilized to monitor and control various systems such as lighting, heating, and security. Sensors collect data on temperature, humidity, and occupancy, allowing for automated adjustments that enhance comfort and energy efficiency. For instance, smart thermostats can analyze real-time data to optimize heating and cooling schedules, significantly reducing energy consumption.
Health Monitoring
Wearable health devices employ WSN technology to continuously monitor vital signs such as heart rate, blood pressure, and glucose levels. These devices transmit data to healthcare providers in real-time, facilitating immediate responses to any anomalies. This application not only improves patient care but also enables proactive health management, reducing hospital visits and associated costs.
Environmental Monitoring
WSNs are extensively used for environmental monitoring, including air quality assessment and weather forecasting. Sensors deployed in various locations collect data on pollutants, temperature, and humidity, which is then processed and analyzed to provide real-time updates. This information is crucial for public health advisories and disaster management, allowing communities to respond swiftly to environmental changes.
REAL TIME APPLICATIONS IN DAY TO DAY LIFE AND TO INDUSTRY
Real-Time Applications in Industry
Industrial Automation
In industrial settings, WSNs play a vital role in automating processes and monitoring equipment. Sensors track machine performance, detect anomalies, and predict maintenance needs, thereby minimizing downtime and enhancing productivity. Real-time data management allows for immediate adjustments to production processes, optimizing resource use and reducing waste.
Supply Chain Management
WSNs are integral to modern supply chain management, where they facilitate real-time tracking of goods and inventory levels. Sensors embedded in shipping containers and warehouses provide continuous updates on the location and condition of products. This capability enhances transparency and efficiency, enabling companies to respond quickly to supply chain disruptions and customer demands.
Smart Agriculture
In agriculture, WSNs are employed for precision farming, where sensors monitor soil moisture, temperature, and crop health. This data is analyzed in real-time to optimize irrigation and fertilization, leading to increased yields and reduced resource consumption. Farmers can make informed decisions based on real-time data, improving sustainability and profitability.
Data Management Techniques in WSN
The effectiveness of real-time applications in WSNs relies on various data management techniques, including storage and indexing, query processing, and data aggregation.
REAL TIME APPLICATIONS IN DAY TO DAY LIFE AND TO INDUSTRY
Storage and Indexing
Efficient storage and indexing methods are crucial for managing the vast amounts of data generated by sensors. Techniques such as multi-resolution storage allow for quick access to relevant data while minimizing storage costs. This is particularly important in applications where timely access to data is critical, such as in emergency response scenarios.
Query Processing
Real-time query processing enables the rapid retrieval and analysis of data from sensor networks. By optimizing query execution plans and utilizing distributed processing, WSNs can handle complex queries efficiently, ensuring that users receive timely information for decision-making.
Data Aggregation
Data aggregation techniques, such as directed diffusion, tiny aggregation, and greedy aggregation, are employed to reduce the volume of data transmitted over the network. These methods combine data from multiple sensors, minimizing redundancy and conserving energy. For instance, directed diffusion allows for efficient data routing based on user interest, while tiny aggregation focuses on processing data at the sensor level to reduce transmission costs.
Security Protocols
Security protocols are essential for protecting the integrity and confidentiality of data in WSNs. Given the sensitive nature of the data collected, robust security measures are necessary to prevent unauthorized access and ensure data authenticity. Key management schemes and encryption techniques are commonly implemented to safeguard data as it is transmitted across the network 8 9.
In conclusion, the integration of real-time data management in wireless sensor networks has transformed various aspects of daily life and industrial operations. By leveraging advanced data management techniques, WSNs enable timely decision-making, enhance efficiency, and improve overall quality of life.
Environmental and Chemical Sensors
The integration of environmental and chemical sensors in WSNs is a significant area of study. These sensors are designed to monitor various environmental parameters, such as air quality, temperature, and humidity, which are essential for applications in environmental monitoring and disaster management. Understanding the deployment and data management strategies for these sensors can lead to improved public health and safety measures.
IP-Based Wireless Sensor Networks
The development of IP-based WSNs is another critical topic. This approach allows for the seamless integration of sensor networks with the Internet, enabling remote monitoring and control. Students should explore the protocols and standards that facilitate this integration, as well as the challenges associated with scalability and security in IP-based systems.
Data Mining and Analysis Techniques
Data mining techniques applied to WSN data can uncover valuable insights and patterns. This includes the use of machine learning algorithms for predictive analytics, which can enhance decision-making processes in various applications, from smart cities to healthcare. Understanding these techniques is vital for optimizing data management and improving the efficiency of WSNs.
Security and Privacy in WSNs
Security protocols are essential for protecting the integrity and confidentiality of data transmitted in WSNs. Topics such as encryption methods, authentication protocols, and intrusion detection systems are critical for safeguarding sensitive information. A comprehensive understanding of these security measures is necessary to address the vulnerabilities inherent in wireless communication.
Energy-Efficient Data Management
Energy efficiency is a paramount concern in WSNs due to the limited power supply of sensor nodes. Advanced data management techniques, such as data aggregation and compression, are essential for minimizing energy consumption while maintaining data integrity. Exploring these techniques can lead to more sustainable and effective WSN deployments.
CONTENT BEYOND THE SYLLABUS
Emerging Applications
The exploration of emerging applications of WSNs, such as in smart agriculture, healthcare, and industrial automation, is crucial. These applications leverage real-time data collection and analysis to enhance operational efficiency and decision-making. Understanding the specific requirements and challenges of these applications can provide insights into future trends in WSN technology.
In summary, the content beyond the syllabus for data management in wireless sensor networks includes advanced topics such as environmental sensors, IP-based networks, data mining techniques, security measures, energy-efficient management, and emerging applications. These areas are essential for a comprehensive understanding of WSNs and their impact on various industries and daily life.
CONTENT BEYOND THE SYLLABUS
ASSESSMENT SCHEDULE
S.NO | TEST | PROPSED DATE | ACTUAL DATE |
1 | Unit Test 1 | | |
2 | First internal Assessment Test | | |
3 | Unit Test 2 | | |
4 | Second Internal Assessment Test | | |
5 | Model Examination | | |
PRESCRIBED TEXT BOOKS & REFERENCE BOOKS
REFERENCE BOOKS
TEXT BOOKS
MINI PROJECT SUGGESSTIONS
WSN based Monitoring of Temperature and Humidity of Soil using Arduino
WSN and GSM Module based Automated Irrigation System
WSN based Wireless SCADA
Eyeball Controlled Automatic Wheelchair
Multi- Sensor Based Security Robot Using Zigbee
Sensor Based Brushless DC Motor Speed Control using Microcontroller
Remote Monitoring System Using XBEE
Design of a Low-Cost Contact- Less Digital Tachometer with Added Wireless Feature
Street Light Glow on Detecting Vehicle Movement using Sensor Accident Prevention Using Eye Blinking and Head Movement
GATE & COMPETITIVE EXAM MCQ QUESTIONS
Q1. (GATE ECE 2016)�The primary objective of data aggregation in WSN is:�a) Improve sensing range�b) Reduce communication overhead and save energy�c) Avoid node mobility issues�d) Increase transmission power�Answer: b) Reduce communication overhead and save energy
Q2. (GATE ECE 2018)�Directed Diffusion protocol in WSN is:�a) Address-centric and proactive�b) Data-centric and query-driven�c) Energy-harvesting based�d) MAC layer protocol�Answer: b) Data-centric and query-driven
Q3. (GATE ECE 2019)�A Sybil attack in a wireless sensor network refers to:�a) A malicious node tunnelling packets�b) A single node assuming multiple identities�c) A compromised sink node dropping data�d) Two nodes replaying each other’s packets�Answer: b) A single node assuming multiple identities
Q4. (GATE ECE 2019)�In a wormhole attack, the attacker:�a) Advertises false routing metrics�b) Creates a low-latency link between distant nodes�c) Floods the network with dummy packets�d) Replaces interest messages with noise�Answer: b) Creates a low-latency link between distant nodes
GATE & COMPETITIVE EXAM MCQ QUESTIONS
Q5. (GATE ECE 2020)�Which of the following is not a typical WSN security goal?�a) Confidentiality�b) Integrity�c) Availability�d) Frequency reuse�Answer: d) Frequency reuse
Q6. (GATE ECE 2020)�Tiny Aggregation (TAG) protocol is mainly used for:�a) In-network query processing�b) Node synchronization�c) Cluster head selection�d) Key distribution�Answer: a) In-network query processing
Q7. (GATE ECE 2017)�Which one of the following best explains query processing in WSN?�a) Disseminating queries and collecting responses efficiently�b) Assigning unique addresses to sensor nodes�c) Synchronizing clock of all nodes�d) Performing error control coding�Answer: a) Disseminating queries and collecting responses efficiently
Q8. (GATE ECE 2018)�The greedy aggregation method in WSN involves:�a) Forwarding raw data directly to sink�b) Local aggregation at intermediate nodes before forwarding�c) Ignoring data packets without timestamps�d) Using TDMA for efficient MAC scheduling�Answer: b) Local aggregation at intermediate nodes before forwarding
GATE & COMPETITIVE EXAM MCQ QUESTIONS
Q9. (GATE ECE 2021)�Security protocols in WSN are most constrained by:�a) Limited spectrum bandwidth�b) Limited power, memory, and computation�c) Poor modulation techniques�d) Use of wired gateways�Answer: b) Limited power, memory, and computation
Q10. (GATE ECE 2022)�In WSN, storage and indexing techniques are mainly required for:�a) Energy harvesting�b) Distributed query resolution�c) Routing table construction�d) Time synchronization�Answer: b) Distributed query resolution
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