Mobile Ad hoc

&

Sensor Networks

UNIT –IV & V

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Introduction

• Wireless Sensor Networks can be considered as a special case of ad hoc networks with reduced or no mobility

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• WSNs enable reliable monitoring and analysis of unknown and untested environments

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• These networks are “data centric”, i.e., unlike traditional ad hoc networks where data is requested from a specific node, data is requested based on certain attributes such as, “which area has temperature over 35ºC or 95ºF”

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• A typical sensor consists of a transducer to sense a given physical quantity, an embedded processor, small memory and a wireless transceiver to transmit or receive data and an attached battery

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Introduction to WSN

• What is a
• Sensor?
• Sensor Node?
• Sensor Network?

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

A Sensor is a …

Device that measures a physical quantity and converts it into a signal which can be read by an observer or by an instrument.

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Sensor Node (Mote) is a …

• Node that is capable of
• Sensing Information
• Processing (on-board)
• Communicating

to nodes in the network

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Typical SN contains several transducers

Temperature, Pressure, Velocity, Acceleration, Stress and Strain, Fatigue, Tilt, Light Intensity, Sound, Humidity, Gas-Sensors, Biological, Pollution, Nuclear Radiation, Civil Structural Sensors, Blood Pressure, Sugar Level, White Cell Count, ...

• Any one could be selected under the program control at a given time

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Functional Diagram of a Typical Sensor Node

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

The Mica Mote

• The Mica Mote is a comprehensive sensor node developed by University of California at Berkeley and marketed by Crossbow

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• It uses an Atmel Atmega 103 microcontroller running at 4 MHz, with a radio operating at the 916 MHz frequency band with bidirectional communication at 40 kbps when energized with a pair of AA batteries

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• Mica Board is stacked to the processor board via the 51 pin extension connector to provide temperature, photo resistor, barometer, humidity, and thermopile sensors

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• To conserve energy, later designs include an A/D Converter and an 8x8 power switch on the sensor board

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

The Mica Mote

Mica Motes-2

Mica Board

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Sensing and Communication Range

• A Wireless Sensor Network (WSN) consists of a large number of sensor nodes (SNs)
• Adequate density of sensors is required so as to void any unsensed area

r_s

Sensing Area

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Sensing and Communication Range

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• If N SNs are put in an area A, then the SNs density can be given by N/A
• The sensing range of each sensor is r_s
• To cover the whole space, adjacent SNs need to be located at most at a distance of 2r_s from each other
• If the SNs are uniformly distributed with the node density of , the probability that there are m SNs within the space of S is Poisson distributed as

where space for two dimensional spaces

• This gives the probability that the monitored space is not covered by any SN and hence the probability p_cover of the coverage by at least one SN is:

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Sensing and Communication Range

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Sensing and Communication Range

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Sensing and Communication Range

• Transmission between adjacent SNs is feasible if there is at least one SN within the communication range of each SN

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• Not just the sensing coverage, but the communication connectivity is equally important

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• The wireless communication coverage of a sensor must be at least twice the sensing distance

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• Data from a single SN is not adequate to make any useful decision and need to be collected from a set of SNs

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Design Issues: Advantages of WSNs

• Ease of deployment – Can be dropped from a plane or placed in a factory, without any prior organization, thus reducing the installation cost and time, and increasing the flexibility of deployment

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• Extended range – One huge wired sensor (macro-sensor) can be replaced by many smaller wireless sensors for the same cost

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• Fault tolerant – With wireless sensors, failure of one node does not affect the network operation

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• Mobility – Since these wireless sensors are equipped with battery, they can possess limited mobility (e.g., if placed on robots)

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• Disadvantage: The wireless medium has a few inherent limitations such as low bandwidth, error prone transmissions, and potential collisions in channel access, etc.

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Design Issues

Traditional routing protocols defined for MANETs are not well suited for wireless sensor networks due to the following reasons:

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• Wireless sensor networks are “data centric”, where data is requested based on particular criteria such as “which area has temperature 35ºC”

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• In traditional wired and wireless networks, each node is given a unique identification and cannot be effectively used in sensor networks

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• Adjacent nodes may have similar data and rather than sending data separately from each sensor node, it is desirable to aggregate similar data before sending it

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• The requirements of the network change with the application and hence, it is application-specific

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Desirable Features

• Attribute-based addressing: This is typically employed in sensor networks where addresses are composed of a group of attribute-value pairs

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• Location awareness: Since most data collection is based on location, it is desirable that the nodes know their position

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• The sensors should react immediately to drastic changes in their environment

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• Query Handling: Users should be able to request data from the network through some base station (also known as a sink) or through any of the nodes, whichever is closer

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Design Issues : Challenges

• Routing protocol design is heavily influenced by many challenging factors

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• These challenges can be summarized as follows:

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• Ad hoc deployment – Sensor nodes are randomly deployed so that they form connections between the nodes

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• Computational capabilities – Sensor nodes have limited computing power and therefore may run simple versions of routing protocols

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• Energy consumption without losing accuracy – Sensor nodes can use up their limited energy supply carrying out computations and transmitting information

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Design Issues : Challenges

• Scalability – The number of sensor nodes deployed in the sensing area may be in the order of hundreds, thousands, or more and routing scheme must be scalable enough to respond to events

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• Communication range – The bandwidth of the wireless links connecting sensor nodes is often limited, hence constraining inter-sensor communication

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• Fault tolerance – Some sensor nodes may fail or be blocked due to lack of power, physical damage, or environmental interference

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• Connectivity – High node density in sensor networks precludes them from being completely isolated from each other

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Design Issues : Challenges

• Transmission media – Communicating nodes are linked by a wireless medium and traditional problems associated with a wireless channel (e.g., fading, high error rate) also affect the operation

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• QoS – In some applications (e.g., some military applications), the data should be delivered within a certain period of time from the moment it is sensed

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• Control Overhead – When the number of retransmissions in wireless medium increases due to collisions, the latency and energy consumption also increases

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• Security –Besides physical security, both authentication and encryption should be feasible while complex algorithm needs to be avoided

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Energy Consumption

• Minimizing the energy consumption of WSs is critical yet a challenge for the design of WSNs

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• Energy Consumption in WSN involves three different components:

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• Sensing Transducer : Sensing transducer is responsible for capturing the physical parameters of the environment

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• A/D Converter: sensor consumes only 3.1 , in 31 pJ/8-bit sample at 1Volt supply, standby power consumption at 1V supply is 41pW,

the lower bound on energy per sample is roughly E_min= C_totallV_ref ²,

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where C _total is total capacitance of the array, and

V _ref is input voltage

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

• Transmission Energy, transmission energy transmits a k-bit message to distance d can be computed as:
• E_Tx(k,d)=E_Tx-elec(k)+E_Tx-amp(k,d)=E_elec*k+*k*d ² ,

where

• E_Tx-elec is the transmission electronics energy consumption,
• E_Tx-amp is the transmit amplifier energy consumption,
• example values: E_Tx-elec=E_Rx-elec=E_elec=50nJ/bit, =100pJ/bit/m²
• Receiver Energy, E_Rx(k)=E_Rx-elec(k)=E_elec*k

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• Computing/Processing Unit, E_switch=C_totalV_dd²
• In order to conserve energy, we may make some SNs go to sleep mode and need to consider energy consumed in that state

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Energy Consumption : Clustering of SNs

• Clustering of SNs not only allows aggregation of sensed data, but limits data transmission primarily within the cluster

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• The sequence starts with discovery of neighboring SNs by sending periodic Beacon Signals, determining close by SNs with some intermediate SNs, forming clusters and selecting cluster head (CH) for each cluster

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• So, the real question is how to group adjacent SNs, and how many groups should be there that could optimize some performance parameter

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• One approach is to partition the WSN into clusters such that all members of the clusters are directly connected to the CH

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• One such example for randomly deployed SNs

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• SNs in a WSN in a cluster, can transmit directly to the CH without any intermediate SN

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Energy Consumption : Clustering of SNs

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Clustering of Sensors

• Data from SNs belonging to a single cluster can be combined together in an intelligent way (aggregation) using local transmissions

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• This can not only reduce the global data to be transferred and localize most traffic to within each individual cluster

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• A lot of research gone into testing coverage of areas by k-sensors clustering adjacent SNs and defining the size of the cluster so that the cluster heads (CHs) can communicate and get data from their own cluster members

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• If each cluster is covered by more than one subset of SNs all the time, then some of the SNs can be put into sleep mode so as to conserve energy while keeping full coverage

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• The use of a second smaller radio has been suggested for waking up the sleeping sensor, thereby conserving the power of main wireless transmitter

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Clustering of Sensors: Predetermined Grid v/s Random Placement

Regularly placed sensors

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• A simple strategy is to place the sensors in the form of two-dimensional grid as such cross-point and such configuration may be very useful for uniform coverage

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• Such symmetric placement allows best possible regular coverage and easy clustering of the close-by SNs

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• Three such examples of SNs in rectangular, triangular and hexagonal tiles of clusters are shown

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Regularly Placed Sensors

• Clusters of size 5x5, with a SN located at each intersection of lines
• Square, triangle, or hexagonal placement of the SNs also dictates the minimum sensing area that need to be covered by each sensor

Useful for deploying in a controlled environment

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

• For simplicity of calculation, the sensing area covered by rectangular placement is taken rectangular, while sensing are by the two configurations are assumed hexagonal and triangular respectively

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• Radio transmission distance between adjacent SNs need to be such that the sensors can receive data from adjacent sensors using wireless radio

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• Clustering can be done for these configurations and the size of each cluster can be fixed as per application requirements

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• If the sensing and radio transmission ranges are set to the minimum value, then all the SNs need to be active all the time to cover the area and function properly

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• If these ranges are increased, then each sub-region can be covered by more than one sensor node and selected SNs can be allowed to go to sleep mode

Regularly placed sensors

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Triangular Placed Sensors

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Hexagonal Placed Sensors

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Regularly Placed Sensors

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Placement of Sensors and Covered Sensing Area

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Randomly distributed sensors

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• The sensors could also be used in an unknown territory or inaccessible area by deploying them from a low flying airplane or unmanned ground/aerial vehicle

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• SNs have to find themselves who their communicating neighbors are and how many of them are present

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• The adjacency among SNs can be initially determined by sending bacon signals as is done in a typical ad hoc network (MANET)

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• The communication range of associated wireless radio should be such that the SNs could be connected together to form a WSN

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• Distribution of the SNs and their sensing range would also determine if the physical parameter in the complete deployed area can be sensed by at least one SN

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Randomly Distributed Sensors

• The sensing and communication ranges required in a randomly placed sensor are governed by the maximum distance to be covered by any one of the sensors in the given area
• If the N-nodes are uniformly distributed in an area A=LxL, then the node density can be given by
• The probability that there are m nodes within the area S, is Poisson distributed and can be given by :

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• The probability that the monitored area or space is 1-covered, can be expressed as :

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• In many situations, an event need to be sensed by at least k close-by sensors for a cooperative decision (such as relative location using triangulation), then concurrent sensing by k SNs can be given by

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• One way to determine the area to be covered by each SN is to form a Voronoi diagram and one such example is shown in Figure 8.10
• The basic idea is to partition the area in to a set of convex polygons such that all polygons edges are equidistant from neighboring sensors
• A simplistic approach is to let each sensor at least sense the area covered by its surrounding polygon and maximum distance to be covered by a SN in a polygon will govern the required sensing area
• Similarly, minimum wireless transmission range can be determined by the maximum distance between any pair of adjacent sensors

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Randomly Distributed Sensors: Voronoi diagram

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Heterogeneous WSNs

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• With constant sensing and transmission range for all SNs, WSNs are also known as homogeneous WSNs

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• This makes the design simpler and easier to manage

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• In some situations, when a new version of SNs are deployed to cover additional area, or some of the existing SNs are replaced by new ones for extended life or precision, then sensing and/or communication range and/or computing power may also depend on the sensor type or version

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• Use of sensors with different sensing and/or communication and/or computation capabilities leads to a heterogeneous WSN which is helpful for performing additional functionalities or be given much more responsibilities

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Heterogeneous WSNs

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Mobile Sensors

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• The enhancements in the field of robotics are paving the way for industrial robots to be applied to a wider range of tasks

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• However, harnessing their full efficiency also depends on how accurately they understand their environment

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• Thus, as sensor networks are the primary choice for environmental sensing, combining sensor networks with mobile robots is a natural and very promising application

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• Robots could play a major role of high-speed resource carriers in defense and military applications where human time and life is very precious

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• Other applications include fire fighting, autonomous waste disposal

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• Thus, we see that there are a number of future applications where sensors and robots could work together through some form of cooperation

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Mobile Sensors

• Sensors detect events autonomously and the mobile robots could take appropriate actions based on the nature of the event

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• Coordination between the mobile robots is obviously critical in achieving better resource distribution and information retrieval

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• Mobile sensor Networks have been suggested to cover the area not reachable by static sensors

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• Coordination between multiple robots for resource transportation has been explored for quite some time now

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• Transporting various types of resources for different applications like defense, manufacturing process, and so on, has been suggested

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• In these schemes, time taken to detect an event depends entirely on the trail followed by the robots

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• Though the path progressively gets better with the use of an ant-like type of algorithm, the whole process has to be started anew when the position of the event changes

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Mobile Sensors

• In terrains where human ingress is difficult, mobile robots can be used to imitate the human’s chore

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• Typical resource-carrying robots are depicted in below Figure, which depicts a possible means of a robot transferring its resources to another's

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• Once depleted of their resource, they may get themselves refilled from the sink

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• The resource in demand could be water or sand (to extinguish fire), oxygen supply, medicines, bullets, clothes or chemicals to neutralize hazardous wastes, and so on

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• The target region that is in need of these resources is sometimes called an event location

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• Whether it is a sensor or another robot within collision distance, it is considered an obstacle and the robot proceeds in a direction away from it

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Mobile Sensors

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Applications

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• Thousands of sensors over strategic locations are used in a structure such as an automobile or an airplane, so that conditions can be constantly monitored both from the inside and the outside and a real-time warning can be issued whenever a major problem is forthcoming in the monitored entity

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• These wired sensors are large (and expensive) to cover as much area is desirable

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• Each of these need a continuous power supply and communicates their data to the end-user using a wired network

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• The organization of such a network should be pre-planned to find strategic position to place these nodes and then should be installed appropriately

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• The failure of a single node might bring down the whole network or leave that region completely un-monitored

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Applications

• Unattendability and some degree of fault tolerance in these networks are desirable in those applications where the sensors may be embedded in the structure or places in an inhospitable terrain and could be inaccessible for any service

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• Undoubtedly, wireless sensor networks have been conceived with military applications in mind, including battlefield surveillance and tracking of enemy activities

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• However, civil applications considerably outnumber the military ones and are applicable to many practical situations

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• Judging by the interest shown by military, academia, and the media, innumerable applications do exist for sensor networks

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• Examples include weather monitoring, security and tactical surveillance, distributed computing, fault detection and diagnosis in machinery, large bridges and tall structures, detecting ambient conditions such as temperature, movement, sound, light, radiation, vibration, smoke, gases, or the presence of certain biological and chemical objects

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Applications

• Under the civil category, envisioned applications can be classified into environment observation and forecast system, habitat monitoring equipment and human health, large structures and other commercial applications

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Habitat Monitoring

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• A prototype test bed consisting of iPAQs (i.e., a type of handheld device) has been built to evaluate the performance of these target classification and localization methods

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• As expected, energy efficiency is one of the design goals at every level: hardware, local processing (compressing, filtering, etc.), MAC and topology control, data aggregation, data-centric routing and storage

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• Preprocessing is proposed in for habitat monitoring applications, where it is argued that the tiered network in GDI is solely used for communication

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• The proposed 2-tier network architecture consists of micro nodes and macro nodes, wherein the micro nodes perform local filtering and data to significantly reduce the amount of data transmitted to macro nodes

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Applications The Grand Duck Island Monitoring Network

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• Researchers from the University of California at Berkeley (UCB) and Intel Research Laboratory deployed in August 2002 a mote-based tiered sensor network in Great Duck Island (GDI), Maine, aimed at monitoring the behavior of storm petrel

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• The overall system architecture is depicted in Figure 8.13

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• A total of 32 motes have been placed in the area to be sensed grouped into sensor patches to transmit sensed data to a gateway which is responsible for forwarding the information from the sensor patch to a remote base station through a local transit network

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• The base station then provides data logging and replicates the data every 15 minutes to a database in Berkeley over a satellite link

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• Remote users can access the replica database server in Berkeley, while local users make use of a small PDA-size device to perform local interactions such as adjusting the sampling rates, power management parameters, etc.

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

The Grand Duck Island Monitoring Network

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Applications: Remote Ecological Micro-Sensor Network

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• PODS is a research project undertaken at the University of Hawaii that has built a wireless network of environmental sensors to investigate why endangered species of plants will grow in one area but not in neighboring areas

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• They deployed camouflaged sensor nodes, (called PODS), in the Hawaii Volcanoes National Park

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• The PODS consist of a computer, radio transceiver and environmental sensors, sometimes including a high resolution digital camera, relaying sensed data via wireless link back to the Internet

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• Bluetooth and 802.11b are chosen as the MAC layer, while data packets are delivered through the IP

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• In PODS, energy efficiency is identified as one of the design goals and an ad hoc routing protocols called Multi-Path On-demand Routing (MOR) has been developed

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Applications: Remote Ecological Micro-Sensor Network

• Weather data are collected every ten minutes and image data are collected once per hour

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• Users employ the Internet to access the data from a server in University of Hawaii at Manoa

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• The placement strategy for the sensor nodes is then investigated

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• Topologies of 1-dimensional and 2-dimensional regions such as triangle tile, square tile, hexagon tile, ring, star, and linear are discussed

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• The sensor placement strategy evaluation is based on three goals: resilience to single point of failure, the area of interest has to be covered by at lease one sensor, and minimum number of nodes

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• Finally, it is found that the choice of placement depends on d and r

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Environmental Monitoring Application

• Sensors to monitor landfill and the air quality

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• Household solid waste and non-hazardous industrial waste such as construction debris and sewer sludge are being disposed off by using over 6000 landfills in USA and associated organic components undergo biological and chemical reaction such as fermentation, biodegradation and oxidation-reduction

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• This causes harmful gases like methane, carbon dioxide, nitrogen, sulfide compounds and ammonia to be produced and migration of gases in the landfill causes physical reactions which eventually lead to ozone gases, a primary air pollutant and an irritant to our respiratory systems

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• The current method of monitoring landfill employs periodic drilling of collection well, collecting gas samples in airtight bags and analyze off-site, making the process very time consuming

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Environmental Monitoring Application

• The idea is to interface gas sensors with custom-made devices and wireless radio and transmit sensed data for further analysis

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• Deployment of a large number of sensors allows real-time monitoring of gases being emitted by the waste material or from industrial spills

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• Place a large number of sensors throughout the area of interest and appropriate type of sensors can be placed according to the type of pollutant anticipated in a given area

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• A large volume of raw data from sensors, can be collected, processed and efficiently retrieval

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• A generic set up of a WSN, has been covered and various associated issues have been clearly pointed out

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• The scheme can be easily used and adopted for other applications as well

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Environment Observation and Forecasting System

• The Environment Observation and Forecasting System (EOFS) is a distributed system that spans large geographic areas and monitors, models and forecasts physical processes such as environmental pollution, flooding, among others

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• Usually, it consists of three components: sensor stations, a distribution network, and a centralized processing farm

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• Some of the characteristics of EOFS are:
• Centralized processing: The environment model is computationally very intensive and runs on a central server and process data gathered from the sensor network
• High data volume: For example, nautical X-band radar can generate megabytes of data per second
• QoS sensitivity: This defines the utility of the data and there is an engineering trade-off between QoS and energy constraint
• Extensibility
• Autonomous operation

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Drinking Water Quality

• A sensor based monitoring system with emphasis on placement and utilization of in situ sensing technologies and doing spatial-temporal data mining for water-quality monitoring and modeling

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• The main objective is to develop data-mining techniques to water-quality databases and use them for interpreting and using environmental data

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• This also helps in controlling addition of chlorine to the treated water before releasing to the distribution system

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• Detailed implementation of a bio-sensor for incoming wastewater treatment has been discussed

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• A pilot-scale and full scale system has also been described

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Disaster Relief Management and Soil Moisture Monitoring

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• Novel sensor network architecture has been proposed in that could be useful for major disasters including earthquakes, storms, floods, fires and terrorist attacks

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• The SNs are deployed randomly at homes, offices and other places prior to the disaster and data collecting nodes communicate with database server for a given sub area which are in-turn linked to a central database for continuous update

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Soil Moisture Monitoring

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• A soil moisture monitoring scheme using sensors, over a one hectare outdoor area and various performance parameters measured from an actual system

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• A custom made moisture sensor is interfaced with Mica-2 Mote wireless board

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Health Care Monitoring

• Telemonitoring of human physiological data, tracking and monitoring of doctors and patients inside a hospital, drug administrator in hospitals, …

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• An example: Artificial retina developed within the Smart Sensors and Integrated Microsystems (SSIM) project

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• A retina prosthesis chip consisting of one hundred microsensors are built and implanted within the human eye, allowing patients with no vision or limited vision to see at an acceptable level

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• Wireless communication is required to suit the need for feedback control, image identification and validation

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• The communication pattern is deterministic and periodic like a TDMA scheme

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Building, Bridge and Structural Monitoring

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• Projects have explored the use of sensors in monitoring the health of buildings, bridges and highways

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• A Bluetooth based scatternet has been proposed to monitor stress, vibration, temperature, humidity etc. in civil infrastructures

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• Simulation results are given to justify effectiveness of their solution by having a set of rectangular Bluetooth equipped sensor grids to model a portion of bridge span

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• Fiber optic based sensors have been proposed for monitoring crack openings in concrete bridge decks, of strain and corrosion of the reinforcement in concrete structures

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• Corrosion of steel bars is measured by using special super glue and angular strain sensors

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Smart Energy and Home/Office Applications

• Societal-scale sensor networks can greatly improve the efficiency of energy-provision chain, which consists of three components: the energy-generation, distribution, and consumption infrastructure

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• It has been reported that 1% load reduction due to demand response can lead to a 10% reduction in wholesale prices, while a 5% load response can cut the wholesale price in half

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DARPA Efforts towards Wireless Sensor Networks

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• The DARPA has identified networked micro sensors technology as a key application for the future

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• On the battlefield of the future, a networked system of smart, inexpensive and plentiful microsensors, combining multiple sensor types, embedded processors, positioning ability and wireless communication, will pervade the environment and provide commanders and soldiers alike with heightened situation awareness

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

Body Area Network

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• Specialized sensors and transducers are being developed to measure human body characterizing parameters

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• There has been increased interest in the biomedical area and numerous proposals have recently been introduced

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• Micro sensor array is used for artificial retina, glucose level monitoring, organ monitors, cancer detectors and general health monitoring

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• A wearable computing network has been suggested to remotely monitor the progress of a physical therapy done at home and an initial prototype has been developed using electroluminescent strips indicating the range of human body’s motion

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• An indoor/outdoor wearable navigation system has been suggested for blind and visually impaired people through vocal interfaces about surrounding environment and changing the mode from indoor to outdoor and vice-versa using simple vocal command

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

GDI Monitoring System Architecture

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

WSN Architecture

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Sensor Networks Applications …

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Other Applications…

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WSN Vs Manets

• The number of nodes in a sensor network can be several orders of magnitude higher than the nodes in an ad hoc network.
• Sensor nodes are densely deployed.
• Sensor nodes are limited in power, computational capacities and memory.
• Sensor nodes are prone to failures.
• The topology of a sensor network changes frequently.
• Sensor nodes mainly use broadcast, most ad hoc networks are based on p2p.
• Sensor nodes may not have global ID.

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Introduction

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What is a Sensor Network?

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Classifications of WSNs

WSNs can be classified on the basis of their mode of operation or functionality, and the type of target applications

• Proactive Networks – The nodes in this network periodically switch on their sensors and transmitters, sense the environment and transmit the data of interest and they provide a snapshot of the relevant parameters at regular intervals and are well suited for applications requiring periodic data monitoring

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• Reactive Networks – In this scheme, the nodes react immediately to sudden and drastic changes in the value of a sensed attribute and as such, these are well suited for time critical applications

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• Hybrid Networks – This is a combination of both proactive and reactive networks where sensor nodes not only send data periodically, but also respond to sudden changes in attribute values

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Network Architecture

• WSN architecture need to cover a desired area both for sensing coverage and communication connectivity point of view
• Therefore, density of the WSN network is critical for the effective use of the WSN
• There is no well-defined measure of life-time of a WSN and some assume either the failure of a single sensor running out of battery power as life-time of the network
• Perhaps a better definition is if certain percentage of sensors stops working, may define the life-time as the network continues to operate
• The percentage failure may depend on the nature of application and as long as the area is adequately covered by the operating sensors, a WSN may be considered operational

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Network Architecture

• There is an optimal distance between two sensors that would maximize the sensor lifetime
• So, if the density of sensors is high, then some of the sensors can be put into sleep mode to have close to optimal distance between the sensors
• Very little work has been done on protocols that suits well to the needs of WSNs
• With respect to the radio transmission, the main question is how to transmit as energy efficiently as possible, taking into account all related costs (possible retransmissions, overhead, and so on)

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MAC Protocols

• WSNs are designed to operate for long time
• Nodes are in idle state for most time when no sensing occurs
• Measurements have shown that a typical radio consumes the similar level of energy in idle mode as in receiving mode
• Important to operate in low duty cycles

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The Sensor-MAC

• Protocol explores design trade-offs for energy-conservation in the MAC layer
• Reduces the radio energy consumption from the following sources:

collision,

control overhead,

overhearing unnecessary traffic,

and idle listening

• The basic scheme is to put all SNs into a low-duty-cycle mode –listen and sleep periodically
• When SNs are listening, they follow a contention rule similar to the IEEE 802.11 DCF

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Sensor-MAC

• Figure depicts the low-duty-cycle operation of each SN
• The listen interval is divided into two parts for both SYNC and data packets
• There is a contention window for randomized carrier sense time before sending each SYNC or data (RTS or broadcast) packet

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SMACS

• The SMACS is an infrastructure-building protocol that forms a flat topology (as opposed to a cluster hierarchy) for sensor networks
• SMACS is a distributed protocol which enables a collection of SNs to discover their neighbors and establish transmission/reception schedules for communicating with them without the need for any local or global master nodes
• In order to achieve this ease of formation, SMACS combines the neighbor discovery and channel assignment phases

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SMACS (Contd.)

• SMACS assigns a channel to a link immediately after the link’s existence is discovered
• This way, links begin to form concurrently throughout the network
• By the time all nodes hear all their neighbors, they would have formed a connected network
• In a connected network, there exists at least one multi hop path between any two distinct nodes

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Network Topology

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SMACS (Contd.)

• Here, nodes A and D wake up at times T_a and T_d
• After they find each other, they agree to transmit and receive during a pair of fixed time slots
• This transmission/reception pattern is repeated periodically every T_frame
• Nodes B and C, in turn, wake up later at times T_b and T_c, respectively

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Node Discovery Phase in SMACS

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Routing Layer

• Routing in sensor networks is usually multi-hop
• The goal is to send the data from source node(s) to a known destination node
• The destination node or the sink node is known and addressed by means of its location
• A BS may be fixed or mobile, and is capable of connecting the sensor network to an existing infrastructure where the user can have access to the collected data
• The task of finding and maintaining routes in WSNs is nontrivial since energy restrictions and sudden changes in node status (e.g., failure) cause frequent unpredictable topological changes
• Thus, the main objective of routing techniques is to minimize the energy consumption in order to prolong WSN lifetime
• To achieve this objective, routing protocols proposed in the literature employ some well-known routing techniques as well as tactics special to WSNs
• To preserve energy, strategies like data aggregation and in-network processing, clustering, different node role assignment, and data-centric methods are employed

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Routing Layer

• In sensor networks, conservation of energy is considered relatively more important than quality of data sent
• Therefore, energy-aware routing protocols need to satisfy this requirement
• Routing protocols for WSNs have been extensively studied in the last few years
• Routing protocols for WSNs can be broadly classified into flat-based, hierarchical-based, and adaptive, depending on the network structure
• In flat-based routing, all nodes are assigned equal role
• In hierarchical-based routing, however, nodes play different roles and certain nodes, called cluster heads (CHs), are given more responsibility
• In adaptive routing, certain system parameters are controlled in order to adapt to the current network conditions and available energy levels
• Furthermore, these protocols can be classified into multipath-based, query-based, negotiation-based, or location-based routing techniques

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Routing Layer

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Directed Diffusion�

• Directed Diffusion is a data aggregation and dissemination paradigm for sensor networks
• It is a data-centric (DC) and application-aware approach in the sense that all data generated by sensor nodes is named by attribute-value pairs
• Directed Diffusion is very useful for applications requiring dissemination and processing of queries
• The main idea of the DC paradigm is to combine the data coming from different sources en-route (in-network aggregation) by eliminating redundancy, minimizing the number of transmissions; thus saving network energy and prolonging its lifetime
• Unlike traditional end-to-end routing, DC routing finds routes from multiple sources to a single destination (BS) that allows in-network consolidation of redundant data

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Data Centric Routing and Directed Diffusion

• In Directed Diffusion, sensors measure events and create gradients of information in their respective neighborhoods
• The BS requests data by broadcasting interests, which describes a task to be done by the network
• Interest diffuses through the network hop-by-hop, and is broadcast by each node to its neighbors
• As the interest is propagated throughout the network, gradients are setup to draw data satisfying the query towards the requesting node
• Each SN that receives the interest setup a gradient toward the SNs from which it receives the interest
• This process continues until gradients are setup from the sources back to the BS
• The strength of the gradient may be different towards different neighbors, resulting in variable amounts of information flow
• At this point, loops are not checked, but are removed at a later stage

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Data Centric Routing and Directed Diffusion

• When interests fit gradients, paths of information flow are formed from multiple paths, and the best paths are reinforced so as to prevent further flooding according to a local rule
• In order to reduce communication costs, data is aggregated on the way The BS periodically refreshes and re-sends the interest when it starts to receive data from the source(s)
• This retransmission of interests is needed because the medium is inherently unreliable
• Sensor nodes in a directed diffusion-based network are application-aware, which enables diffusion to achieve energy savings by choosing empirically good paths and by caching and processing data in the network
• An application of directed diffusion is to spontaneously propagate an important event to regions of the sensor network
• Such type of information retrieval is well suited for persistent queries where requesting nodes expect data that satisfy a query for a period of time

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Data Centric Routing and Directed Diffusion

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Data Centric Routing and Directed Diffusion

• The performance of data aggregation methods employed by the directed diffusion paradigm is affected by the location of the source nodes in the network, the number of sources, and the network topology
• These models are called the event radius (ER) model, and the random sources (RS) model
• In the ER model, a single point in the network area is defined as the location of an event
• All nodes within a distance S (called the sensing range) of this event that are not sinks, are considered to be data sources
• In the RS model, K nodes that are not sinks are randomly selected to be sources
• Unlike the ER model, in the RS model the sources are not necessarily close to each other

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Network Structure Based

Event Radius (ER) model

Randum source (RS) model

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Sequential Assignment Routing (SAR)

• The routing scheme in SAR depends on three factors: energy resources, QoS on each path, and the priority level of each packet
• To avoid single route failure, a multi-path approach coupled with a localized path restoration scheme is employed
• To create multiple paths from a source node, a tree rooted at the source node to the destination nodes (i.e. , the set of BSs) is constructed
• The paths of the tree are defined by avoiding nodes with low energy or QoS guarantees
• At the end of this process, each sensor node is a part of multi-path tree
• For each SN, two metrics are associated with each path: delay (which is an additive QoS metric); and energy usage for routing on that path
• The energy is measured with respect to how many packets will traverse that path
• SAR calculates a weighted QoS metric as the product of the additive QoS metric and a weight coefficient associated with the priority level of the packet
• The goal of SAR is to minimize the average weighted QoS metric for the network

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Minimum Cost Forwarding Algorithm

• The minimum cost forwarding algorithm (MCFA) exploits the fact that the direction of routing is always known, that is, towards fixed and predetermined external BS
• Therefore, a SN need not have a unique ID nor maintain a routing table
• Instead, each node maintains the least cost estimate from itself to the BS
• Each message forwarded by the SN is broadcast to its neighbors
• When a node receives the message, it checks if it is on the least cost path between the source SN and the BS
• If so, it re-broadcasts the message to its neighbors
• This process repeats until the BS is reached
• In MCFA, each sensor node should know the least cost path estimate from itself to the BS

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Coherent and Non-Coherent Processing

• Data processing is a major component in the operation of any WSN
• In general, sensor nodes cooperate with each other in processing different data flooded throughout the network
• Two examples of data processing techniques are coherent and non-coherent data processing-based routing
• In non-coherent data processing routing, nodes locally process the raw data before being sent to other nodes for further processing
• The nodes that perform further processing are called the aggregators
• In coherent routing, the data is forwarded to aggregators after minimum processing of time stamping and duplicate suppression
• To perform energy-efficient routing, normally coherent processing is selected

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Energy Aware Routing

• This protocol is similar to directed diffusion (discussed earlier) with the difference that it maintains a set of paths instead of maintaining or enforcing one optimal path
• These paths are maintained and chosen by means of a certain probability, which depends on how low the energy can be conserved for each path
• By selecting different routes at different times, the energy of any single route will not deplete so quickly
• The protocol initiates a connection through localized flooding, which is used to discover all routes between source/destination pair and their costs; thus building up the routing tables
• Next, the high-cost paths are discarded and a forwarding table is constructed by choosing neighboring nodes inversely proportional to their cost
• Then, data is sent to the destination using the forwarding table with a probability that is inversely proportional to the node cost
• Finally, to keep the various paths alive, localized flooding is carried out by the destination node

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Hierarchical Routing

• Hierarchical, or cluster-based routing has its roots in wired networks, where the main goals are to achieve scalable and efficient communication
• As such, the concept of hierarchical routing has also been employed in WSN to perform energy-efficient routing
• In a hierarchical architecture, higher energy nodes (usually called cluster heads) can be used to process and send the accumulated information while low energy nodes can be used to sense in the neighborhood of the target and pass on to the CH
• In these cluster-based architectures creation of clusters and appropriate assignment of special tasks to CHs can contribute to overall system scalability, lifetime, and energy efficiency
• As we can see from this figure, each cluster has a CH which collects data from its cluster members, aggregates it and sends it to the BS or an upper level CH

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Hierarchical Routing

or sink

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Cluster Based Routing Protocol (CBRP)

• A simple cluster based routing protocol (CBRP) divides the network nodes into a number of overlapping or disjoint two-hop-diameter clusters in a distributed manner
• The cluster members just send the data to the CH, and the CH is responsible for routing the data to the destination
• The major drawback with CBRP is that it requires a lot of hello messages to form and maintain the clusters, and thus may not be suitable for WSN
• Given that sensor nodes are stationary in most of the applications this is a considerable and unnecessary overhead

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Scalable Coordination

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• In hierarchical clustering method, the cluster formation appears to require considerable amount of energy as periodic advertisements are needed to form the hierarchy
• Also, any changes in the network conditions or sensor energy level result in re-clustering which may be not quite acceptable as some parameters tend to change dynamically

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Low-Energy Adaptive Clustering Hierarchy

Low-Energy Adaptive Clustering Hierarchy (LEACH)

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• LEACH is a hierarchical clustering algorithm for sensor networks, called Low-Energy Adaptive Clustering Hierarchy (LEACH)
• LEACH is a good approximation of a proactive network protocol, with some minor differences which includes a distributed cluster formation algorithm
• LEACH randomly selects a few sensor nodes as CHs and rotates this role amongst the cluster members so as to evenly distribute the energy dissipation across the cluster
• In LEACH, the CH nodes compress data arriving from nodes that belong to the respective cluster, and send an aggregated packet to the BS in order to reduce the amount of information that must be transmitted
• LEACH uses a TDMA and CDMA MAC to reduce intra-cluster and inter-cluster collisions, respectively, while data collection is centralized and is performed periodically

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Power-Efficient Gathering in Sensor Information Systems (PEGASIS)

• The Power-Efficient Gathering in Sensor Information Systems (PEGASIS) is a near optimal chain-based protocol which is an enhancement over LEACH
• In order to prolong network lifetime, nodes employing PEGASIS communicate with their closest neighbors only and they take turns in communicating with the BS
• Whenever a round of nodes communicating with the BS ends, a new round starts
• This decreases the power required to transmit data per round, as energy dissipation is spread uniformly over all nodes and as a result, PEGASIS has two main goals
• First, it aims at increasing the lifetime of each node by using collaborative techniques, as a result, the overall network lifetime is also increased
• Second, it only allows coordination between nodes that are close together, thus reducing the bandwidth consumed for communication
• To locate the closest neighbor SN, SNs use the signal strength to measure the distance to all of its neighboring nodes and then adjust the signal strength so that only one node can be heard

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Small Minimum Energy Communication Network (MECN)

• The minimum energy communication network (MECN) protocol has been designed to compute an energy-efficient subnetwork for a given sensor network
• On top of MECN, a new algorithm called Small MECN (SMECN) has been proposed to construct such a subnetwork
• The subnetwork (i.e. , subgraph G') constructed by SMECN is smaller than the one constructed by MECN if the broadcast region around the broadcasting node is circular for a given power assignment
• The subgraph G' of graph G, which represents the sensor network, minimizes the energy consumption satisfying the following conditions:
• The number of edges in G' is less than in G, while containing all nodes in G
• The energy required to transmit data from a node to all its neighbors in subgraph G' is less than the energy required to transmit to all its neighbors in graph G
• The resulting subnetwork computed by SMECN helps in the task of sending messages on minimum-energy paths

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Threshold-sensitive Energy Efficient (TEEN)

• A Threshold-sensitive Energy Efficient sensor Network (TEEN) protocol has been depicted in Figure
• In this scheme, at every cluster change time, the CH broadcasts the following to its members in addition to the attributes:
• Hard Threshold (HT): This is a threshold value for the sensed attribute
• It is the absolute value of the attribute beyond which, the node sensing this value must switch on its transmitter and report to its CH
• Soft Threshold (ST): This is a small change in the value of the sensed attribute which triggers the node to switch on its transmitter and transmit once the HT has been crossed
• In TEEN, nodes sense their environment continuously, thereby making it appropriate for real time applications

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• Features
• Suited for time critical sensing applications
• Time critical data reaches the user almost instantaneously
• At every cluster change time, the parameters are broadcast afresh and so, the user can change them as required
• Energy consumption can be controlled by changing the threshold values

Parameters

Threshold-sensitive Energy Efficient (TEEN)

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TEEN Reactive Protocol

• Features
• It offers flexibility by allowing the user to set the threshold values for the attributes
• Attributes can be changed every cluster change

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• Drawback
• If threshold H_T not reached then user never gets to know about the network

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• Application
• Time critical environment like intrusion detection, etc.

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APTEEN (Adaptive Periodic Threshold Sensitive Energy Efficient Sensor Network Protocol)

• Combines features of Reactive and Proactive Networks
• Periodicity and threshold values controlled by the user
• Energy Efficient Protocol

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Hybrid Protocol (UC)

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Hybrid Protocol (APTEEN)

• To take advantage of both the networks, it is preferable to have both the features in the system (UC)

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​

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• Functioning
• [Attributes, Thresholds (H_T , S_T), Count Time (T_C)] are broadcast to all cluster members

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The cluster head first broadcasts the following parameters:

• Attributes (A): This is a set of physical parameters which the user is interested in obtaining data about.
• Thresholds: This parameter consists of a hard threshold (HT) and a soft threshold (ST). HT is a value of an attribute beyond which a node can be triggered to transmit data. ST is a small change in the value of an attribute which can trigger a node to transmit.
• Schedule: This is a TDMA schedule similar to the one used in assigning a slot to each node.
• Count Time (CT): It is the maximum time period between two successive reports sent by a node.

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Hybrid Protocol

• Features:
• Combines both proactive and reactive policies
• Offers a lot of flexibility by allowing the user to set the time interval and the threshold values for the attributes
• Energy consumption can be controlled by changing periodic interval as well as the threshold values
• Can emulate either a proactive network or a reactive network, based on the application
• Drawback: Increased complexity

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Modified TDMA for APTEEN

• Time-critical queries and historical queries are answered by the BS
• Based on the assumption that adjacent nodes sense similar data, we can make only one of them handle the query
• This might reduces the accuracy of data for non-critical queries
• This is acceptable since it almost doubles the life of the network

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Routing in Fixed-size Clusters

• Routing in sensor networks can also take advantage of geography-awareness
• One such routing protocol is called Geography Adaptive Fidelity (GAF) where the network is firstly divided into fixed zones
• Within each zone, nodes collaborate with each other to play different roles
• For example, nodes elect one SN to stay awake for a certain period of time while the others sleep
• This particular elected SN is responsible for monitoring and reporting data to the BS on behalf of all nodes within the zone
• Here, each SN is positioned randomly in a two dimensional plane
• When a sensor transmits a packet for a total distance r, the signal is strong enough for other sensors to hear it within the Euclidean distance r from the sensor that originates the packet

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Routing in Fixed-size Clusters

• A fixed cluster of each side a can be selected and is connected if:
• If the signal travels a distance of a = r/( SQRT of 5) in adjacent vertical or horizontal directions, two sensors can communicate directly
• For a diagonal communication to take place, the signal has to span a distance of a = r/(2 SQRT of 2)

r is the distance of packet transmission by each sensor

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Sensor Aggregates Routing

• A sensor aggregate includes those SNs in a network that satisfy a grouping predicate for a collaborative processing task
• The parameters of the predicate depend on the task and its resource requirements
• Here, the formation of appropriate sensor aggregate is considered in terms of resource allocation for communication and sensing
• Sensors in the network are divided into clusters according to their sensed signal strength
• After that, local cluster leaders (CH) are elected by exchanging information between neighboring sensors
• Once a sensor node has exchanged packets with all its one-hop neighbors, if it finds that its signal strength is higher than all its one-hop neighbors, it declares itself as a leader
• This leader-based tracking algorithm assumes a unique leader to know surrounding geographical region for collaboration

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Hierarchical Power-Aware Routing

• A hierarchical power-aware routing scheme divides the network into groups of sensors
• The groups in a geographic proximity, are clustered together as a zone and each zone is treated as an entity
• Routing is performed by allowing each zone to decide how it routes a message hierarchically across other zones
• In this scheme, messages are routed along the path with the maximal fraction of the remaining power after the message is transmitted, and this is called the max-min path
• One of the concerns with the max-min path is that traversal through the SNs with high residual power may be expensive as compared to the path with the minimal power consumption
• Too much power consumption decreases the overall power level of the system, thereby decreasing the lifetime of the network

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Sensor Protocols for Information via Negotiation (SPIN)

• Disseminates all the information of each SN to every other SN in the network
• All SNs in the network are potential BS
• A user is able to query any SN and get the required information immediately
• These protocols make use of the property that SNs in close proximity have similar data and thus transmit only the data that the other SNs do not have
• SPIN assigns a high-level name to appropriately describe their collected data, called meta-data, and perform meta-data negotiations before any data is transmitted
• This ensures that no redundant data is transmitted throughout the network
• The format of the meta-data is application-specific and is not specified in SPIN
• SPIN works in a time-driven manner wherein it distributes the information all over the network, even when a user does not request any data
• The SPIN family of protocols includes two protocols, namely, SPIN-1 and SPIN-2, which incorporate negotiation before transmitting data so as to ensure that only useful information is transferred

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Flat versus Hierarchical

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Comparison of SPIN, LEACH and Directed Diffusion

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Negotiation-based Routing

• Negotiation-based routing protocols use high level data descriptors in order to eliminate redundant data transmissions
• Communication decisions are also made based on the available resources
• The motivation here is that the use of flooding to disseminate data produces implosion and data overlap, leading to scenarios where nodes receive duplicate copies of the same data
• If the same data is transmitted by several sensors, considerable energy is consumed
• The main idea behind negotiation-based routing in WSNs is to suppress duplicate information and prevent redundant data from being sent to the next sensor or the BS
• This is done by conducting a series of negotiation messages before the actual data transmission begins

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Multipath-based Routing

• Network performance, and possibly lifetime, in WSNs can be significantly improved if the routing protocol is able to maintain multiple paths to a destination
• The fault tolerance (resilience) is considerably increased, which is measured by the likelihood that an alternate path exists between a source and a destination when the primary path fails
• Clearly, this can be increased if we maintain multiple paths between the source and the destination at the expense of an increased energy consumption and traffic generation (i.e., overhead), as alternate paths are kept alive by sending periodic messages
• We would also like to note here that multipath routes between a source and a destination can be node-disjoint or not
• Multiple paths between a source and destination are said to be node-disjoint when there is no node overlap amongst them

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Multipath-based Routing

• An extension of the multi-path algorithm is described that contains several important characteristics
• The idea is to reduce the complexity of finding the paths by defining the rectangular region bounded by the responding sensor and the BS as the routing region and defining the paths passing through cross-diagonal sensors as multiple paths
• One such example for a rectangular mesh-based WSN, is shown in Figure 9.15
• This identifies many paths, with different path lengths in terms of number of intermediate SNs in the path and hence, reduce the delay between the responding SN and the BS by the process of data store-and-forward along the selected path
• The path along the diagonal, is shortest in length and if this path is used all the time in responding the persistent query, the energy of the sensors lying on this path, could get depleted at a much faster rate then rest of the network

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Multiple Paths are used to route query responses (resemble sides of an

expanding rhombus)

Q

R

P

Source

S

Multipath-based Routing

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1

Sink

Source

2

3

4

5

6

7

Non Critical

Critical

Load Shedding

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Query-based Routing

• In query-based routing, the destination nodes propagate a query for data (sensing task) from a node throughout the network
• A node having the data matching the query sends it back to the node which requested it
• Usually, these queries are described in natural language or in high-level query languages
• For example, a BS B1 may submit a query to node N1 inquiring: “Are there moving vehicles in battlefield region 1?”
• In query-based routing, all the nodes have tables consisting of the sensing tasks queries that they received, and send back data matching these tasks whenever they receive it
• Directed diffusion (discussed earlier in this chapter) is an example of this type of routing
• Here, the sink node sends out messages of interest to SNs
• As the interest is propagated throughout the WSN, the gradients from the source back to the sink (BS) are set up

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Location-based Routing

• In location-based routing, SNs are addressed by means of their locations
• Here, the distance between neighboring SNs can be estimated on the basis of incoming signal strengths, and relative coordinates of neighboring SNs can be obtained by exchanging such information
• Alternatively, the location of nodes may be available directly through GPS if we consider nodes are equipped with a small low power GPS receiver
• In order to conserve energy, some location-based schemes demand that SNs should go to sleep if there is no activity
• Clearly, the more sleeping SNs in the network the more energy can be saved
• However, the active SNs should be connected, should cover the entire sensing region, and should provide basic routing and broadcasting functionalities

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

High-Level Application Layer Support

• The protocols we have presented so far are also found, albeit in some different form in traditional wired, cellular, or ad hoc networks
• For specific applications, a higher level of abstraction specifically tailored to WSN appears to be useful

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Distributed Query Processing

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• The number of messages generated in distributed query processing is several magnitudes less than in centralized scheme
• There are two approaches for processing sensor queries: warehousing and distributed
• In the warehousing approach, data is extracted in a pre-defined manner and stored in a central database
• In the distributed approach, only relevant data is extracted from the sensor network, when and where it is needed

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

High-Level Application Layer Support

Sensor Databases

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• One can view the wireless sensor network as a comprehensive distributed database and interact with it via database queries
• This approach solves, en passant, the entire problem of service definition and interfaces to WSNs by mandating, for example, SQL queries as the interface
• The problems encountered here are in finding energy-efficiency ways of executing such queries and of defining proper query languages that can express the full richness of WSNs
• The TinyDB project carried out at the University of California at Berkeley is looking at these issues
• A model for sensor database systems known as COUGAR, defines appropriate user and internal representation of queries
• The sensor queries is also considered so that it is easier to aggregate the data and to combine two or more queries

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao

High-Level Application Layer Support

Distributed Algorithms

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• WSNs are not only concerned with merely sensing the environment but also with interacting with the environment
• Once actuators like valves are added to WSNs, the question of distributed algorithms becomes inevitable
• One showcase is the question of distributed consensus, where several actuators have to reach a joint decision (a functionality which is also required for distributed software update, for example)

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In-Network Processing

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• In-network processing, requires data to be modified as it flows through the network
• It has become one of the primary enabling technologies for WSNs as it has the potential to considerably increase the energy efficiency of the network

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Mobile Ad hoc & Sensor Networks by Dr. S.Siva Nageswara Rao