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An Energy-Aware QoS Routing Protocolfor

WIRLESS SENSOR NETWORK

Outline :

Wireless Sensor Network .Application of WSN .Routing issues .Concept of energy-aware QoS routing .System Architecture .Queuing model .Cost function .Calculation of end-to-end delay.Algorithm .Implementation of protocol using simulator.Conclusion .References.

WIRELESS SENSOR NETWORK

A wireless sensor network(WSN)consist of spatially distributed autonomous sensors to monitor physical or environmental conditon,such as temperature,sound,vibration,pressure,motion or pollutants and cooperatively pass their data through the network to a main location.

The emerging field of WSN combine sensing ,computation and communications into a single tiny devices.

The most straight forward application of WSN technology is to monitor remote environments. For example the chemical plant can be easily monitored for leaks by hundreds of sensors that automatically form a wireless interconnection network and immediately reports the detection of any chemical leaks

Installer can simply have to place a quarter sized devices at each sensor point . The network could be immediately extended by simply adding more devices with no rework or complex configuration.

WSN have the ability to dynamically adapt to changing environments. Adaptations mechanism can respond to changes in network topology or network can shift between drastically different mode of operations.

WSN architecture include both hardware platform and OS designed specifically to address the need of WSN (TinyOS is a component based OS designed to run in resource constrained wireless devices)

Communication in sensor network is from a multiple source to a single sink,wich is not the case in ad-hoc networks. Moreover there is a major energy resource constraint for sensor nodes.

The concept of WSN is based on simple equation:

sensing+CPU+Radio=Thousands of potential applications

Application of WSN area monitoring.Air pollution monitoring.Forest fires detection.Green house monitoring.Landslide detection.Machine health monitoring.Water/waste water monitoring.Agriculture.Structural monitoring.

Routing challenges and Design issues in WSN

1. Node deployment.2. Energy consumption without losing accuracy.3. Data reporting model.4. Node/link heterogeneity.5. Fault tolerance.6. Scalability.7. Network dynamics.8. Transmission media.9. Data aggregation.10. Quality of service.

Node deployment

it is application dependent and effects the routing performance.

Deployment can be1. Deterministic.2. Randomized.

Energy consumption without losing accuracy:

WSN uses its limited energy supply for both data computation and data communication . Hence energy conserving communication and computation are essential.

In multi hope WSN each node play a dual role as a data sender and a data router.

Multi-functioning of sensor node due to power failure can cause a significant topological changes ,rerouting of packets, and reorganization.

Data reporting model: Data sensing and reporting in WSN is depend on • Application.• Time criticality of data reporting.Data reporting is categorized as• Time driven(continous).• Event driven.• Query driven.• Hybrid.routing protocol is highly influenced by data

reporting model.

Node/link heterogeneity: most of the cases all sensor node assumed to be homogenous

that having capacity.

Fault tolerance:o sensor node may fail due to -lack of power. -physical damage. -environmental interference.o If many node fails MAC and routing protocol must

accommodate new formation of new link and routing.o This may require actively adjusting the transmit power,singnal

rate on existing link to reduce energy consumption , or reporting packets through regions of the network where more energy is available.

o Therefore multi-level redundancy is needed for fault tolerant sensor network.

Scalability:Routing scheme must be able to work with these

huge number of sectors.Routing protocol is scalable enough to respond to

events in the environment.

Network dynamics:mobility of sensor node and BS is sometime

necessary in many conditions.Routing message from moving sensor node is

challenging since route stability become an important issue in adition to energy and bandwidth.

Transmission media:The traditional problems associated with a wireless

channel (e.g., fading, high error rate)may also effect the operation of the sensor network.

Data aggregation:Since node may generate significant redundant data,

similar packets from multiple nodes can be aggregated so that number of transmission can be reduced.

Data aggregation is the combination of data from different source according to certain aggregation function.

This technique has been used to achieve energy efficiency and data transfer optimization in a number of routing protocol.

Quality of service:Bounded latency for data delivery is another

condition for time constrained application .As the energy get depleted ,the network may be

required to reduce the quality of the result in order to reduce the energy dissipation in the nodes and hence lengthen the network lifetime. Hence energy aware routing protocol are required to capture this requirement.

Preliminaries:

Recent advances in WSN have led to many new routing protocols specifically designed for sensor networks.

Almost all of these routing protocols considered energy efficiency as the ultimate objective in oder to maximize the whole network lifetime.

Transmission of video and image data requires both energy and QoS aware routing in order to ensure efficient usage of sensors and effective access to the gathered measurements.

we propose an “Energy-aware QoS Routing Protocol” that finds a least cost ,delay-constrained path for real-time data in term of link cost that capture nodes energy reserve, transmission energy , error rate and other communication parameters.

Sensor network architecture

In the architecture sensor node are grouped into clusters controlled by a single command node. Sensors are only capable of radio-based short-haul communications and are responsible for probing the environment to detect a target/event.

We assume that sensor and gateway nodes are stationary and the gateway node is located within the communication range of all the sensors of its cluster.

Sensor receive commands from and send reading to its gateway node , which process this readings.

Sensor that belong to a particular cluster are only accessible via the gateway of that cluster . Therefore a gateway should be able to route sensor data to other gateways

Here we are only focusing on the QoS routing of data within one particular cluster.

Energy-aware QoS Routing:

Our aim is to find an optimal path to the gateway in terms of energy consumption and error rate while meeting the end-to-end delay requirements.

In this case we have both real time and non-real time traffic co-existing in the network , which makes the problem more complex.

Our approach is based on associating a cost function for each link and used a K least cost path algorithm to find a set of candidate routes . Such routes are checked against the end-to-end constraints and one that provides maximum throughput is picked .

Before explaining the details of proposed algorithm , we introduce the queuing model.

Queuing Model:

The queuing model is specifically designed for the case of coexistence of real time and non-real-time traffic in each sensor node.

We use separate queue for real-time and non-real-time traffic whose packet are labeled accordingly.

Classifier. Scheduler. The bandwidth ratio r is actually an initial value set

by the gateway and represents the associated bandwidth to be dedicated to both real-time and non-real time traffic on a particular outgoing link in case of a congestion.

Since the queuing delay depends on this r-value , we cannot calculate the end-to-end delay for a particular path without knowing the r-value. therefore we should first find a list of candidate least-cost paths and then select one that meets the end-to-end delay requirement.

our approach is based on a two-step strategy incorporating both link-based-cost and end-to-end constraints.

1. We calculate the candidate paths without considering the end-to-end delay . Simply calculate the costs for each particular link and then use an extended version of Dijiksta’s algorithm to find an ascending set of least cost paths.

2. Trying to find an optimal r-value that will also maximize the throughput for non-real time traffic.

Calculation of link cost

Costij = K =C0 × (distij)l+C1 ×f (energyj)+C2/TJ+C3+C4+

+ C6 × f (eij)

distij is the distance between node I and j

f(energyj) is the function for finding current residual energy of node j.

TJ is the expected time under the current consumption rate.

f(eij) is the function for finding the error rate on the link between I and j.

C0 (Communication cost)

C1 (Energy stock)C2 (Energy Consumption Rate)

C3 (Relay enabling cost)

C4 (Sensing-state cost)

C5 (Maximum connection per relay)C6 (Error rate)

Calculation of end to end delay for a path:

In order to find the QoS path for sending real time data to the gateway , end to end delay requirement should be met. Before explaining the computation of the delay , which consist of queuing delay and propagation delay for a particular path pi we introduce the following notation.

• λRT :real time data generation rate for imaging sensors. riµ :Service rate for real-time data on sensor node i. (1-ri)µ :Service rate for non-real time data on sensor node i. Pi :The number of sensing only neighbors of node I on path P qi : The number of relaying-only neighbors of node I on path P λRT(i) :Total real time data rate on sensor node i. TQRT(i) :Total queuing delay on node i. TE :end to end queuing delay for particular path P. TP :End to end propagation delay for a particular path P. Tend-end :Total end to end delay for a particular path P. Trequired :End to end delay requirement for all paths.

Total real time data rate by Pi nodes will be PiλRT and total real time data rate by qi nodes will be

(since every relay-only node produces real-time data by the Rate riµ).

Then total real time data load on a sensor node is:

Hence , total queuing delay on a node is:

The end-to-end queuing delay for a particular path is:

The end to end propagation delay for the path is:

Where c is a constant, which is obtained by dividing a weighting constant by the speed of wireless transmission.Hence, total end to end delay will be:

ALGORITHM:While we generate a formula for calculating

the end to end delay for a particular path , finding the optimal r value for each link as far as the queuing delay is concerned , will be very difficult optimization problem to solve.

The distribution of this r values to each node is not an easy task because the each value should be unicasted to the proper sensor node.

Basically we define each r value to be same on each link so that the optimization problem will be simple and this unique r value can be easily broadcasted to all the sensors by the gateway.

If the we let all r values be same for every link then the formula will be simplified as:

Subject to:

The algorithm calculate the cost of each link based on the cost function defined above .

Then , for each node the least cost path to the gateway is found by running dijikstra’s shortest path algorithm .

Appropriate r value are calculated for paths from sensors to the gateway .

If the value is not between 0 and 1 , extended dijikstra’s algorithm for K-shortest path is run in order to find alternative paths with bigger cost .

If there is no such r value ,the connection request for that node to the gateway is simply rejected .

The algorithm might generate different r value for different paths

Since the r value r stored in a list ,maximum of them is selected to be used for whole network , that r value will satisfy the end-to-end delay requirements for all the paths established from sensors to the gateway .

Implementation of the protocol using simulator:

Simulation design : Design the network by creating all nodes and links

between them for simulation .Network configuration phase : Events are scheduled to start at a certain time.Simulation phase : It maintain the simulation clock and executes events

chronologically .Post simulation process : It include verifying the integrity of the program and

evaluating the performance of the simulated network either by text-based approach or by NAM(Network Animation Trace)packet tracing .

Conclusion:

In this paper protocol finds QoS paths for real time data with certain end-to-end delay requirements .

The selected queuing model for the protocol allows the throughput for the normal data not to diminish by employing a network wide r-value ,which guarantees certain service rate for real time and non-real time data on each link .

References:

M. Younis, M. Youssef and K. Arisha, “Energy- aware Routing in Cluster-Based Sensor Networks,” in the Proceedings of the 10th IEEE/ACM .

“Finding the k shortest loopless paths in a network,” Management Science,17:712-716, 1971.

Kemal Akkaya and Mohamed Younis ,”Energy-aware QoS Routing Protocol for Wireless Sensor Network”.

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