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ENERGY EFFICIENT ROUTING TO ENHANCE NETWORK LIFETIME IN WIRELESS SENSOR NETWORK

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ENERGY EFFICIENT ROUTING TO ENHANCE NETWORK

LIFETIME IN WIRELESS SENSOR NETWORK

T.AMSAVENI(M.E), Jay Shriram Group of Institutions,

Tirupur, jothiamsa@gmail.com

,

Miss.P.MALLIGA M.E., Assistant Professor

Jay Shriram Group of Institutions, Tirupur,

mallikajaycse@gmail.com

Dr.S.RAJALAKSHMI Ph.D.

Associate Professor, Jay Shriram Group of Institutions,

Tirupur,

mrajislm@gmail.com

ABSTRACT

This work attempts to enhance the energy efficiency of the bottleneck zone which leads to overall improvement of the network lifetime by considering an adaptive duty cycled WSN. In this paper, an efficient communication paradigm has been adopted in the bottleneck zone by combining adaptive duty cycle and network coding. Comparative studies carried out to estimate the upper bounds of the network lifetime by considering (i) Adaptive duty cycle, (ii) Network coding and (iii) Combinations of adaptive duty cycle and network coding.By applying the above techniques the overall lifetime of the node will eventually increases. An energy-balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. In the experiments, FAFEBRM is compared with LEACH and EEUC, experimental results show that FAF-EBRM outperforms LEACH and EEUC, which balances the energy consumption, prolongs the function lifetime and guarantees high QoS of WSN.

Keywords:DTN networks, Security and Routing

1. INTRODUCTION

A wireless environment is a collection of nodes organized into a cooperative network. Each node consists of processing capability (one or more microcontrollers, CPUs or DSP chips), may contain multiple types of memory (program, data and flash memories), have a RF transceiver (usually with a single Omni- directional antenna), have a power source (e.g., batteries and solar cells), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion. Systems of 1000s or even 10,000 nodes are anticipated. Such systems can revolutionize the way we live and work.

Currently, WSN are beginning to be deployed at an accelerated pace. It is not unreasonable to expect that in 10-15 years that the world will be covered with WSN with access to them via the Internet. This can be considered as the Internet becoming a physical network. This new technology is exciting with unlimited potential for numerous application areas including

environmental, medical, military, transportation,

entertainment, crisis management, homeland defense, and smart spaces.

The concept of wireless sensor networks is based on a simple equation:

Sensing + CPU + Radio = Thousands of potential applications

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78 applications.

Figure 1: WSN Environment

2. LITERATURE SURVEY

2.1 Routing Protocols

Routing protocols can also be classified based on whether they are reactive or proactive. A Proactive protocol sets up routing paths and states before there is a demand for routing traffic. Paths are maintained even there is no traffic flow at that time. In reactive routing protocol, routing actions are triggered when there is data to be sent and disseminated to other nodes. Here paths are setup on demand when queries are initiated. Routing protocols are also classified based on whether they are destination-initiated (Dst-initiated) or source-initiated (Src-initiated). A source-initiated protocol sets up the routing paths upon the demand of the source node, and starting from the source node. Here source advertises the data when available and initiates the data delivery. A destination initiated protocol, on the other hand, initiates path setup from a destination node.

2.2 Maximum Lifetime Routing in Wireless Sensor Networks

This work was researched and published by J.chang and L.tassiulas .This paper describes a protocol that routes data through a path whose nodes have the largest residual energy. The path is switched whenever a better path is discovered. The primary path will be used until its energy is below the energy of the backup path. By means of this approach, the nodes in the primary path will not deplete their energy resources through continual use of the same route, thus achieving longer lifetime. A disadvantage for applications that require mobility on the nodes, is that the protocol is oriented to solve routing problem in static wireless networks[4].

2.3 Energy Efficient Clustering Scheme (EECS)

EECS is a clustering algorithm in which CH candidates compete for the ability to elevate to CH for a given round. This competition involves candidates broadcasting their residual energy to neighboring candidates. If a given node does not find a node with more residual energy, it becomes a CH. Cluster formation is different than that of LEACH. LEACH forms clusters based on the minimum distance of nodes to their corresponding CH.

2.4 Destination-Sequenced Distance-Vector (DSDV)

Destination-Sequenced Distance Vector (DSDV) is a routing algorithm that focuses on finding the shortest paths. The protocol is based on the bellman-ford algorithm to find the routes with improvements. The latter algorithm is very similar to the well-known Dijkstra's algorithm with the support of negative weights. DSDV falls in the proactive category of routing protocols; hence, every mobile node maintains a table containing all the available destinations, the number of hops to reach each destination, and a sequence number. The sequence number is assigned by the destination node its purpose is to distinguish between old nodes and new ones. In order for the nodes to keep track of moving other nodes, a periodic message containing a routing table is sent by each node to its neighbors.

The same message can also be sent if significant change occurs at the level of the routing table. Therefore, the update of the routing table is both time-driven and event-driven. Further discussion can be done for better performance, such as not sending the whole table (full dump update), but only the modified portions (incremental update).The motivation behind it is to be able to update the rest of the network through one packet. This means that if the update requires more than one packet, a full dump is probably a safer approach in this case.

Advantages

 A DSDV was one of the early algorithms

available. It is quite suitable for creating ad hoc networks with small number of nodes. Since no formal specification of this algorithm is present there is no commercial implementation of this algorithm.

Disadvantages

 DSDV requires a regular update of its routing

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79 amount of bandwidth even when the network is idle.

 Whenever the topology of the network changes, a

new sequence number is necessary before the network re-converges; thus, DSDV is not suitable for highly dynamic networks. (As in all distance-vector protocols, this does not perturb traffic in regions of the network that are not concerned by the topology change.)

2.5 Ad-Hoc On-Demand Distance Vector (AODV)

AODV also called source initiated routing protocol. Messages in network are of two types, routing messages and data messages. Routing messages are further divided into two types, path discovery message and path maintenance message. Path discovery includes RREQ (Route Request) and RREP (Route reply). Path maintenance includes RERR (Route error) and HELLO messages. Its basic principal contains two components:

(i) Route discovery

(ii) Route maintenance.

When a source wants to find a route it broadcast a RREQ (route request message) to all the nodes in the network. Upon receiving of RREQ message node checks whether it is the originator or if such an RREQ is repeated. If it is repeated then it will be dropped otherwise it will be broadcasted to all the neighbor nodes again. Each node maintains a routing table and updates it after receiving a routing message. In processing of RREQ, an intermediate node checks whether if corresponding reverse route exists in the routing table, if not then it creates an entry for the reverse route[1].

Destination sequence number is used for checking the freshness of routing message. If route already exists then it checks entry whether it contains fresh message or not. Larger sequence number means fresh message. If message in the routing table is not fresh then it is replaced with the newer one and it also check hop count if the sequence number is same but hop count is different than message with lesser hop count will be placed in the routing table. Then, it checks whether it contains route to the destination and route is not expired then it sends RREP packet back to the source through reverse route, otherwise it broadcast the route request (RREQ). In AODV, each mobile node would periodically broadcast Hello messages thus; each node knows which nodes are in its neighboring nodes within one hop. If one node has not an error message (RERR) to the nodes that are recorded in

the corresponding precursor list in the routing table. The node receiving an RERR would remove the compromised route from their routing table.

Advantages and Disadvantages

 The main advantage of this protocol is that routes

are established on demand and destination sequence numbers are used to find the latest route to the destination. The connection setup delay is lower.

 One of the disadvantages of this protocol is that intermediate nodes can lead to inconsistent routes if the source sequence number is very old and the intermediate nodes have a higher but not the latest destination sequence number, thereby having stale entries. Also multiple Route Reply packets in response to a single Route Request packet can lead to heavy control overhead.

 SAODV is that the periodic beaconing leads to

unnecessary bandwidth consumption.

3. EXISTING SYSTEM

In a typical WSN, the network traffic converges at the Sink node S. There is a significant amount of data flow near the Sink. The area near the Sink is known as the bottleneck zone. Heavy traffic load imposes on the sensor nodes near the Sink node. The nodes in the bottleneck zone deplete their energy very quickly, referred as energy holeproblem in WSN. Failure of such nodes inside the bottleneck zone leads to wastage of network energy and reduction of network reliability. The bottleneck zone needs special attention for reduction of traffic which improves the network lifetime of the whole WSN. Due to increase in network traffic which also depletes energy around the bottleneck zone, known as energy-hole problem. The focus of the present work is to estimate the upper bounds of network lifetime in WSN, considering (i) adaptive duty cycle, (ii) network coding, and (iii) combinations of the duty cycle and network coding.

4. PROPOSED SYSTEM

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energy-80 balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. Furthermore, a spontaneous reconstruction mechanism for local topology is designed additionally. In the experiments, FAFEBRM is compared with LEACH and EEUC, experimental results show that FAF-EBRM outperforms LEACH and EEUC, which balances the energy consumption, prolongs the function lifetime and guarantees high QoS of WSN. In order to conserve battery power in very dense sensor networks, some sensor nodes may be put into the sleep state while other sensor nodes remain active for the sensing and communication tasks.

5. THE SLEEP SCHEDULING SCHEMES

In our study, the following assumptions are made about the sensor network:

 A sufficient number of sensor nodes are

deployed over a sensing field such that some sensor nodes can go into the sleeping mode without degrading the sensing coverage of the network.

 Static circular cluster associations are assumed

in the sensor network. Each sensor node belongs to the same cluster throughout its lifetime.

 Each sensor can use variable transmission

power (assumed to be a continuous variable here) according to its distance from its cluster head. Consequently, it can use the minimal transmission power that is necessary for communication with its cluster head. The cluster head, however, uses the maximum transmission power, with a range of R, to communicate with all the sensor nodes.

 The distance between each sensor node and the

cluster head is known to these two nodes. The distance can be estimated, e.g., by measuring the strength of signals received from the cluster head. It is not necessary for a node to know other sensors’ distances to the cluster head.

6. PERFORMANCE EVALUATION

In the figure x- axis represents the varying number of nodes and y- axis represents the energy consumption in mille seconds

Figure 2: Energy consumption

The life time of sensors is increased by reducing the energy consumption of sensor nodes using a proposed clustering algorithm. The proposed clustering increases the life of sensor nodes when compared to LEACH protocol by comparing the remaining energy level of nodes.

7. CONCLUSION & FUTURE WORK

In this work an efficient clustering technique using static CMR has been implemented and the results were analyzed. In a wireless sensor network (WSN), the area around the Sink forms a bottleneck zone where the traffic flow is maximum. Thus, the lifetime of the WSN network is dictated by the life time of the bottleneck zone. The lifetime upper bounds have been estimated with (i) duty cycle, (ii) network coding and (iii) combinations of duty cycle and network coding. It has-been observed that there is a reduction in energy consumption in the bottleneck zone with the proposed approach. This in turn will lead to increase in network lifetime.

The future work would be implementation of the proposed clustering technique for very large scale networks, localization of sensor nodes by CMR and implementation of sleep scheduling and mac protocol combine to make ideal nodes tosleepmode with respect to various problem domains.

REFERENCES

[1] RashmiRanjan Rout, Soumya K. Ghosh, “Enhancement of Lifetime using Duty Cycle and Network Coding in Wireless Sensor Networks” IEEE Transactions On the Wireless Communications, Vol. 12, No. 2, February 2013 [2] HeejungByun, Junglok Yu, "Adaptive Duty Cycle

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81 Networks,"IEEE Transactions on Mobile Computing, vol. 12, no. 6,pp. 1214-1224, June 2013, [3] Q. Wang and T. Zhang, “Bottleneck zone analysis in

energy constrained wireless sensor networks,” IEEE Communications Letter, Vol. 13, No. 6, pp. 423– 425, June 2009

[4] Y. Wu, S.M. Das, and R. Chandra, "Routing with a Markovian Metric to Promote Local Mixing,"' MSR Technical Report, Nov 2006

[5] Jennifer Y , Biswanath M , Dipak G , ” Wireless sensor network survey,” Computer Network, Vol.52, No.12, pp.2292–2330, April 2008

[6] S. Lee and S. H. Lee, “Analysis of network lifetime in cluster-based sensor networks,”IEEECommun. Lett., vol. 14, no. 10, pp. 900–902,2010

[7] Shuo - Yen Robert Li , Senior Member, IEEE, Raymond W. Yeung, and NingCai“Linear Network Coding” IEEE Transactions On Information Theory, Vol. 49, No. 2, February 2011 [8] Soobin Lee , Student Member, IEEE, and Hwang

S. Lee, Member, “Analysis of the Network Lifetime in Cluster - Based Sensor Networks ” IEEE Communications Letters,Vol. 14, No. 10, October 2010

[9] Manish Bhardwaj, Timothy Garnett, Anantha P. Chandrakasan,” Upper Bounds on the Lifetime of Sensor Networks “43, no. 5, pp. 51-58, May 2011. [10] Hamid RafieiKarkvandi, Student Member, IEEE,

EfraimPecht, and OrlyYadid- Pecht,Fellow , IEEE , “ Effective Lifetime - Aware Routing in Wireless SensorNetworks” IEEE Sensors Journal, VOL. 11, NO. 12, DECEMBER 2011

[11] Dongsook Kim, Chih-Fan Hsin And Mingyan Liu,” Asymptotic Connectivity Of Low Duty-Cycled Wireless Sensor Networks” VOL 2 Apr. 2009 [12] Shuhui Yang , Member , IEEE , and Jie Wu,

Fellow, IEEE,” Efficient Broadcasting Using Network Coding and Directional Antennas in MANETs”, IEEE Transactions Parallel And Distributed Systems, VOL. 21, NO. 2, FEBRUARY 2010

[13] R. R. Rout , S.K. Ghosh , S. Chakrabarti , “Network Coding-aware Data Aggregation for a Distributed Wireless Sensor Network ” Fourth International Conference on Industrial and Information Systems, ICIIS 2009, 28 - 31 December 2009, Sri Lanka [14] Mohamed Hamdi , NejlaEssaddi , and

NoureddineBoudriga , “ Energy – Efficient Routing In Wireless Sensor Networks Using Probabilistic Strategies” NOV 2012

[15] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, no. 4, pp. 393– 422, 2002

[16] Q. Wang and T. Zhang, “Bottleneck zone analysis in energy-constrained wireless sensor networks,” IEEE Commun. Lett., vol. 13, no. 6, pp. 423–425, June 2009

[17] D. Ganesan , R . Govindan , S . Shenker , and D. Estrin, “Highly-resilient, energy- efficient multipath routing in wireless sensor networks,” ACMSIGMOBILE MobileComputing and Commun. Rev., vol. 5, no. 4, pp. 11–25, 2001 [18] Z .Cheng , M . Perillo , and W. B. Heinzelman,

Figure

Figure 1: WSN Environment  2. LITERATURE SURVEY
Figure 2: Energy consumption

References

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