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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 3, Issue 6, June 2013)

153

Enhanced Leach Protocol to Increase Network Life Time

Sonu Vashist

1

, Jitender Khurana

2

1M.Tech. Student, 2Associate Professor, Shri Baba Mastnath Engineering College, Rohtak, Haryana, India

Abstract—Recently Wireless Sensor Network has become

the active research topics in academics and industries. The important things to be considered in designing of protocols do sensor network are minimizing the energy dissipation and network lifetime. The Wireless Sensor Network consists of distributed devices known as sensor nodes which monitor physical and environmental conditions and communicate with nearby sensor node via radio broadcast. We will focus on primarily on the Extended Leach Protocol to increase the Network lifetime of the cluster based wireless sensor network. We seek optimized energy enhancement to the network in such a way that it optimize the network lifetime while considering the full area coverage and connectivity of all cluster sensor nodes.

KeywordsWireless Network, Life Time, Leach, Cluster,

Energy

I. INTRODUCTION

A wireless sensor network system usually includes sensor nodes, sink node and management node. A large number of sensor nodes are deployed in the monitored area, constituting a network through the way of self-organization. The data monitored by sensor nodes is transmitted along other nodes one by one, that will reach the sink node after a multi-hop routing and finally reach the management node through the wired and (or) wireless Internet. The energy, the storage capacity and communication capability of sensor nodes are very limited [1],[2]. Random distribution of the nodes in the sensing field makes battery recharge or exchange an impossible fact. Due to their energy constraints, wireless sensors usually have a limited transmission range, making multi hop data routing toward the PN more energy efficient than direct transmission (one hop). A primary design goal for wireless sensor networks is to use the energy efficiently. Cluster-based routing algorithm has a better energy utilization rate compared with non-cluster routing algorithm.

The basic idea of clustering routing is to use the information aggregation mechanism in the cluster head to reduce the amount of data transmission, thereby, reduce the energy dissipation in communication.

In the clustering routing algorithms for wireless networks, LEACH (low-energy adaptive clustering hierarchy) is well-known because it is simple and efficient. LEACH divides the whole network into several clusters, and the run time of network is broken into many rounds. In each round, the nodes in a cluster contend to be cluster head according to a predefined criterion. However, since CHs (Cluster Head) consume more energy in aggregating and routing data, it is important to have an energy-efficient mechanism for CHs’ election and rotation. In LEACH protocol, all the sensor nodes have the same probability to be a cluster head, which makes the nodes in the network consume energy in a relatively balanced way so as to prolong network.

Besides, the connectivity of every sensor to a CH has to be ensured at any time. Furthermore, for data to be routed from any CH to the PN (Processing Node), all CHs have to belong to a single connected graph. Hence, for sensors’ states allocation to be optimal, coverage, connectivity of sensors to CHs, and routing has to be taken into account within the same global planning process.

Besides achieving energy efficiency, clustering reduces channel contention and packet collisions, resulting in better network throughput under high load.

Generally, energy conservation is dealt with on four different levels:

1. Efficient scheduling of sensor states to alternate between sleep and active modes;

2. Energy-efficient routing, clustering, and data aggregation;

3. Efficient control of transmission power to ensure an optimal trade-off between energy consumption and connectivity;

4. Data compression (source coding) to reduce the amount of uselessly transmitted data

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 3, Issue 6, June 2013)

[image:2.612.48.299.121.297.2]

154 FIGURE 1: ILLUSTRATION OF DATA FLOW IN A CLUSTERED

NETWORK

II. BACKGROUND

We classify the clustering techniques based on two criteria:

 The parameter(s) used for electing CHs(e.g., remaining energy, degree, mobility, and average distance to neighbors).

 The execution nature of a clustering algorithm (probabilistic or iterative)

LEACH (Low Energy Adaptive Clustering Hierarchy) is a distributed clustering protocol which utilizes randomized rotation of local CHs to evenly distribute energy utilization between the nodes of WSNs. The whole operation of the LEACH protocol is divided into rounds. Each round consists of:

a) Set-up phase (clusters are organized) Cluster Head Selection.

Cluster Formation.

b) Steady state Phase (data transmission)

Set-up phase:

 At the beginning of each round, each node advertises it probability, (depending upon its current energy level) to be the Cluster Head, to all other nodes.

 Nodes (k for each round) with higher probabilities are chosen as the Cluster Heads.

 Cluster Heads broadcasts an advertisement message (ADV) using CSMA MAC protocol.

 Based on the received signal strength, each non-Cluster Head node determines its non-Cluster Head for this round (random selection with obstacle).

 Each non-Cluster Head transmits a join-request message (Join-REQ) back to its chosen Cluster Head using a CSMA MAC protocol.

 Cluster Head node sets up a TDMA schedule for data transmission coordination within the cluster.

Steady- state phase:

 TDMA schedule is used to send data from node to head cluster.

 Head Cluster aggregates the data received from node clusters.

 Communication is via direct-sequence spread spectrum (DSSS) and each cluster uses a unique spreading code to reduce inter-cluster interference.

 Data is sent from the cluster head nodes to the BS using a fixed spreading code and CSMA.

FIGURE 2: LEACH PROTOCOL

The number of nodes that remain alive using LEACH is significantly larger (four to eight times larger) than that using static clustering or minimum transmission energy (MTE) routing. But the main problem with LEACH protocol lies in the random selection of cluster heads. There exists a probability that the cluster heads formed are unbalanced and may remain in one part of the network making some part of the network unreachable.

[image:2.612.326.589.318.473.2]
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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 3, Issue 6, June 2013)

155 However, LEACH-C is not feasible for larger networks because nodes far away from the base station will have problem sending their states to the base station and as the role of cluster heads rotates so every time the far nodes will not reach the base station in quick time increasing the latency and delay.

Further, the clustering protocol known as LEACH-E was proposed by Heinzelman et.al.[5] In this protocol it is proposed to elect the cluster-heads according to the energy left in each node. The drawback of LEACH-E is that it requires the assistance of routing protocol, which should allow each node to know the total energy of network. Stable Election Protocol (SEP) was developed for the two-level heterogeneous networks. SEP performs poorly in multi-level heterogeneous network and when heterogeneity is a result of operation of the sensor network [6].

Distributed Efficient Clustering (DEEC) which is dedicatedly designed for energy heterogeneous scenarios, where nodes are initialized at various energy levels [7]. However neither of them assures the selection of energy-rich cluster heads, or the evenness of cluster head dispersion. Decentralized Energy Efficient clustering Propagation (DEEP) [8] prevents cluster heads from being too close to each other, but ignores cluster head’s energy qualifications. Lindsey et al. proposed Power-Efficient Gathering in Sensor Information Systems (PEGASIS) [9]. PEGASIS makes a communication chain using a Traveling Sales Person heuristic. Each node only communicates with two close neighbors along the communication chain. Only a single designated node gathers data from other nodes and transmits the aggregated data to the sink node.

Tan and Korpeoglu [10] proposed two new algorithms under the name Power Efficient Data Gathering and Aggregation Protocol (PEDAP), which are near optimal minimum spanning tree based wireless routing scheme. The performance of the PEDAP was compared with LEACH and PEGASIS, and showed a slightly better network lifetime than PEGASIS. Manjeshwar et. al[11] developed a protocol called Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network Protocol (APTEEN) that uses an enhanced TDMA schedule to efficiently incorporate query handling. APTEEN provides a combination of proactive and reactive policies. In the Hierarchy-Based Anycast Routing[12](HAR) Protocol for WSNs, the sink constructs a hierarchical tree by sending packets to discover each node’s own child nodes in turn. The drawback of HAR is that it sends and receives too many packets in the network, expending much energy.

Therefore a novel Hierarchy-Based Multipath Routing Protocol (HMRP) is proposed by Wang Al[13] to overcome the defects of HAR Yang et al. proposed a new routing scheme; called Shortest Hop Routing Tree [14] (SHORT),to achieve higher energy efficiency, network lifetime, and more throughput than PEGASIS, and PEDAP-PA protocols. This scheme used the centralized algorithms and required the powerful base station.

Hybrid Energy-Efficient Distributed algorithm [15] (HEED) is capable of setting up a cluster head in the center of a dense area irrespective of network topologies. The probability for each node to become a tentative cluster head depends on its residual energy, and all the tentative heads in which are competing for becoming the final cluster heads. Time-driven clustering schemes have been recently developed. Clustering Algorithm via Waiting Timer [16] (CAWT) still relies on the local messages exchange to calculate the timer. Backoff strategy [17] clustering scheme, which is advance in selecting energetic nodes as cluster heads and eliminating huddling cluster heads, suggests a novel way to configure the timer.

Li et. Al[18] proposed Energy–Efficient Unequal Clustering (EEUC) protocol. It divides the nodes into clusters of unequal size. An overhead free fully distributed clustering scheme, called Slotted Waiting period Energy-Efficient Time Driven clustering algorithm (SWEET) is proposed. Though SWEET is an overhead-free method, it performs even better than some representative clustering schemes in extending system lifetime and enlarging network data capacity[19].

III. RESEARCH METHODOLOGY

The lifetime of a WSN can be defined as the time elapsed until the first node dies, the last node dies, or a fraction of nodes die. To maximize network lifetime, we should consider a trade-off between total energy consumption and energy balancing among sensors.

In cluster-based WSN, there is one sensor called as CH which acts as router. All non-CH nodes transmit their data to their CH, which routes it to the remote PN. However, since CHs consume more energy in aggregating and routing data. We present an optimal allocation of states to sensors which maximizes the efficiency of sensors' energy. Mathematically formulate our problem as an Integer Linear Programming (ILP) model. Resolution method based on a Tabu search algorithm.

PROBLEM: NETWORK LIFE TIME:

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 3, Issue 6, June 2013)

156 Two issues at hand:

1.Extra overhead of the network routing redundant messages

2.Extra overhead of running redundant sensors.

Using clustering-or routing-based compression, duplicate observations are eliminated as they are routed towards the sink

Deactivating redundant sensors minimizes coverage overlap and waste

Reduce unnecessary cost by disabling or turning off redundant sensors…

Reduction from 12 to 7 active

Many papers address separately energy-efficient routing, clustering, and area coverage and connectivity

Many others address integrated problem of area coverage and network connectivity, but do so in flat networks and don’t reap the benefits the energy savings and ease of manageability of cluster-based networks.

―When coverage and connectivity are dealt with separately, the obtained configuration may not be optimal‖

Proposed Algorithm

Parameters:

E-initial = initial energy of nodes

E-Transmission = energy consumed during transmission E-remaining = E-initial - E-transmission

T-life = life time of node.

Remaining life time of node = (E-remaining/E-initial) * T-Life

T-s =sending time-stamp of last data packet

T-R = receiving time stamp of last data packet at base station

LEACH has two phases: the set-up and steady-state.In the set-up phase, the cluster-heads are chosen ―stochastically‖, which is randomly based on an algorithm. A threshold is determined based on this algorithm.

1) The first round will be same as normal leach round.

2) In the 2nd round, each node would send residual energy along with the sending time stamp T-S and the remaining lifetime of battery.

3) When the base station receives the packet, it will calculate T-R - T-S (the difference between receiving timestamp and current time stamp)

4) If difference > = remaining lifetime of node the node will become=cluster head

else

if remaining lifetime = max among all nodes of the cluster choose the node as cluster head

IV. CONCLUSION

Due to the scarce energy resources of sensors, energy efficiency is one of the main challenges in the design of protocols for WSNs. The ultimate objective behind the protocol design is to keep the sensors operating for as long as possible, thus extending the network lifetime. In this paper we have surveyed and summarized recent research works focused mainly on the energy efficient hierarchical cluster-based routing protocols for WSNs. As this is a broad area, this paper has covered only few sample of routing protocols. The protocols discussed in this paper have individual advantages and pitfalls. The proposed work will increase the network lifetime with some intelligent custer selection approach.

REFERENCES

[1 ] S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Tag Hong, ―A Tiny Aggregation Service for Ad-Hoc Sensor Networks‖ SIGOPS Opemting System Review 96, SI (December 2002), pp. 131-146. [2 ] G.J. Pottle, and W.J. Kaiser, ―Embedding the Internet: Wireless

Integrated Network Sensors‖ Communications of the ACM 43,5 [May 2000], pp. 51-58.

[3 ] A. Depedri, A. Zanella, R. Verdone, An energy efficient protocol for wireless sensor networks, in: Autonomous Intelligent Networks and Systems (AINS 2003), Menlo Park, CA, June 30–July 1, 2003. [4 ] W. Heinzelman, Application-specific protocol architectures for wireless networks, Ph.D. thesis, Massachusetts Institute of Technology, 2000.

[5 ] W. R. Heinzelman, A. P. Chandrakasan, H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks, IEEE Transactions on Wireless Communications 1 (4), pp. 660–670, 2002.

[6 ] G. Smaragdakis, I. Matta, A. Bestavros, SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks, in: Second International Workshop on Sensor and Actor Network Protocols and Applications (SANPA 2004), 2004.

[7 ] Q. Li, Z. Qingxin, and W. Mingwen, "Design of a distributed energy efficient clustering algorithm for heterogeneous wireless sensor networks," Computer Communications, vol. 29, pp. 2230-7, 2006.

[8 ] M. Veyseh, B. Wei, and N. F. Mir, "An information management protocol to control routing and clustering in sensor networks," Journal of Computing and Information Technology, vol. 13, pp. 53-68, 2005.

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 3, Issue 6, June 2013)

157

[10 ]H.O. Tan, I. Korpeoglu, Power efficient data gathering and aggregation in wireless sensor networks, Issue on Sensor Network Technology, ACM SIGMOD Record, Vol. 32, No. 4, pp. 66–71, 2003.

[11 ]A. Manjeshwar, Q-A. Zeng, D. P. Agarwal, ―An Analytical Model for Information Retrieval in Wireless Sensor Networks using Enhanced APTEEN Protocol‖, IEEE Trans. Parallel and Distributed Systems, vol. 13, no. 12, pp. 1290-1302, Dec. 2002.

[12 ]N. Thepvilojanapong, Y. Tobe and K. Sezaki, ―HAR: Hierarchy-Based Anycast Routing Protocol for Wireless Sensor Networks,‖ Proc. IEEE Symposium on Applications and the Internet, Trento, Italy, pp. 204-212, 2005.

[13 ]Y. H. Wang, C. H. Tsai and H. J. Mao, ―HMRP: Hierarchy-Based Multipath Routing Protocol for Wireless Sensor Networks‖, Tamkang Journal of Science and Engineering, Vol. 9, No 3, pp. 255-264, 2006.

[14 ]Y. Yang, H.-H. Wu, H.-H. Chen, Short: Shortest hop routing tree for wireless sensor networks, in: Proceedings of IEEE ICC-2006, 2006.

[15 ]O. Younis, S. Fahmy, Heed: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks, IEEE Transactions on Mobile Computing, vol. 3(4), pp. 366–379, 2004.

[16 ]C. Y. Wen and W. A. Sethares, "Automatic decentralized clustering for wireless sensor networks," EURASIP Journal on Wireless Communications and Networking, vol. 2005, pp. 686-97, 2005.

[17 ]C. Yongtao and H. Chen, "A distributed clustering algorithm with an adaptive backoff strategy for wireless sensor networks," IEICE Transactions on Communications, vol. E89-B, pp. 609-13, 2006. [18 ]C. Li, M. Ye, G. Chen, J. Wu, An energy-efficient unequal

clustering mechanism for wireless sensor networks, in: Proceedings of the 2nd IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS’05), 2005.

Figure

FIGURE 1:  ILLUSTRATION OF DATA FLOW IN A CLUSTERED NETWORK

References

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