Abstract

Clustering is an effective topology approach in wireless sensor network, which can increase network lifetime and scalability. Either Node Mobility or Channel fading has a negative impact on various clustering protocol. In case of Node Mobility when all sensor nodes are mobile the current nearest cluster head may be the farthest one for the sensor node when message transmission phase starts. In the present research the received signal strength is used to estimate the sensor location. Consequently, channel fading affects the path loss between the nodes thus affecting the estimated distance between them. This paper introduces a new clustering protocol which is built on Adaptive Decentralized re-clustering protocol called E-ADRP (Enhanced Adaptive Decentralized re-clustering protocol). Simulations are performed to test the effect of node mobility using Random Walk Mobility model (RWM) on Low Energy Adaptive Clustering Hierarchy (LEACH) and Enhanced Adaptive Decentralized re-clustering protocol (E-ADRP). The simulation results show that the applied mobility on LEACH affected the network lifetime and energy dissipation negatively while in contrast E-ADRP simulation results were much better. On the other side, Rayleigh channel model also was applied on LEACH and E-ADRP clustering algorithms. The simulation results show that network lifetime and energy dissipation at mobile nodes were nearly stable compared to static nodes in case of E-ADRP while in case of LEACH mobile nodes were negatively affected by rate up to 24% less than static nodes, at fading E-ADRP and LEACH were both negatively affected where E-ADRP was affected by rate up to 40% less than static nodes and LEACH was affected by rate up to 50% less than static nodes.

Highlights

  • Wireless sensor networks (WSNs) have gained world-wide attention in recent years

  • The performance of sensors are affected by sensor mobility but as it is seen Low Energy Adaptive Clustering Hierarchy (LEACH) is more affected by mobility than Enhanced Adaptive Decentralized re-clustering protocol (E-Adaptive Decentralized re-clustering protocol (ADRP)), that is because LEACH randomly choose its cluster heads according to probability threshold which may determine cluster heads to be at the edge of its own clusters so mobility may lead to probably movement of cluster head far away maybe double the distance away from its cluster members while in E-ADRP re-clustering occurs which choose the best cluster head for all clusters based on best residual energy and position

  • The effect of sensor mobility and fading is studied on both Enhanced Adaptive Decentralized re-clustering protocol (E-ADRP) and Low Energy Adaptive Clustering Hierarchy (LEACH)

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Summary

Introduction

Wireless sensor networks (WSNs) have gained world-wide attention in recent years. These networks are composed of tiny and compact devices called sensor nodes which contain three components: a sensing subsystem for data acquisition from the physical surrounding environment, a processing subsystem for local data. Processing and storage, and a wireless communication subsystem for data transmission (Al-Karaki & Kamal, 2004) These Sensor Nodes are deployed in a remote area to detect certain phenomena collect the information of interest, perform local processing for these data and forward that data to base station (BS) directly or through a neighboring relay node. Threshold-sensitive Energy Efficient sensor Network protocol (TEEN) (Manjeshwar & Agarwal, 2001) is designed for time-critical applications. Adaptive Decentralized re-clustering protocol (ADRP) (Bajaber & Awan, 2011) provides an efficient way to improve the scalability and lifetime of Wireless Sensor Network. The paper is organized as follows: section 2 defines system model, section 3 provides an overview of the used clustering techniques, section 4 presents the simulation setup and results and section 5 concludes the paper

System Model of Clustering Algorithm
Clustering Techniques
Simulation and Discussion
Simulating Different Network Size
Effect of Sensor Mobility
Findings
Effect of Channel Fading on Clustering
Conclusion
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