Abstract

We present a sleep/wake schedule protocol for minimizing end-to-end delay for event driven multi-hop wireless sensor networks. In contrast to generic sleep/wake scheduling schemes, our proposed algorithm performs scheduling that is dependent on traffic loads. Nodes adapt their sleep/wake schedule based on traffic loads in response to three important factors, (a) the distance of the node from the sink node, (b) the importance of the node's location from connectivity's perspective, and (c) if the node is in the proximity where an event occurs. Using these heuristics, the proposed scheme reduces end-to-end delay and maximizes the throughput by minimizing the congestion at nodes having heavy traffic load. Simulations are carried out to evaluate the performance of the proposed protocol, by comparing its performance with S-MAC and Anycast protocols. Simulation results demonstrate that the proposed protocol has significantly reduced the end-to-end delay, as well as has improved the other QoS parameters, like average energy per packet, average delay, packet loss ratio, throughput, and coverage lifetime.

Highlights

  • The latest advances in distributed computing and micro electro mechanical systems have enabled in the past few years the emergence of a variety of wireless sensor network applications comprising military [1], disaster management [2], building, health [3], environment, industry, and domains

  • Performance of proposed protocol is compared with the two contemporary protocols: S-MAC [17] and Anycast protocol [33]

  • The performance of Scheme for Minimizing End-to-end Delay (SMED) is compared against the SMAC and Anycast protocols

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Summary

Introduction

The latest advances in distributed computing and micro electro mechanical systems have enabled in the past few years the emergence of a variety of wireless sensor network applications comprising military [1], disaster management [2], building, health [3], environment, industry, and domains. The proposed protocol does not use a generic sleep/wake schedule for all the nodes, rather it uses a heuristic which maximizes the active duration of the nodes according to their expected traffic load at three different levels.

Results
Conclusion

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