Monash University
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Energy harvesting aware clustering-based routing protocols for wireless sensor networks

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posted on 2017-02-28, 03:33 authored by Khan, Md Anit
Routing protocols for wireless sensor networks (WSNs) play an important role in the performance of WSNs. They have an impact on, for instance, energy efficiency, reliable data transmission, channel utilization, and faster data delivery. Routing protocols can be broadly classified into two groups: i) Flat and ii) Hierarchical or clustering based techniques. The latter techniques are more energy efficient and scalable than the former. However, clustering techniques inherently create extra load on cluster heads and cluster heads (CHs) are more prone to breakdown. To address these issues and to support a sustainable environment, energy harvesting aware clustering techniques are evolving. However, there are only a limited number of these techniques available in the literature. Most are either single hop or location aware or not mostly self-organized. Therefore, they are not appropriate and economically viable for medium and large scale WSNs. In this dissertation, we have developed an innovative multi-hop energy harvesting aware clustering technique for location unaware WSNs, the Energy Harvesting Aware Energy Efficient (EHAEE) clustering scheme. EHAEE takes into account the intra-cluster communication cost, maximum storage capacity, and the dynamic values of load, gain rate, and remaining energy of a sensor node during the CHs selection and joining phases. This enables EHAEE to be more self-organized in the clustering process and makes it more suitable for non-uniform node distribution. The performance of EHAEE is evaluated through the network simulation models and is also compared and contrasted with another promising and widely accepted clustering technique, HEED, in the context of many different real-world network scenarios. Simulation results demonstrated that EHAEE increases network lifetime and reliability simultaneously. We have also conducted a statistical significance test using the t-test which exhibits a significant improvement of the performance of EHAEE over HEED.


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Gour Karmakar

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Information Technology (Monash University Gippsland)


Master of Information Technology (Research)

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Faculty of Information Technology

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