Improving on the Reliability of Wireless Sensor Networks

Abstract

The application of wireless sensor networks is gradually gaining popularity in ubiquitous computing. They are used for military surveillance, home automation and monitoring, underwater surveillance, wearable sensor and many others. With their way of deployment and use, these tiny motes are mostly vulnerable to attacks and their mode of transfer of data depletes battery power leaving motes dead after a short while. In this paper an algorithm that can be used to minimize the energy consumed by sensor nodes communicating over multihop links is proposed. The algorithm employs unequal clustering technique taking into consideration the distance of the motes from the base station. Our algorithm considers a base station with sparsely distributed nodes that may not all receive adequate signal strength from the base station. Intuitive performance analysis of the proposed algorithm was done in comparison with EEUC and EAUCF considering their energy metrics and clustering techniques. From the preliminary results obtain, our algorithm performs better than the others and hence more stable and energy efficient clustering algorithm.

Enhancing the Reliability of WSN Through Wireless Energy Transfer

“…From the model in Fig. 1, to mitigate the interference of in the region of C3 and beyond, we assume a distance close to the base station referred to as "close" from the fuzzy logic algorithm from the paper in (Engmann et al 2015). This distance will most likely be our region C3 and beyond (closer to the base station).…”

“…Unequal clustering algorithms form clusters where nodes closest to the base station have smaller number of nodes per cluster as compared to nodes further away (Basagni et al 2013;Engmann et al 2015). The use of distance and the residual energy parameters are used in forming cluster sizes for a large network but do not employ energy harvesting technique (Engmann et al 2015).…”

“…The use of distance and the residual energy parameters are used in forming cluster sizes for a large network but do not employ energy harvesting technique (Engmann et al 2015). For data communication, I assume the use of unequal clustering and propose the implementation of the hybrid method of energy transfer to mitigate the dying of nodes, and hence increase the network lifetime.…”

“…This is because cluster closer perform more intra-cluster and inter-cluster communications than peripheral nodes, depleting them of more energy. In a network, even with unequal clustering where clusters closest to the base station are smaller in sizes compared to clusters further away (Engmann et al 2015;Li et al 2005) these near cluster heads will eventually die leaving some nodes with energy still alive but unable to forward data to the base station.…”