An Energy Efficient Tour Construction Using Restricted k-Means Clustering Algorithm for Mobile Sink in Wireless Sensor Networks

There has been much research on efficient energy utilisation to prolong the life-span of wireless sensor networks and other tiny devices, with various techniques deployed to address energy consumption issues. The aim of this paper is to build on previous research and further investigate the use of a...

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Vydané v:2018 11th International Conference on Developments in eSystems Engineering (DeSE) s. 100 - 107
Hlavní autori: Rasul, Aram, Al-Talabani, Abdulbasit
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2018
Predmet:
Clustering algorithms
covering salesman problem
Data collection
disk covering problem
Energy consumption
Heuristic algorithms
k-means clustering algorithms
Mobility
Spread spectrum communication
Urban areas
Wireless sensor networks
ISSN:2161-1351
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Shrnutí:There has been much research on efficient energy utilisation to prolong the life-span of wireless sensor networks and other tiny devices, with various techniques deployed to address energy consumption issues. The aim of this paper is to build on previous research and further investigate the use of a mobile sink for data collection in wireless sensor networks. We aim to find an optimal path for a mobile sink to collect a single packet from each sensor via a single hop and return back to the starting point such that, subject to the length constraint L, total energy wastage is minimised. We have previously referred to this problem as the minimum energy cost mobile sink restricted tour problem and showed that this is NP-hard. We were inspired by the concept of the k-means clustering algorithm and propose a restricted k-means clustering algorithm. In this approach, we first divide the sensing field into a set of k clusters such that the radius of each cluster is R, where R is the maximum transmission range of the sensor. We iteratively increase the value of k until all the sensors are covered under the length constraint. Simplicity, efficiency, and flexibility are the most important and distinctive features of this algorithm. The technique is implemented to evaluate the algorithm and compare it to our previous algorithm. Our simulation results outperformed the previous technique.
ISSN:2161-1351
DOI:10.1109/DeSE.2018.00022