An Efficient Clustering based Data Collection using Mobile Sink in Wireless Sensor Networks

Data collection using mobile sink is a popular method that mitigates the sink hole problem in wireless sensor networks. In this regard, rendezvous point based data collection is a well-researched topic as it overcomes the problems of latency in data delivery and the scalability of the network. However, finding the optimal number of rendezvous points and their locations for the construction of the mobile sink tour is a challenging issue. An optimal path reduces delay in data collection and balances the energy consumption of the network. In this paper, we propose an algorithm that builds an efficient path for the mobile sink. The algorithm is based on the affinity propagation clustering which obtains an optimal set of rendezvous points. The extensive simulation results of the proposed algorithm show better performance over the existing algorithms in terms of number of the rendezvous points and path length.