Abstract
ABSTRACTIn most wireless sensor networks (WSN), multi-hop routing algorithm is used to transmit the data collected by sensors to user. Multi-hop forwarding leads to energy hole problem and high transmission overhead in large scale WSN. In order to address these problems, this paper proposes multiple mobile sink based data collection algorithm, which introduces energy balanced clustering and Artificial Bee Colony based data collection. The cluster head election is based on the residual energy of the node. In this study, we focused on a large-scale and intensive WSN which allows a certain amount of data latency by investigating mobile Sink balance from three aspects: data collection maximization, mobile path length minimization, and network reliability optimization. Simulation results show that, in comparison with other algorithms such Random walk and Ant Colony Optimization, the proposed algorithm can effectively reduce data transmission, save energy, improve network data collection efficiency and reliability, and extend the network lifetime.
Highlights
In recent years, there has been massive development in research of wireless sensor networks (WSN), which are commonly used in military, intelligent medical and monitoring applications
In [4], the authors showed that if the cluster size is not properly chosen, the total energy consumption of the network will increase exponentially, either when the cluster size is smaller than the optimal value or when the cluster size is larger than the optimal size
When a single mobile sink is used for data collection, it has to travel a whole deployment area which is not feasible for large scale WSN, so multiple mobile sinks are used for energy efficient data collection
Summary
There has been massive development in research of WSN, which are commonly used in military, intelligent medical and monitoring applications. The data collection algorithms of WSNs should save energy and maximize the network lifetime. Most existing algorithms for balancing the energy consumption of sensors are too difficult to be implemented in practice due to multiple limitations imposed on WSNs for different applications. We propose an efficient algorithm, referred to as the Artificial Bee Colony based mobile sink movement algorithm, for the problem, which balances the workload among the mobile sinks and the energy consumption among the sensor nodes. (2) The path optimization of the mobile Sink can be formulated as a shortest path finding problem; the artificial bee colony algorithm can be used to seek the features of the optimal solution and the shortest path of the mobile Sink so as to improve network data collection efficiency.
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