For the target problem of a directional wireless sensor network, the greedy algorithm can easily fall into the local optimal solution, whereas the genetic algorithm must forecast the lifetime of the upper bound of the network. We propose a novel multi-objective coverage optimization memetic algorithm that encodes the solutions as chromosomes and simulates the biological evolution process in search for a favourable solution to address the aforementioned problems. Experimental results show that the proposed algorithm can prolong the network lifetime more effectively than similar heuristic algorithms in other studies.
A minimum cover set coverage algorithm (MCSCA) for pursuing low energy is presented in this paper to prolong the lifetime of wireless sensor networks. The proposed algorithm improves energy efficiency in three aspects. First, when generating cover sets, the selection strategy of the algorithm considers the contributions of sensor nodes, energy variance, and other factors. The algorithm can cover all targets with a few sensor nodes. Second, useless coverage optimization reduces coverage areas without target nodes to save energy. Third, redundant coverage optimization further saves energy by reducing redundant coverage in wireless sensor networks. Compared with similar heuristic algorithms, the proposed MCSCA can extend network lifetime by 11% on average.
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