Abstract
Resource efficiency in wireless ad hoc networks has become a widely studied NP-problem. This problem may be suboptimally solved by heuristic strategies, focusing on several features like the channel capacity, coverage area, and more. In this work, maximizing coverage area and minimizing energy consumption are suboptimally adjusted with the implementation of two of Storn/Price’s Multiobjective Differential Evolution (DE) algorithm versions. Additionally, their extended representations with the use of random-M parameter into the mutation operator were also evaluated. These versions optimize the initial random distribution of the nodes in different shaped areas, by keeping the connectivity of all the network nodes by using the Prim–Dijkstra algorithm. Moreover, the Hungarian algorithm is applied to find the minimum path distance between the initial and final node positions in order to arrange them at the end of the DE algorithm. A case base is analyzed theoretically to check how DE is able to find suboptimal solutions with certain accuracy. The results here computed show that the inclusion of random-M and completion of the algorithm, where the area is pondered with 60% and the energy is pondered with 40%, lead to energy optimization and a total coverage area higher than 90%, by considering the best results on each scenario. Thus, this work shows that the aforementioned strategies are feasible to be applied on this problem with successful results. Finally, these results are compared against two typical bioinspired multiobjective algorithms, where the DE algorithm shows the best tradeoff.
Highlights
Nowadays, it is well known that the distribution of sensors in a wireless ad hoc sensor network (WAHSN) is a challenge in the research of wireless communications, because it is necessary to increase both lifetime and coverage area demand of the network
In [44] the Multiobjective Evolution algorithm based on Decomposition (MOEA/D) and Nondominated Sorting Genetic Algorithm II (NSGAII) are compared to determine which is the best option to maximize the coverage area and minimize the energy consumption in wireless sensor networks (WSN), where it is claimed that MOEA/D gives better results than NSGA-II
It can be seen that the addition of the random-M parameter in Storn’s algorithm helps to converge toward the optimal solution quickly, for Multiobjective Differential Evolution algorithm (MODEA)-BM
Summary
It is well known that the distribution of sensors in a wireless ad hoc sensor network (WAHSN) is a challenge in the research of wireless communications, because it is necessary to increase both lifetime and coverage area demand of the network. It is important to mention that [26,27,28] do not consider ad hoc sensor networks They use the DE algorithm only for WSNs. On the other hand, a DE based topology control mechanism in MANETs is presented in [29] to deploy the nodes with the maximum coverage area. In [44] the Multiobjective Evolution algorithm based on Decomposition (MOEA/D) and NSGAII are compared to determine which is the best option to maximize the coverage area and minimize the energy consumption in WSN, where it is claimed that MOEA/D gives better results than NSGA-II.
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