Multiobjective Optimization for a Wireless Ad Hoc Sensor Distribution on Shaped-Bounded Areas

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 consu...

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Bibliographic Details
Published in:Mathematical problems in engineering 2018-01, Vol.2018 (2018), p.1-22
Main Authors: Cárdenas-Barrón, Leopoldo Eduardo, Martínez-Herrera, Alberto F., Castañón, Gerardo, Céspedes-Mota, Armando
Format: Article
Language:English
Subjects:
Ad hoc networks
Applied mathematics
Channel capacity
Connectivity
Control algorithms
Cybernetics
Dijkstra's algorithm
Energy consumption
Evolutionary algorithms
Multiple objective analysis
Optimization
Sensors
Spectrum allocation
Wireless communications
Wireless networks
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2018/7873984