Optimal sensor deployment in non-convex region using Discrete Particle Swarm Optimization algorithm

Majid, A. S.; Joelianto, Endra

doi:10.1109/ccsii.2012.6470483

Cited by 13 publications

(12 citation statements)

References 13 publications

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“…Ab Aziz et al 36 applied a PSO algorithm with a special fitness value in the dynamic deployment of nodes to improve the traditional coverage calculation function and replaced the more commonly used grid calculation method by Voronoi Diagram to divide the monitored area and calculate the coverage of the monitored area. Majid et al 37 applied the discrete PSO algorithm to optimize the coverage of the nodes in a non-convex region and verified the effectiveness of the algorithm for solving the coverage of a non-convex region.…”

Section: Configuration Mechanismsmentioning

confidence: 99%

Topological configuration and optimization in underwater acoustic sensor networks: A survey

Chen

Dai

et al. 2018

International Journal of Distributed Sensor Networks

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Marine environmental monitoring, marine resource development, and maritime rights and interests protection had put great demands on underwater acoustic sensor networks. At the same time, it was found that the configuration and optimization of the underwater acoustic sensor network had important influence on the network performance and service quality in the uncertain marine environment. According to the characteristics of dynamic evolution of network topology and combining with different practical application scenarios, this article studied and discussed the model description, configuration mechanism, and optimization strategy of underwater acoustic sensor network. First, the classical underwater acoustic sensor network models were divided and discussed from three aspects: dimension, sensor types, and dynamic and static states. The characteristics of the six models are compared and analyzed. Second, according to the characteristics of six different models, the configuration mechanism and optimization strategy are discussed and classified. The configuration mechanism of models was studied from the aspects about determinism, self-adaptation, and group intelligence. The optimization strategy of models was discussed from the aspects about coverage, connectivity, energy consumption, time delay, data quality, and so on, and the relations and differences between different methods were compared and analyzed. Finally, the five future researches direction were expected, in order to provide a clear idea for further research in this field.

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Section: Configuration Mechanismsmentioning

confidence: 99%

Topological configuration and optimization in underwater acoustic sensor networks: A survey

Chen

Dai

et al. 2018

International Journal of Distributed Sensor Networks

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“…Nonconvex deployment areas such as C-shaped and S-shaped topologies are used. Moreover, in [11] the authors focused on the area coverage problem in a nonconvex region, as the region of interest limited by the existence of obstacles, administrative boundaries, or geographical conditions. The authors apply the discrete particle swarm optimization (PSO) algorithm with the use of a grid system to discretize the region of interest.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Optimization of the Distribution and Localization of Wireless Sensor Networks Based on Differential Evolution Approach

Céspedes-Mota

Castañón

Martínez-Herrera

et al. 2016

Mathematical Problems in Engineering

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Location information for wireless sensor nodes is needed in most of the routing protocols for distributed sensor networks to determine the distance between two particular nodes in order to estimate the energy consumption. Differential evolution obtains a suboptimal solution based on three features included in the objective function: area, energy, and redundancy. The use of obstacles is considered to check how these barriers affect the behavior of the whole solution. The obstacles are considered like new restrictions aside of the typical restrictions of area boundaries and the overlap minimization. At each generation, the best element is tested to check whether the node distribution is able to create a minimum spanning tree and then to arrange the nodes using the smallest distance from the initial position to the suboptimal end position based on the Hungarian algorithm. This work presents results for different scenarios delimited by walls and testing whether it is possible to obtain a suboptimal solution with inner obstacles. Also, a case with an area delimited by a star shape is presented showing that the algorithm is able to fill the whole area, even if such area is delimited for the peaks of the star.

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“…[5] PSO Energy and coverage area (by clustering) [6] PSO Coverage area [8] Ant Colony Routing [36] Ant Colony Routing [37] SPEA2 Routing [38] DE, PSO, GA Optimal path [39] Ant Colony Node deployment (target coverage) [40] Combined GA and PSO Node deployment (target coverage) [41] MOSA and NSGA-II Node deployment [42] SPEA-2 and NSGA-II Energy, coverage area [43] NSGA-II and LA Energy, coverage area [44] MOEA/D and NSGA-II Energy, coverage area [45] NSGA-II, MOPSO, H3P Node deployment (target coverage) [46] NSGA-II Node deployment (target coverage), routing where ( , ) is the number of times that the coordinate ( , ) is covered by the node set. Nevertheless, to obtain the proper covered area by the set of nodes, the function ( , ) is computed to verify if the coordinate is covered at least once or if it is not covered at all .…”

Section: Othersmentioning

confidence: 99%

“…Sensor distribution is one of the fundamental problems in WAHSNs and regularly has been suboptimally solved by heuristic strategies such as genetic algorithms, among several others as shown by [4][5][6][7][8]. It is worth mentioning that sensor distribution with minimum energy connectivity is an NP-complete problem [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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

Céspedes-Mota

Castañón

Martínez-Herrera

et al. 2018

Mathematical Problems in Engineering

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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-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-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.

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Optimal sensor deployment in non-convex region using Discrete Particle Swarm Optimization algorithm

Cited by 13 publications

References 13 publications

Topological configuration and optimization in underwater acoustic sensor networks: A survey

Topological configuration and optimization in underwater acoustic sensor networks: A survey

Optimization of the Distribution and Localization of Wireless Sensor Networks Based on Differential Evolution Approach

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

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