Optimal coverage of Wireless Sensor Network using Termite Colony Optimization Algorithm

Das, Pronaya Prosun; Chakraborty, Nirjhar; Allayear, Shaikh Muhammad

doi:10.1109/iceeict.2015.7307523

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…The proposed method is compared to the TCO algorithm [17] and ACO‐Greedy [14] using Omnet++ simulation modelling software. The implementation parameters are presented in Table 1.…”

Section: Simulation and Resultsmentioning

confidence: 99%

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Distributed energy efficient algorithm for ensuring coverage of wireless sensor networks

Dezfuli

Barati

2019

IET Communications

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“…The proposed method is compared to the TCO algorithm [17] and ACO‐Greedy [14] using Omnet++ simulation modelling software. The implementation parameters are presented in Table 1.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…The termite colony algorithm for optimising coverage in the wireless sensor network is used in [17]. Some parameters such as the size of network, the number of nodes and the number of termites are used to calculate the coverage area.…”

Section: Related Workmentioning

confidence: 99%

Distributed energy efficient algorithm for ensuring coverage of wireless sensor networks

Dezfuli

Barati

2019

IET Communications

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“…Other approaches use convex optimization techniques, such as the Simplex Algorithm [52], the Bundle Methods [49], the Nelder-Mead Algorithm [49], the Quasi-Newton BFGS Algorithm and the conjugate gradient search procedure [52,57]. A wide range of metaheuristics are also used in the literature of WLAN deployment optimization: genetic algorithms [49,52,58], Simulated Annealing [52], Large-Neighbourhood and Tabu Searchs [47], Termite Colony Algorithm [48] or Particle Swarm Optimization [59,60]. Finally, a few authors use techniques from Constraint Programming [51,61] and Black-Box Optimization [53,62].…”

Section: Solution Methodsmentioning

confidence: 99%

Optimal deployment of indoor wireless local area networks

et al. 2022

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We present a two-phase methodology to address the problem of optimally deploying indoor wireless local area networks. In the first phase, we use Helmholtz's equation to simulate electromagnetic fields in a typical environment such as an office floor. The linear system which results from the discretization of this partial differential equation is solved with a state-of-the-art library for sparse linear algebra. In the second phase, we formulate the network deployment problem in the setting of Binary Linear Programming. This formulation employs the simulator output as input parameters, and jointly optimizes the number of Access Points, their locations, and their emission channels. We prove that this optimization problem is NP-Hard, and use mathematical programming based techniques and heuristics to solve it. We present numerical experiments on medium-sized buildings.

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“…The studied optimization problems are often NP-Hard and it is frequent that exact solution approaches do not manage to prove optimality or even find a satisfying solution in a reasonable time. To solve such combinatorial deployment problems, several work recourse to heuristics [46] or meta-heuristics to speed-up the solution process: genetic algorithms [2,33,22], Simulated Annealing [33], Large-Neighbourhood and Tabu Search [6], Termite Colony Algorithm [17] or Particle Swarm Optimization [20,39]. Finally, a few authors use techniques from Constraint Programming [48] and Black-Box Optimization [32].…”

Section: Mathematical Programming Solution Approachesmentioning

confidence: 99%