Proceedings Article10.1109/IWCMC.2016.7577141
Wireless sensor network coverage problem using modified fireworks algorithm
Eva Tuba,Milan Tuba,Dana Simian +2 more
- 01 Sep 2016
- pp 696-701
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TL;DR: This paper proposes a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compares it with other approaches from literature, where the algorithm proved to be very robust and better, considering all conducted tests.
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Abstract: Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it with other approaches from literature, where our algorithm proved to be very robust and better, considering all conducted tests.
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Citations
Static drone placement by elephant herding optimization algorithm
Ivana Strumberger,Nebojsa Bacanin,Slavisa Tomic,Marko Beko,Milan Tuba +4 more
- 01 Nov 2017
TL;DR: An implementation of the recent elephant herding optimization algorithm for solving the static drone location problem is presented and the objective of the model applied is to establish monitoring of all targets with the least possible number of drones.
74
3-D Deployment Optimization for Heterogeneous Wireless Directional Sensor Networks on Smart City
TL;DR: Based on 3D urban terrain data, this paper transformed the deployment problem into a multiobjective optimization problem, in which objectives of Coverage, Connectivity Quality, and Lifetime, as well as the Connectivity and Reliability constraints, were simultaneously considered.
Mobile Robot Path Planning by Improved Brain Storm Optimization Algorithm
Eva Tuba,Ivana Strumberger,Dejan Zivkovic,Nebojsa Bacanin,Milan Tuba +4 more
- 08 Jul 2018
TL;DR: This paper proposes path planning method in environments with static obstacles based on the recent swarm intelligence algorithm, brain storm optimization, improved by local search procedure that each new candidate solution moves to the local best position thus reducing computational time.
55
Wireless Sensor Network Localization Problem by Hybridized Moth Search Algorithm
Ivana Strumberger,Eva Tuba,Nebojsa Bacanin,Marko Beko,Milan Tuba +4 more
- 25 Jun 2018
TL;DR: This paper presents hybridized recent swarm intelligence moth search algorithm adapted for solving localization problem in wireless sensor networks, and demonstrates promising approaches for dealing with this kind of problem.
34
Drone Placement for Optimal Coverage by Brain Storm Optimization Algorithm
Eva Tuba,Romana Capor-Hrosik,Adis Alihodzic,Milan Tuba +3 more
- 14 Dec 2017
TL;DR: This paper proposes recent brain storm optimization algorithm for finding the locations for static drones, which maximizes the number of covered targets while minimizing drones altitude.
31
References
Coverage Maximization and Energy Conservation for Mobile Wireless Sensor Networks: A Two Phase Particle Swarm Optimization Algorithm
Nor Azlina Ab Aziz,Ammar Mohemmed,Mohamad Yusoff Alias,Kamarulzaman Ab. Aziz,Syabeela Syahali +4 more
- 27 Sep 2011
TL;DR: The simulation results show that the proposed algorithm increases the coverage and reduces the energy consumption significantly and the algorithm performance is evaluated through simulation using different WSNs.
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Fireworks algorithm for RFID network planning problem
Milan Tuba,Nebojsa Bacanin,Marko Beko +2 more
- 21 Apr 2015
TL;DR: A comparative analysis with other state-of-the-art metaheuristics proved that the proposed approach outperformed other algorithms and was successful in achieving total coverage without interference with smaller number of deployed readers and less transmitted power.
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Particle swarm optimization for coverage maximization and energy conservation in wireless sensor networks
Nor Azlina Ab Aziz,Ammar Mohemmed,Mengjie Zhang +2 more
- 07 Apr 2010
TL;DR: This paper proposes a PSO based algorithm for maximizing the coverage subject to a constraint on the maximum distance any sensor can move, and shows that the proposed algorithm achieves good coverage and significantly reduces the energy consumption for sensors repositioning.
23
Target coverage optimisation of wireless sensor networks using a multi-objective immune co-evolutionary algorithm
TL;DR: The experiment results show that the MOICEA can obtain promising performance in efficiently searching optimal vertex set by comparing with other approaches and compares its performance with that of integer linear program and genetic algorithm in terms of four objectives while maintaining network connectivity.
22
Sensing task assignment via sensor selection for maximum target coverage in WSNs
TL;DR: The sensing task is defined as an optimization problem of adjusting the sensing range parameter jointly with selection of nodes in a target coverage mission and an energy consumption model for the sensing operation is derived and a distributed greedy-based heuristic is proposed.
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