B.3. Communication/Networking and Information Technology
Seyed M. Hosseinirad
Abstract
Due to the resource constraint and dynamic parameters, reducing energy consumption became the most important issues of wireless sensor networks topology design. All proposed hierarchy methods cluster a WSN in different cluster layers in one step of evolutionary algorithm usage with complicated parameters ...
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Due to the resource constraint and dynamic parameters, reducing energy consumption became the most important issues of wireless sensor networks topology design. All proposed hierarchy methods cluster a WSN in different cluster layers in one step of evolutionary algorithm usage with complicated parameters which may lead to reducing efficiency and performance. In fact, in WSNs topology, increasing a layer of cluster is a tradeoff between time complexity and energy efficiency. In this study, regarding the most important WSN’s design parameters, a novel dynamic multilayer hierarchy clustering approach using evolutionary algorithms for densely deployed WSN is proposed. Different evolutionary algorithms such as Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to find an efficient evolutionary algorithm for implementation of the clustering proposed method. The obtained results demonstrate the PSO performance is more efficient compared with other algorithms to provide max network coverage, efficient cluster formation and network traffic reduction. The simulation results of multilayer WSN clustering design through PSO algorithm show that this novel approach reduces the energy communication significantly and increases lifetime of network up to 2.29 times with providing full network coverage (100%) till 350 rounds (56% of network lifetime) compared with WEEC and LEACH-ICA clsutering.
Seyed Mojtaba Hosseinirad; S.K. Basu
Abstract
In this paper, we study WSN design, as a multi-objective optimization problem using GA technique. We study the effects of GA parameters including population size, selection and crossover method and mutation probability on the design. Choosing suitable parameters is a trade-off between different network ...
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In this paper, we study WSN design, as a multi-objective optimization problem using GA technique. We study the effects of GA parameters including population size, selection and crossover method and mutation probability on the design. Choosing suitable parameters is a trade-off between different network criteria and characteristics. Type of deployment, effect of network size, radio communication radius, density of sensors in an application area, and location of base station are the WSN’s characteristics investigated here. The simulation results of this study indicate that the value of radio communication radius has direct effect on radio interference, cluster-overlapping, sensor node distribution uniformity, communication energy. The optimal value of radio communication radius is not dependent on network size and type of deployment but on the density of network deployment. Location of the base station affects radio communication energy, cluster-overlapping and average number of communication per cluster head. BS located outside the application domain is preferred over that located inside. In all the network situations, random deployment has better performance compared with grid deployment.
