الفهرس 
Chapter 1 : IntroductionOnly 14 pages are availabe for public view
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Abstract
The widespread of wireless sensor networks (WSNs) and their many uses in different aspects of life make them subject to research and investigation to improve their performance and solve their various problems. WSNs suffer from many problems such as area coverage which is decreased by the random deployment of sensor nodes which is considered as a result of using the topology construction algorithms such as the A3 and the energy efficient connected dominating set (EECDS) algorithms. Another problem is the static central position of the sink node in any geometric region of interest (ROI). These problems affect negatively the lifetime of the network and the energy consumption rates. To mitigate the damage caused by these problems this work uses the methodology of meta-heuristics which represented in optimization algorithms to apply the optimization process on the network performance.
Firstly, in this work, a new novel meta-heuristic algorithm for optimization called the lévy flight distribution (LFD) algorithm is proposed based on the various inspirations of Lévy flights (LFs). To evaluate and verify the performance of the LFD algorithm, two optimization test bed problems, the Congress on Evolutionary Computation (CEC) 2017 suite and three engineering problems, including the welded beam design, tension/compression spring design, and pressure vessel design problems are solved. The statistical results revealed that the LFD algorithm can provide superior results in most tests compared to seven well-known meta-heuristic algorithms, such as the simulated annealing (SA), particle swarm optimization (PSO), elephant herding optimization (EHO), moth-flame optimization algorithm (MFO), whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), and Harris Hawks Optimization (HHO) algorithm. The proposed LFD algorithm is employed to fix random deployment damages of the A3 and the EECDS topology construction algorithms in terms of wasting energy, increasing interference between sensor nodes, decreasing the lifetime of the WSN, reducing the area coverage for the ROI, increasing the number of message collisions between sensor nodes, providing several copies of the same information, increasing the communication cost of the WSN, and wasting network resources. Eventually, the LFD algorithm succeeded in achieving a high coverage ratio up to 99.6 % approximately but the A3 and the EECDS algorithms achieved a lower coverage ratio up to 91.3 %, 90.1 % respectively according to the considered different network sizes in the simulation experiments. Also, the LFD algorithm succeeded in providing a better deployment schema than the A3 and the EECDS algorithms and enhancing the detection capability of WSNs by maximizing the coverage ratio and minimizing the overlap between sensor nodes.
Secondly, the Harris hawks optimization (HHO) algorithm is employed to choose the optimal location of the sink node in large-scale wireless sensor networks (LSWSNs) and subsequently, the Prims shortest path algorithm is employed to reconstruct the network by making minimum transmission paths from the sink node to the rest of the sensor nodes. The employed algorithm is compared with other well-known algorithms such as particle swarm optimization (PSO), flower pollination algorithm (FPA), grey wolf optimizer (GWO), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and whale optimization algorithm (WOA). Simulation results on different network sizes showed the superiority of the employed algorithm in terms of energy consumption and localization error, and ultimately prolonging the lifetime of the network in an efficacious way.