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| dc.contributor.author | Bitew, Haimanot | |
| dc.date.accessioned | 2020-06-04T08:05:43Z | |
| dc.date.available | 2020-06-04T08:05:43Z | |
| dc.date.issued | 2020-02-25 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/10888 | |
| dc.description.abstract | Energy consumption, network lifetime and coverage are the main issue when designing wireless sensor networks (WSN). Since performances of WSNs are determined by multiobjective that consists network connectivity, network lifetime, energy consumption, network latency and throughput. The qualities of services (QoS) on WSN is determined by optimization those parameters through finding trade-off points. However, these objectives are contradictory and impossible to solve optimization problems with a single optimal decision. The study concerns on multi-objective optimization to have balanced coverage area of sensor nodes, minimize the energy consumption, and maximize the network lifetime and maintaining connectivity between the currently deployed sensor nodes during the designing of WSN. The Pareto optimal based approach is used to address conflicting objectives and made trade-offs solutions concerning non-dominance using non-dominating sorting genetic algorithm 2 (NSGA-2). We have used network simulator 2(NS2) and check the coverage area, packet delivery ratio, energy consumption and operating lifetime of sensor nodes to evaluate the performance of the proposed scheme. To measure the connectivity between these nodes we had used the metrics like packet delivery ratio, throughput, and the residual energy when the optimization of the trade-off between energy consumption and coverage, energy consumption and lifetime in wireless sensor networks. Packet delivery ratio and throughput are important to give guarantee for the delivery of received informations in base station and nodes. We have calculated them using AWK script from trace files. According to the simulation results, the packet Delivery ratio (PDR) and throughput are 99.02%, 98.98% respectively in Nodes Deployment region of interest I (RoI-I) which is the best from region of interest II (RoI-II). | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Information Technology | en_US |
| dc.title | Pareto Optimal Solution for Multi-Objective Optimization in Designing Wireless Sensor Networks | en_US |
| dc.type | Thesis | en_US |
