التوزيع الامثل للمتحسسات في شبكة المتحسسات اللاسلكية باستخدام خوارزمية الامثلية المستوحاة من نظرية الاسراب == Optimum Sensors Deployment In Wireless Sensor Networks Using Swarm Based Bio - Inspired Optimization Algorithms
Author name:
هيثم سعدون عفتان
Supervisor name:
نزار هادي عباس
General topic:
Electrical, Electronic and Communications Engineering
Specific topic:
Electrical Engineering
Degree:
Master
University:
University of Baghdad - College Of Engineering - Department Of Electrical Engineering
Language:
English
University location:
Baghdad
First pages:
34T468 - p.pdf
Abstract:
The increased demand for Wireless Sensor Networks (WSNs) in different areas of application has intensified studies dedicated to the deployment of sensor nodes in the recent past. The deployment of sensor nodes required that some of the key objectives should be satisfied, which are the coverage ratio of the monitoring area and the lifetime of the network.In this thesis, a mathematical model to optimize the coverage ratio and the lifetime of network is developed to ensure a better utility of the WSN. The model is formulated based on several parameters such as the size of the monitoring area, network models, the total number of sensor nodes, visibility requirements, sensing/communication radius, etc.Popular swarm based bio inspired algorithms have been used to optimize the WSN deployment. The coverage optimization process has been carried out by single objective optimization algorithms such as the Ant Colony Optimization (ACO) algorithm, modified version of Particle Swarm Optimization called Discrete PSO (DPSO), Discrete Artificial Bee Colony (DABC) algorithm and a new proposed algorithm called Quantum Artificial Bee Colony (QABC). QABC is based on quantum physical concepts to improve the local search capability of standard ABC algorithm. Thereafter, a multi objective optimization algorithm has been utilized to optimize the coverage ratio and lifetime of WSN. The multi objective optimization has been carried out by Non - dominated Sorting Genetic Algorithm (NSGA - II). All of these algorithms are simple, effective and computationally efficient optimization techniques.The WSN deployment has been simulated using MATLAB 7.12.0 (R2011a) package, NetBeans 7.4 Java integrated development environments, JMETAL 4.5 and Java Universal Network/Graph (JUNG 2.0.1) frameworks. The computer simulation results showed that the proposed algorithm for coverage ratio maximization was up to two times faster than the others. Furthermore, the conducted simulation indicated that the QABC algorithm offered (6%) better solution in terms of coverage in comparison with the others in some cases. Also the results showed that the deterministic deployment can achieve up to (25%) better coverage ratio than the random deployment. In addition, QABC outperformed GA, PSO and ABC algorithms when applied to several test problems. Additionally, the results showed that the NSGA - II algorithm could effectively optimize the network lifetime and coverage ratio and produced good convergent solutions to the Pareto front and was uniformly distributed along it.
