Open AccessWSN in Monitoring Oil Pipelines Using ACO and GA
Author(s) -
Ola E. Elnaggar,
Rabie Α. Ramadan,
Magda B. Fayek
Publication year - 2015
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2015.05.158
Subject(s) - computer science , wireless sensor network , ant colony optimization algorithms , software deployment , pipeline (software) , pipeline transport , genetic algorithm , node (physics) , process (computing) , set (abstract data type) , computer network , real time computing , distributed computing , artificial intelligence , machine learning , environmental engineering , structural engineering , engineering , programming language , operating system
Wireless Sensor Networks (WSNs) are one of the most important technologies in the fields of wireless networking today. WSNs have a vast amount of applications including sensors embedded in the outer surface of pipeline or mounted along the supporting structure of bridges, robotics and health care. In this paper, we study the issues of linear sensor placement to monitor oil pipelines. We address the problem of optimal number of sensors to be deployed given initial energy of each sensor node and message buffering limitations. The objectives of the deployment process are: 1) maximizing the coverage of the pipe, 2) producing a connected network, and 3) prolonging the overall network lifetime. The paper utilizes two of the evolutionary algorithms to solve the deployment problem which are Genetic Algorithms (GA) and Ant Colony Optimization (ACO). Extensive set of experiments are performed for performance evaluation
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