Computational Intelligence in Wireless Sensor Networks

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Prince25

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Paradigms of Computational Intelligence (CI) have been successfully used in recent years to address various challenges such as optimal deployment, data aggregation and fusion, energy aware routing, task scheduling, security, and localization.
 
With the advances in MIMS technology, sensor networks applications have been emerging. They are used in battle field, environmental monitoring, health care, rescue operations, and monitoring critical infrastructure such as water and gas pipes as well as important buildings. However, sensor networks face a large number of challenges; for instance, sensor nodes suffer from limited memory, limited computational capabilities, and short sensing and communication ranges. In addition and the most important challenge is the sensors� energy where sensors are usually deployed in unattended environment and they are supposed to function for a long period of time. With these challenges, to form a wireless sensor network (WSN), there are four phases that a network has to go through to operate which are deployment, sensing, routing, and decision making. In the deployment phase, sensors either placed manually or randomly using a flying robot for example in the monitored field. Many problems as well as techniques are used for accurate and efficient sensor deployment.
 
At the same time, computational intelligence techniques including genetic algorithms, swarm intelligence, neural networks, rough set, and fuzzy logic were proven to be efficient in solving many of the hard problems in different fields as well as in WSN.
 
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