Data Collection Performance in WSNs by Pattern Variation Discovery

This book provides an extensive knowledge of the most important aspects of Wireless Sensor Networks especially the data collection performance in WSNs.The primary goal of optimization is to minimize the traveling distance by the robot. The problem can be regarded as a special case of the Travelling Salesman Problem with Neighborhoods (TSPN) tiny sensor nodes equipped with sensing communication capabilities and computation can be deployed in large numbers in geographical areas to monitor detect and report events. The problem of irregularities detection is to find those sensory values that deviate significantly from the norm. This problem is important in the sensor network setting because it can be used to identify abnormal or interesting events or faulty sensors. A new approach named pattern variation discovery is used to solve this problem.Detection of irregularities is tightly interrelated to modeling of sensor data. Therefore we propose to detect irregular single-attribute sensor data with respect to time or space by building models. This problem is important in the sensor network setting because it can be used to identify abnormal or interesting events or faulty sensors.