DESIGN OF ALGORITHMS TO ASSOCIATE SENSOR NODES TO FUSION CENTERS USING QUANTIZED MEASUREMENTS

Abstract

Wireless sensor networks (WSNs) typically consist of a significant number of inexpensive sensor nodes, each of which is powered by a battery or another finite energy source that is difficult to replace because of the environment they are in or the cost of doing so. The applications of WSNs include military surveillance, disaster management, target tracking and monitoring environmental conditions. In order to increase the lifespan of WSNs, energy-efficient sensing and communication approaches for sensor nodes are essential. Recently, there has been an increase in interest in using unmanned aerial vehicles (UAVs) as portable data collectors for ground sensor nodes in WSN. Several approaches to solving effective communication between sensor nodes and the fusion center have been investigated in this thesis. Because processing, sensing range, transmission bandwidth, and energy consumption are always limited, it is beneficial not to use all the information provided at each sensor node in order to prolong its life span and reduce communication costs. In order to address this problem, first, efficient measurement quantization techniques are proposed using a single fusion center and multiple sensors. The dynamic bit distribution is done among all the sensors and within the measurement elements. The problem is then expanded to include multiple fusion centers, and a novel algorithm is proposed to associate sensors to fusion centers. The bandwidth distribution for targets which are being monitored by several sensors is addressed. Additionally, how to use the situation in which the sensors are in the coverage radius of multiple fusion centers in order to share the targets between them is discussed. Finally, performance bounded data collection algorithms are proposed where the necessary accuracy for each target is specified. In order to determine the minimum number of data collectors needed and their initial placement, an algorithm is proposed. When there are fewer fixed data collectors than there are regions to collect the data from, a coverage path planning method is developed. Since the optimal solution requires an enormous computational requirement and not realistic for real-time online implementation, approximate algorithms are proposed for multi-objective integer optimization problems. In order to assess each suggested algorithm's effectiveness, many simulated scenarios are used together with baselines and simple existing methods.