Optimal placement, scheduling and routing to maximize lifetime in wireless sensor networks under connectivity restrictions

Abstract

A wireless sensor network consists of distributed autonomous electronic devices called sensors. They are capable of sensing the changes in their vicinity, process the information as data packets and transmit the data to other sensors or a base station namely sink. In order to have an effective sensor network that can keep track of the changes in the interested region, sensors have to work cooperatively since they have limited battery energy. Working in accordance is also important to transmit the collected information eventually to a sink, since sensors can communicate only with the others that fall in a certain range. In most of the real life applications, for a wireless sensor network the number of periods that the network can operate as desired is a significant performance indicator. In this thesis, we propose mixed-integer linear programming models to maximize the network lifetime by optimally determining the locations of sensors, activity schedules of the deployed sensors, sink assignments of the active sensors and their data flow routes to the corresponding sink over a finite planning horizon subject to coverage, flow conservation, energy consumption and budget constraints. Then, we introduce valid inequalities to solve the problem easily. Due to the characteristics of the problem, even the small instances cannot be solved exactly in considerable amount of time and the linear programming relaxations give poor upper bounds. Hence, we develop heuristics using techniques such as Lagrangean relaxation and greedy selection criterion. Computational experiments indicate that the heuristic methods are accurate and efficient.