Degree Name

Doctor of Philosophy


School of Electrical, Computer and Telecommunications Engineering


An Internet of Things (IoT) network or a Wireless Sensor Network (WSN) consists of sensor devices and one or more sinks. In general, these sensor devices monitor or collect samples of targets, e.g., vehicles, or their surrounding environment; e.g., the temperature of a room. They then upload their collected samples to a sink for further analysis. A critical issue when operating sensor devices is their energy limitation. To this end, researchers have considered charging sensor devices using a variety of sources, include solar, wind, and Radio Frequency (RF). Consequently, sensor devices with energy harvesting capability are able to operate perpetually assuming they do not spend more than their harvested energy. Apart from energy harvesting technologies, researchers have recently exploited the negligible energy cost afforded by backscatter communications. Consequently, it allows sensor devices to use more of their harvested energy to collect samples that otherwise would be used for active RF transmissions.

To this end, this thesis first addresses a novel target-monitoring problem. Its objective is to maximize a novel Quality of Monitoring (QoM) metric, which is a function of the total target monitoring duration and inversely proportional to sensor-to-target distance. The optimization at hand is to determine the activation schedule of sensor devices in conjunction with the charging schedule of a Hybrid Access Point (HAP). In this respect, this thesis provides three solutions. The first solution uses an Mixed Integer Linear Program (MILP) to obtain the optimal schedule. The second and third solutions determine the charging schedule via a Cross-Entropy (CE) based algorithm and a heuristic named Energy Reallocation Linear Programming Approximation (ERLPA). Simulation results show that (i) QoM is affected by the energy requirement of sensor devices, energy storage capacity, number of channels available to the HAP, sensor sensing radius and energy conversion efficiency of sensor devices, and (ii) both the CE method and ERLPA are capable of producing schedules that are near optimal.

FoR codes (2008)

1005 COMMUNICATIONS TECHNOLOGIES, 010206 Operations Research, 010207 Theoretical and Applied Mechanics



Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.