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.
Fei, Jia, Targets Monitoring and Data Collection in Radio Frequency (RF) Energy Harvesting IoT Networks, Doctor of Philosophy thesis, School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, 2021. https://ro.uow.edu.au/theses1/1225
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.