Stochastic optimizations of mobile energy management [thesis]

Zhang Yang
With the growing computing power of mobile devices and increasingly sophisticated applications, mobile computing and wireless communication are nowadays pervasive. However, since mobile devices have limited energy storage and sporadic energy supply, energy management remains a critical issue in mobile networks. To address the problem, a few approaches have been introduced to develop intelligent and optimal energy management for mobile networks. Firstly, wireless energy charging can be employed
more » ... ng can be employed to replenish batteries of mobile devices. The mobile devices can harvest or receive energy to charge its battery without being physically connected to any power source. Alternatively, to control the energy consumption and resource usage, a mobile device can offload energy-intensive jobs to other devices, e.g., cloudlets. This thesis aims to address some of the important issues of energy management in mobile networks with wireless energy charging and job offloading. Three major contributions are presented in this thesis. Firstly, with the wireless energy transfer and harvesting technologies (e.g., radio frequency, or namely RF energy), mobile devices are fully untethered as energy supply is more ubiquitous. The mobile devices can receive energy from wireless chargers which can be static or mobile. We introduce the use of a mobile energy gateway that can receive energy from a fixed charging facility, move, and transfer the energy to other mobile users. The mobile energy gateway aims to maximize the utility by taking energy charging/transferring actions optimally. We formulate an optimal energy charging/transferring problem as a Markov decision process (MDP). The MDP model is then solved to obtain an optimal energy management policy for the mobile energy gateway. Furthermore, we prove that the optimal energy management policy has a threshold structure. We conduct an extensive performance evaluation of the MDPbased energy management scheme. The proposed MDP-based scheme outperforms several conventional baseline schemes in terms of expected overall utility. Secondly, we develop an optimal energy charging scheme for the mobile device, considering the states of location, energy storage, as well as stochastic traffic generation which determines energy demand. In this case, energy management takes data flows in the form of traffics (i.e., job processing and data transmission) into consideration. We formulate the i Upon the completion of this thesis, I would like to express the greatest gratitude to my supervisor Prof. Dusit Niyato, for offering me the opportunity to research and pursue my doctorate degree in Nanyang Technological University. Without his guidance and advices, my work on optimizations and mobile energy management topics would not have started. Prof. Dusit Niyato is among the most insightful and vigorous researchers I have ever seen, who always "stays hungry" in learning, creating and contributing to the academic community. From him, I still have too much to learn on becoming a real qualified researcher for now, and for the rest of my career whatever it will be. I would like to thank my co-supervisor Prof. Ping Wang, who has help me a lot in the progress of completing my research during these years as a PhD student. She is a strict and serious researcher, who has pointed out hidden problems and flaws in my research papers for countless times, which I have always failed to discover.
doi:10.32657/10356/65265 fatcat:ccbbdarp3zdozpciq4gxbq3vbe