| Summary: | 博士 === 國立臺灣大學 === 電信工程學研究所 === 105 === The Internet of Things (IoT) is the inter-networking of smart devices, vehicles, machines, and other items. The key factor for enabling this sophisticated paradigm is the integration of novel communication protocols and technologies, which allows IoT devices to cooperate, communicate, and make decisions. The interconnection of IoT devices can be explained in simple data flows: Devices such as wireless sensors gather data from the environment and transmit the data to data servers for information processing. The processed information is then distributed to devices such as user equipments to achieve greater user utility. Due to the rapid development of the IoT, ubiquitous information gathering, processing, and distribution have escalated the demand of radio resources, which makes radio resources even scarcer. In addition, wireless channels are time-fluctuating. Energy harvesting technology enables self-sustainable IoT devices but harvested energy is usually intermittent and time-varying. Therefore, taking into account radio resource scarcity, channel fluctuations, and energy variations, efficient radio resource allocation in space and time is particularly important in the IoT. Moreover, we also consider selfishness of IoT devices and incomplete information of the network environment. Incomplete information means that the network environment, such as channel conditions and energy levels, is only known to devices themselves. To optimize the network performance, feedback of the incomplete information from devices is required. Note that overall network optimization usually sacrifices individual performance of some devices. Selfish devices can manipulate the network optimization result through untruthful feedback if doing so increase their own performance. The network performance may not be optimal. In this regard, we formulate radio resource allocation problems in the IoT and adopt a mechanism design approach to propose novel IoT resource allocation mechanisms. Our theoretic findings show that the proposed resource allocation mechanisms can induce truthful information feedback from devices so as to achieve optimal equilibrium resource allocation. The equilibrium resource allocation achieves several efficiency and fairness metrics, including maximum system throughput/data fidelity, Pareto efficiency, max-min fairness, proportional fairness. With the proposed pricing schemes, the payment is always made from the devices to the mechanisms. In other words, the mechanisms do not need to pay to ensure truthful feedback (budget balance). Moreover, all devices will join the proposed mechanisms to gain higher utility than without joining (individual rationality).
|
|---|
