Energy Efficient and Low-Latency Communications for Future Wireless Networks

University of Technology Sydney. Faculty of Engineering and Information Technology.The ever-growing number of smart and mobile devices as well as their emerging applications call for novel solutions to address new challenges in energy efficiency and latency requirements. This thesis aims to develop novel protocols, resource allocation algorithms, and network architectures to enable low-latency services for mobile devices and applications (e.g., mission-critical applications in intelligent transportation systems, healthcare, gaming, and virtual/augmented reality applications). Specifically, we first introduce proactive resource allocation approaches to reduce the communications delay in machine type communications. Exploiting the correlation between smart devices (e.g., sensors), we propose an algorithm to proactively allocate uplink resources for these devices, and thereby reducing the expected uplink delay. Second, to address the energy efficiency problem for hardware-constrained devices, we propose a multi-tier task-offloading network architecture. In this novel network architecture, computation tasks from these devices can be offloaded to a network of computation-aiding servers or fog/edge nodes to minimize the energy consumption subject to the delay constraints of services. Because computing resources on fog nodes are usually limited, while task offloading demands from user devices are high, we develop an unprecedented model, allowing fog nodes and a powerful cloud server to collaborate to meet all tasks' requirements. Our experimental results demonstrate that the proposed solution can attain the optimal energy efficiency while meeting strict latency requirements for all devices and computing tasks. Finally, to address the fairness in allocating communication and computation resources of heterogeneous fog nodes for mobile devices considering diverse requirements (i.e., delay, security, and application compatibility), we adopt the proportional fairness criterion to develop a joint task offloading and resource allocation solution. The experimental results (i.e., fairness indexes, energy benefit, and energy consumption) show that the proposed scheme can attain the maximum proportional fairness in terms of the energy benefit (from offloading to fog nodes)

GR-38 Energy Cost and Efficiency on Edge Computing: Challenges and Vision

The Internet of Things (IoT) has been the key for many advancements in next-generation technologies for the past few years. With a conceptual grouping of ecosystem elements such as sensors, actuators, and smart objects connected to perform complex operations to perform environmental monitoring, intelligent transport system, smart building, smart cities, and endless other possibilities. Edge computing helps the IoT’s reach even further and be more robust by connecting multiple censored devices through the internet and forming powerful computational capabilities. Unfortunately, this computation level comes at a cost as the devices are constantly being used to communicate and perform specific actions. Energy efficiency has focused on finding the optimal way to utilize the latest technologies while retaining the battery power’s longevity. In this paper, we present an outline of the difficulties engaged with planning energy-efficient IoT edge devices and depict recent research that has proposed promising answers that address these challenges. First, we analyze the challenges that IoT devices bring in terms of energy consumption. Next, we discuss the different approaches such as computation offloading, modifying the IoT devices’ designs, and the number of algorithms that help reduce energy consumption and few latest technologies. Finally, we will look at the case study that outlines the energy-saving techniques in smart grids, smart cities, electric vehicles, smart home devices, and VR/AR in real time to apply the concepts proposed.Advisors(s): Dr. Kun SuoTopic(s): IoT/Cloud/Networkin