Novel Architectures and Networking Solutions for Intelligent Mobile Edge Computing Networks

University of Technology Sydney. Faculty of Engineering and Information Technology.Mobile edge computing (MEC) has emerged as a highly-effective solution to address the proliferation of smart devices and growing demands for computationally-intensive applications. The key idea of MEC networks is to distribute computing resources closer to mobile users (MUs) by deploying servers at the ``edge'' of the networks, i.e., mobile edge nodes (MENs). Nonetheless, the development of MEC networks has been facing various challenges including the decentralized nature, small coverage, unreliable computing/communication resources, and limited storage capacity of the MENs. This thesis aims to address the above challenges through developing novel collaborative architectures and intelligent networking strategies for MEC networks. Firstly, we introduce a novel MEC network architecture that leverages an optimal joint caching-delivering with horizontal cooperation among MENs. Particularly, we first formulate the content-access delay minimization problem by jointly optimizing content caching and delivering decisions under various network constraints, aiming at minimizing the total average delay for the MEC network. Then, we design centralized and distributed solutions to find the decisions of joint caching and delivering policy for the transformed problem. As the second contribution, we propose a novel economic-efficiency framework for the MEC network to maximize the profits for MENs. Specifically, we first introduce a demand prediction method for MENs leveraging federated learning (FL) approaches. Based on the predicted demands, each MEN can reserve demands from the MEC service provider (MSP) in advance to optimize its profit. Nonetheless, due to the competition among the MENs as well as unknown information from the MSP, we develop a multi-principal one-agent (MPOA) contract-based utility optimization under the MSP's constraints as well as other MENs' contracts. We then develop an iterative algorithm to find the optimal contracts for the MENs. Finally, we propose a novel dynamic FL-based framework leveraging dynamic selection of MENs for the FL process in the MEC network. Particularly, the MSP first implements an MU selection method to determine a set of the best MUs for the FL process according to the location and information significance at each learning round. Then, each selected MU can collect information and offer a payment contract to the MSP based on its collected QoI. For that, we develop an MPOA contract-based policy to maximize the profits of the MSP and learning MUs under the MSP's limited payment budget and asymmetric information between the MSP and MUs

Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks

In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network. Specifically, we first introduce CS-based and CS clustering-based decentralized federated energy learning (DFEL) approaches which enable the CSs to train their own energy transactions locally to predict energy demands. In this way, each CS can exchange its learned model with other CSs to improve prediction accuracy without revealing actual datasets and reduce communication overhead among the CSs. Based on the energy demand prediction, we then design a multi-principal one-agent (MPOA) contract-based method. In particular, we formulate the CSs' utility maximization as a non-collaborative energy contract problem in which each CS maximizes its utility under common constraints from the smart grid provider (SGP) and other CSs' contracts. Then, we prove the existence of an equilibrium contract solution for all the CSs and develop an iterative algorithm at the SGP to find the equilibrium. Through simulation results using the dataset of CSs' transactions in Dundee city, the United Kingdom between 2017 and 2018, we demonstrate that our proposed method can achieve the energy demand prediction accuracy improvement up to 24.63% and lessen communication overhead by 96.3% compared with other machine learning algorithms. Furthermore, our proposed method can outperform non-contract-based economic models by 35% and 36% in terms of the CSs' utilities and social welfare of the network, respectively.Comment: 16 pages, submitted to TM

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice