Federated Learning Meets Contract Theory: Economic-Efficiency Framework for Electric Vehicle Networks

in IEEE Transactions on Mobile Computing (2022), 21(8), 2803-2817

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 ... [more ▼]

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. [less ▲]

Enabling Technologies for Social Distancing: Fundamentals, concepts and solutions

Book published by IET (2022)

The latest advances in several emerging technologies such as AI, blockchain, privacy-preserving algorithms used in localization and positioning systems, cloud computing and computer vision all have great ... [more ▼]

The latest advances in several emerging technologies such as AI, blockchain, privacy-preserving algorithms used in localization and positioning systems, cloud computing and computer vision all have great potential in facilitating social distancing. Benefits range from supporting people to work from home to monitoring micro- and macro- movements such as contact tracing apps using Bluetooth, tracking the movement and transportation level of a city and wireless positioning systems to help people keep a safe distance by alerting them when they are too close to each other or to avoid congestion. However, implementing such technologies in practical scenarios still faces various challenges. This book aims to lay the foundations of how these technologies could be adopted to realize and facilitate social distancing to better manage pandemics and future outbreaks. Starting with basic concepts, models and practical technology-based social distancing scenarios, the authors present enabling wireless technologies and solutions which could be widely adopted to encourage social distancing. They include symptom prediction, detection and monitoring of quarantined people and contact tracing. In the future, smart infrastructures for next-generation wireless systems should incorporate a pandemic mode in their standard architecture and design. [less ▲]

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

in IEEE Transactions on Mobile Computing (2021)

In this paper, we propose novel approaches using state-of-the-art machine learning techniques, aiming at predicting energy demand for electric vehicle (EV) networks. These methods can learn and find the ... [more ▼]

In this paper, we propose novel approaches using state-of-the-art machine learning techniques, aiming at predicting energy demand for electric vehicle (EV) networks. These methods can learn and find the correlation of complex hidden features to improve the prediction accuracy. First, we propose an energy demand learning (EDL)-based prediction solution in which a charging station provider (CSP) gathers information from all charging stations (CSs) and then performs the EDL algorithm to predict the energy demand for the considered area. However, this approach requires frequent data sharing between the CSs and the CSP, thereby driving communication overhead and privacy issues for the EVs and CSs. To address this problem, we propose a federated energy demand learning (FEDL) approach which allows the CSs sharing their information without revealing real datasets. Specifically, the CSs only need to send their trained models to the CSP for processing. In this case, we can significantly reduce the communication overhead and effectively protect data privacy for the EV users. To further improve the effectiveness of the FEDL, we then introduce a novel clustering-based EDL approach for EV networks by grouping the CSs into clusters before applying the EDL algorithms. Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24.63 baseline machine learning algorithms. [less ▲]

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

in IEEE Access (2020), 8

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 ... [more ▼]

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 [less ▲]