Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists

A survey of blockchain and artificial intelligence for 6G wireless communications

The research on the sixth-generation (6G) wireless communications for the development of future mobile communication networks has been officially launched around the world. 6G networks face multifarious challenges, such as resource-constrained mobile devices, difficult wireless resource management, high complexity of heterogeneous network architectures, explosive computing and storage requirements, privacy and security threats. To address these challenges, deploying blockchain and artificial intelligence (AI) in 6G networks may realize new breakthroughs in advancing network performances in terms of security, privacy, efficiency, cost, and more. In this paper, we provide a detailed survey of existing works on the application of blockchain and AI to 6G wireless communications. More specifically, we start with a brief overview of blockchain and AI. Then, we mainly review the recent advances in the fusion of blockchain and AI, and highlight the inevitable trend of deploying both blockchain and AI in wireless communications. Furthermore, we extensively explore integrating blockchain and AI for wireless communication systems, involving secure services and Internet of Things (IoT) smart applications. Particularly, some of the most talked-about key services based on blockchain and AI are introduced, such as spectrum management, computation allocation, content caching, and security and privacy. Moreover, we also focus on some important IoT smart applications supported by blockchain and AI, covering smart healthcare, smart transportation, smart grid, and unmanned aerial vehicles (UAVs). Moreover, we thoroughly discuss operating frequencies, visions, and requirements from the 6G perspective. We also analyze the open issues and research challenges for the joint deployment of blockchain and AI in 6G wireless communications. Lastly, based on lots of existing meaningful works, this paper aims to provide a comprehensive survey of blockchain and AI in 6G networks. We hope this surve..

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence

A Review of Research on Privacy Protection of Internet of Vehicles Based on Blockchain

Numerous academic and industrial fields, such as healthcare, banking, and supply chain management, are rapidly adopting and relying on blockchain technology. It has also been suggested for application in the internet of vehicles (IoV) ecosystem as a way to improve service availability and reliability. Blockchain offers decentralized, distributed and tamper-proof solutions that bring innovation to data sharing and management, but do not themselves protect privacy and data confidentiality. Therefore, solutions using blockchain technology must take user privacy concerns into account. This article reviews the proposed solutions that use blockchain technology to provide different vehicle services while overcoming the privacy leakage problem which inherently exists in blockchain and vehicle services. We analyze the key features and attributes of prior schemes and identify their contributions to provide a comprehensive and critical overview. In addition, we highlight prospective future research topics and present research problems

Emerging drone trends for blockchain-based 5G networks: open issues and future perspectives

Unmanned aerial vehicles, commonly known as drones, are receiving growing research interest due to their ability to carry a multitude of sensors and to connect to mobile networks. They are also able to move freely across the air, which enables the creation of numerous applications that were until now considered impracticable. However, such applications may require high computational resources, reliable connection, and high data transmission rates to accomplish different tasks. Therefore, in this work, first, we discuss 5G communication networks and mobile edge computing (MEC) as promising technologies that can provide several benefits to drone-enabled environments and solve some of the presented issues. We also comment on 5G and MEC approaches, presenting the state of the art and seeking to solve each of the latter issues presented. Afterward, we introduce new security concerns of drone communication networks, given their recent popularity. These concerns are related to the possibility of malicious users taking advantage of this brand new technology, which has made many governments ban drones due to public safety. Next, blockchain technology is brought in as a novel solution to the security issues due to its decentralized nature, making it inherently safe. This article also surveys contributions that make use of each of the technologies mentioned to improve the emerging drone industry. Subsequently, we discuss open issues and future perspectives

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility characteristics of vehicles, it is necessary to cache the user data in advance and learn the most popular and interesting contents for vehicular users. Since user data usually contains privacy information, users are reluctant to share their data with others. To solve this problem, traditional federated learning (FL) needs to update the global model synchronously through aggregating all users' local models to protect users' privacy. However, vehicles may frequently drive out of the coverage area of the VEC before they achieve their local model trainings and thus the local models cannot be uploaded as expected, which would reduce the accuracy of the global model. In addition, the caching capacity of the local RSU is limited and the popular contents are diverse, thus the size of the predicted popular contents usually exceeds the cache capacity of the local RSU. Hence, the VEC should cache the predicted popular contents in different RSUs while considering the content transmission delay. In this paper, we consider the mobility of vehicles and propose a cooperative Caching scheme in the VEC based on Asynchronous Federated and deep Reinforcement learning (CAFR). We first consider the mobility of vehicles and propose an asynchronous FL algorithm to obtain an accurate global model, and then propose an algorithm to predict the popular contents based on the global model. In addition, we consider the mobility of vehicles and propose a deep reinforcement learning algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay. Extensive experimental results have demonstrated that the CAFR scheme outperforms other baseline caching schemes.Comment: This paper has been submitted to IEEE Journal of Selected Topics in Signal Processin

Intelligent Mobile Edge Computing Integrated with Blockchain Security Analysis for Millimetre-Wave Communication

 With the increase in number of devices enabled the Internet of Things (IoT) communication with the centralized cloud computing model. With the implementation of the cloud computing model leads to increased Quality of Service (QoS). The cloud computing model provides the edge computing technologies for the real-time application to achieve reliability and security. Edge computing is considered the extension of the cloud computing technology involved in transfer of the sensitive information in the cloud edge to increase the network security. The real-time data transmission realizes the interaction with the high frequency to derive improved network security. However, with edge computing server security is considered as sensitive privacy information maintenance. The information generated from the IoT devices are separated based on stored edge servers based on the service location. Edge computing data is separated based in edge servers for the guaranteed data integrity for the data loss and storage. Blockchain technologies are subjected to different security problem for the data integrity through integrated blockchain technologies. This paper developed a Voted Blockchain Elliptical Curve Cryptography (VBECC) model for the millimetre wave application. The examination of the blockchain model is evaluated based on the edge computing architecture. The VBECC model develop an architectural model based Blockchain technology with the voting scheme for the millimetre application. The estimated voting scheme computes the edge computing technologies for the estimation of features through ECC model. The VBECC model computes the security model for the data transmission in the edge computing-based millimetre application. The experimental analysis stated that VBECC model uses the data security model ~8% increased performance than the conventional technique