关键词： Task analysis   Servers   Optimization   Computational modeling   Resource management   Edge computing   Energy consumption   Mobile edge computing   computation offloading   multi-server   multi-task   deep reinforcement learning   deep deterministic policy gradient  

摘要： With the development of computer technology, computational-intensive and delay-sensitive applications are emerging endlessly, and they are limited by the computing power and battery life of Smart Mobile Devices (SMDs). Mobile edge computing (MEC) is a computation model with great potential to meet application requirements and alleviate burdens on SMDs through computation offloading. However, device mobility and server status variability in the multi-server and multi-task scenario bring challenges to the computation offloading. To cope with these challenges, we first propose a parallel task offloading model and a small area-based edge offloading scheme in MEC. Then, we formulate the optimization problem to minimize the completion time of all tasks, and transform the problem into a deep reinforcement learning-based offloading scheme by Markov decision approach. Furthermore, we present a deep deterministic policy gradient (DDPG) approach for obtaining the offloading strategy. Experimental results demonstrate that the DDPG- based offloading approach improves long-term performance by at least 19% in ultra-low latency, efficient usage of servers, and frequent mobility of SMDs over traditional strategies.

关键词： Deep neural networks   Mobile edge computing   Mobility management   Partition offloading  

摘要： The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computationintensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： mobile blockchain   deep reinforcement learning   mobile edge computing   power allocation   bandwidth allocation  

摘要： In order to protect the privacy and data security of mobile devices during the transactions in the industrial Internet of Things(IIoT), we propose a mobile edge computing(MEC)-based mobile blockchain framework by considering the limited bandwidth and computing power of small base stations(SBSs). First,we formulate a joint bandwidth and computing resource allocation problem to maximize the long-term utility of all mobile devices, and take into account the mobility of devices as well as the blockchain throughput. We decompose the formulated problem into two subproblems to decrease the dimension of action space. Then,we propose a deep reinforcement learning additional particle swarm optimization(DRPO) algorithm to solve the two subproblems, in which a particle swarm optimization algorithm is leveraged to avoid the unnecessary search of a deep deterministic policy gradient approach. Simulation results demonstrate the effectiveness of our method from various aspects.

关键词： Blockchain technology   blockchain applications   blockchain and 5g   5g blockchain   wireless blockchain   wireless consensus   byzantine generals   byzantine fault tolerance   blockchain history   bitcoin   Ethereum   pow   proof-of-work   smart contracts   mobile edge computing   blockchain ai   hyperledger  

ISBN: (数字)9781119790815 ISBN: (纸本)9781119790808

摘要： Explore foundational concepts in blockchain theory with an emphasis on recent advances in theory and practice In Wireless Blockchain: Principles, Technologies and Applications, accomplished researchers and editors Bin Cao, Lei Zhang, Mugen Peng, and Muhammad Ali Imran deliver a robust and accessible exploration of recent developments in the theory and practice of blockchain technology, systems, and potential application in a variety of industrial sectors, including manufacturing, entertainment, public safety, telecommunications, public transport, healthcare, financial services, automotive, and energy utilities. The book presents the concept of wireless blockchain networks with different network topologies and communication protocols for various commonly used blockchain applications. You’ll discover how these variations and how communication networks affect blockchain consensus performance, including scalability, throughput, latency, and security levels. You’ll learn the state-of-the-art in blockchain technology and find insights on how blockchain runs and co-works with existing systems, including 5G, and how blockchain runs as a service to support all vertical sectors efficiently and effectively. Readers will also benefit from the inclusion of: A thorough introduction to the Byzantine Generals problem, the fundamental theory of distributed system security and the foundation of blockchain technology An overview of advances in blockchain systems, their history, and likely future trends Practical discussions of Proof-of-Work systems as well as various Proof-of-“X” alternatives, including Proof-of-Stake, Proof-of-Importance, and Proof-of-Authority A concise examination of smart contracts, including trusted transactions, smart contract functions, design processes, and related applications in 5G/B5G A treatment of the theoretical relationship between communication networks and blockchain Perfect for electrical engineers, industry professionals, and students and research

关键词： Telecommunications   5G mobile communication   Edge computing   Cloud computing   Ecosystems   Computational modeling   Cellular networks   Mobile edge computing   multi-access edge computing   telecom operator   cloud ownercess edge computing   5G and beyond   heterogeneous cellular networks   ecosystem   telecom operator   cloud owner   revenue   CAPEX   OPEX  

摘要： The quality-of-service (QoS)/quality-of-experience (QoE) demands of mobile services are soaring and have overwhelmed the obsolescent capability of 3G and 4G cellular networks. The emerging 5G networks will bring an unprecedented promotion in transmission data rates. However, the satisfaction of some service requirements is still in dilemma, especially the end-to-end (E2E) latency which varies in different applications. Multi-access edge computing (MEC), a promising technology in 5G cellular networks, can provide ultra-low E2E latency and reduce traffic load on mobile backhaul networks. The potential benefits of MEC for 5G and beyond services have been explored by preliminary studies. What remains is the uncertainty of revenue from the investment of MEC which will shake operators' decisions about whether and how to deploy MEC in cellular networks. In this light, this paper designs a MEC-assisted 5G and beyond ecosystem inclusive of three players: private (local) telecom operators, backhaul, and cloud service owners. We propose a revenue maximization model for private (local) telecom operators and cloud service owners to minimize the cost from the end-user perspective while satisfying the latency requirement. The derived model indicates that two players' revenues can be maximized by optimizing MEC resources and backhaul capacity. The game-theoretic analyses also reveal the optimized hybrid strategy of MEC and cloud for efficient mobile traffic management.

关键词： Monocular ranging   Kalman filter   SIFT   KCF   Mobile edge computing  

摘要： Distance perception is the basis and necessary prerequisite of environment perception, attitude perception and obstacle avoidance for both intelligent vehicle and unmanned vehicle. Passive ranging is a critical part of machine vision measurement. Most passive ranging methods based on machine vision apply binocular technology that needs strict hardware conditions and lacks universality. Therefore, the monocular vision ranging method is one of the mainstream distance sensing methods at present. In order to improve the accuracy of monocular vision ranging, a monocular vision ranging method based on pixel area and aspect ratio is proposed. Subsequently, this method improves the stability of real-time target detection by introducing Kalman filter processing. Experimental results display that that ranging used by this method has higher accuracy. The mean relative error of the depth measurement is 5% when it is 3-10 m. After introducing Kalman filter, the stability of real-time ranging processing is improved by 25.21%. In this paper, the ROS smart car with real-time target tracking is also realized by the method based on the combination of SIFT-KCF target detection and tracking and monocular ranging.

关键词： Ant colonny algorithme   Intelligence artificial   Migration virtual machines   Mobile edge computing   Routing protocole  

摘要： The whole world is inundated with smaller devices equipped with wireless communication interfaces. At the same time, the amount of data generated by these devices is becoming more important. The smaller size of these devices has the disadvantage of being short of processing and storage resources (memory, processes, energy,…), especially when it needs to process larger amounts of data. In order to overcome this weakness and process massive data, devices must help each other. A low-resource node can delegate the execution of a set of computionly heavy tasks to another machine in the network to process them for it. The machine with sufficient computational resources must also deposit the appropriate environment represented by the adapted virtual machine. Thus, in this paper, in order to migrate the virtual machine to an edge server in a mobile edge computing environment, we have proposed an approach based on artificial intelligence. More specifically, the main idea of this paper is to cut a virtual machine into several small pieces and then send them to an appropriate target node (Edge Server) using the ant colony algorithm. In order to test and prove the effectiveness of our approach, several simulations are made by NS3. The obtained results show that our approach is well adapted to mobile environments. © 2021, Institute of Advanced Engineering and Science. All rights reserved.

关键词： Drones   Authentication   Elliptic curve cryptography   Internet of Things   Public key   Hash functions   Smart grids   Authenticated key agreement   CK adversarial model   certificateless AKA   elliptic curve cryptography   Internet of Drones   mobile edge computing   ProVerif   UAS   UAV  

摘要： Security and privacy are among the most critical challenges on the internet of drones (IoD) network. The communication entities of the IoD network can communicate securely with the use of authenticated key agreement (AKA) based techniques. However, the design of such techniques must balance the tradeoff between security and lightweight features. Recently, Chen et al. proposed an authentication and key sharing scheme for IoD deployment. It is, however, realized after scrutiny that the proposed technique is vulnerable to security attacks under the well-accepted Canetti-Krawczyk (CK) adversary model. Moreover, the scheme applies to the IoD network with only one drones' flying zone. To solve these challenges, this paper proposed a secure lightweight proven authenticated key agreement (SLPAKA) technique for IoD deployment. The technique is free from all the problems identified in the scheme of Chen et al. To ensure the reliability of the SLPAKA, the security of the technique has been assessed from a theoretical method and formal way using the ProVerif cryptographic protocol verification tool. Apart from comparing the performance of SLPAKA with the benchmarking schemes in terms of security, computational cost, and communication cost, the SLPAKA and the technique proposed by Chen et al. are implemented using a python programming language to evaluate and compare their performance in terms of energy consumption and computational time metrics. The results show that the SLPAKA outperforms the technique of Chen et al. and all the other benchmarking techniques in terms of security and lightweight features.

关键词： Task analysis   Mobile handsets   Delays   Resource management   Cloud computing   Servers   Optimization   Applications offloading   mobile edge computing   system delay   resource allocation  

摘要： Thanks to the development of the technologies in the wireless communications and Internet of Things (IoT), the adoption of mobile devices is growing rapidly. Accordingly, the number of multimedia applications like face recognition and augmented reality generated from various mobile devices is growing at an unprecedented rate. The processing of these multimedia applications needs a lot of computation resources and has to be processed as quickly as possible. However, as these mobile devices have limited computation resources, the undesirable response delay will occur. By offloading the multimedia applications to the edge cloud close to the access point (AP) or the cellular base station (BS), mobile edge computing (MEC) is considered as a prospective approach to improve the quality of service (QoS) and enhance the computing capacity of mobile devices. Multimedia applications offloading in a MEC system are studied in this paper. The objective of the studied problem is to minimize the execution delay of multimedia applications of all mobile devices by allocating both the communication resource and the computing resource in the edge servers. An optimization problem is formulated and an efficient multimedia applications offloading scheme is proposed to get the solution. Simulation results are conducted to verify the proposed application offloading method, which show that there is a significant execution delay reduction.

关键词： Streaming media   Quality of experience   Tutorials   Uplink   Resource management   Edge computing   Wireless networks   Wireless communications   5G networks   Internet of Things   mobile edge computing   edge analytics   video analytics   caching   task offloading   video streaming   quality of experience  

摘要： Future wireless networks will provide high-bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from virtual reality to the Internet of Things. To this aim, edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 improves the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bit-rate streaming);an overview of mobile network architectures, enabling technologies, and applications for video edge-C3;video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications);and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined.