关键词： mobile edge computing   multi-UAV   computation efficiency   secure transmission   jamming   trajectory scheduling  

摘要： In this study, we consider a security efficiency maximization problem in a multiple unmanned aerial vehicle (UAV)-aided system with mobile edge computing (MEC). Two kinds of UAVs, including multiple computing UAVs (CUAVs) and multiple jamming UAVs (JUAVs), are considered in this system. CUAVs would receive partial computation bits and send the computation results to ground users. JUAVs do not undertake computing tasks and only send interference signals to counter potential ground eavesdroppers. We jointly optimize the ground user scheduling, UAV power, and UAV trajectory to maximize the security efficiency. The original problem is non-convex and difficult to solve. We first use the Dinkelbach method combined with continuous convex approximation technology, and then propose three corresponding subproblems, including user scheduling subproblem, UAV power subproblem, and UAV trajectory problem. Further, we apply the branch and bound method to solve the user scheduling subproblem, and optimize the two remaining subproblems by introducing auxiliary variables and Taylor expansion. The simulation results show that the proposed scheme can obtain better secure off-loading efficiency with respect to the existing schemes.

关键词： Mobile edge computing   spatial crowdsourcing   task allocation   mobile networks  

摘要： With the progress of mobile devices and the successful forms of using the wisdom of crowds, spatial crowdsourcing has attracted much attention from the research community. The idea of spatial crowdsourcing is recruiting a set of available crowds to finish the spatial tasks located in crowdsourcing locations, e.g., landmarks, by using their handheld devices. This paper addresses the worker recruitment problem in spatial crowdsourcing under the coverage and workload-balancing requirements. The coverage constraint means that any crowdsourcing location should be visited by at least one of the recruited workers to satisfy the Quality-of-Service requirement, e.g., traffic monitoring or climate forecast. In addition, we argue that each crowdsourcing operation has a cost in reality, e.g., data traffic or energy consumption and the resource may be limited at each crowdsourcing location. The objective of this paper is to solve a Coverage and Balanced Crowdsourcing Recruiting (CBCR) problem, which ensures the coverage requirement and minimizes the maximum crowdsourcing cost for any crowdsourcing location. We prove that the CBCR problem is NP-hard in the general case. Then, we discuss the CBCR problem in the 1-D scenario. In the 1-D scenario, we first propose a directionally coverage scheme and further extend it to a Polynomial-Time Approximation Scheme (PTAS) to trade-off the computation complexity and the performance. The performance can be bounded to 2 + epsilon, where epsilon can be an arbitrary small value. Then, we found that there exists a sub-optimal structure, and thus the dynamic programming approach is proposed to find the optimal solution in the 1-D scenario. In the general 2-D scenario, we first prove that it has a sub-modular property and thus the naive greedy algorithm has an approximation ratio of ln n + 1. In addition, we propose a randomized rounding algorithm with an expectation bound of O(log n/log log n). Extensive experiments on realistic traces demonstrat

关键词： Machine learning   Internet of Things   traffic steering   mobile edge computing  

摘要： In the Next Generation Radio Networks(NGRN),there will be extreme massive connectivity with the Heterogeneous Internet of Things(HetIoT)*** millimeter-Wave(mmWave)communications will become a potential core technology to increase the capacity of Radio Networks(RN)and enable Multiple-Input and Multiple-Output(MIMO)of Radio Remote Head(RRH)***,the challenging key issues in unfair radio resource handling remain unsolved when massive requests are occurring *** imbalance of resource utilization is one of the main issues occurs when there is overloaded connectivity to the closest RRH receiving exceeding *** handle this issue effectively,Machine Learning(ML)algorithm plays an important role to tackle the requests of massive IoT devices to RRH with its obvious capacity *** paper proposed a dynamic RRH gateways steering based on a lightweight supervised learning algorithm,namely K-Nearest Neighbor(KNN),to improve the communication Quality of Service(QoS)in real-time IoT *** supervises the model to classify and recommend the user’s requests to optimal RRHs which preserves higher *** experimental dataset was generated by using computer software and the simulation results illustrated a remarkable outperformance of the proposed scheme over the conventional methods in terms of multiple significant QoS parameters,including communication reliability,latency,and throughput.

关键词： DASH   mobile edge computing   multicast   quality of experience   adaptive bitrate algorithm  

摘要： Dynamic adaptive streaming over HTTP(DASH)can adaptively select the appropriate video bitrate for mobile *** edge computing(MEC)scenario is of great benefit to improve the performance of mobile networks by providing computing and storage *** the utilization of spectrum resources can be improved by multicast transmission,but the performance of the multicast transmission will be directly affected by the selected grouping algorithm and resource allocation *** order to improve the quality of experience(QoE)of video users in the 5G MEC scenario,this paper proposes a QoE-driven DASH multicast scheme,which mainly covers the grouping algorithm and the adaptive bitrate(ABR)*** of all,we take the optimized target QoE as the basis for grouping and propose an adaptive grouping algorithm that can dynamically adjust the grouping ***,we design a joint optimization ABR algorithm based on the prediction of QoE,which comprehensively considers the process of resource allocation and bitrate decision-making based on the prediction of QoE of video segments in a certain forward-looking field of *** simulation results show that the proposed DASH multicast scheme performs well in QoE and fairness.

关键词： Mobile edge computing  

摘要： Driven by the recent technological advances and the flourishing innovative applications, mobile edge computing (MEC) has emerged as a promising computing paradigm, aiming to extend cloud computing services from the centralized cloud to the edges of networks. Indeed, it is an essential component in the fifth generation architecture, which can empower users with rapid and powerful computation, cache capacity, energy efficiency, mobility, location, and context awareness support. However, as network access methods become diverse, how to ensure ultra-reliable and low-latency communications in MEC is a very challenging task. This article tries to present a comprehensive survey of relevant research and technological developments in MEC. We first highlight the advantages of MEC in comparison with mobile cloud computing, based on which we then make a holistic overview of MEC, including its related architectures and key enablers. Subsequently, we introduce several specific themes and the existing techniques in MEC, where we are particularly concerned with efficient communication techniques and reliable mechanisms in MEC. Furthermore, we elaborate on the evolution of MEC standardization and discuss several typical application scenarios. Finally, we strive to shed light on some challenges and potential research directions for MEC, which may facilitate the transformation of MEC from theory to practice. © 2017 IEEE.

关键词： Processor scheduling   Servers   Autonomous vehicles   Task analysis   Real-time systems   Job shop scheduling   Delays   Autonomous vehicles   computing scheduling   mobile edge computing   restless multi-armed bandit  

摘要： This paper investigates computing resource scheduling for real-time applications in autonomous driving, such as localization and obstacle avoidance. In our considered scenario, autonomous vehicles periodically sense the environment, offload sensor data to an edge server for processing, and receive computing results from the server. Due to mobility and computing latency, a vehicle travels some distance in the duration between the instant of offloading its sensor data and the instant of receiving the computing result. Our objective is finding a scheduling scheme for the edge sever to minimize the above traveled distance of vehicles. The approach is to determine the processing order according to individual vehicle mobility and computing capability of the edge server. We formulate a restless multi-arm bandit (RMAB) problem, design a Whittle index based stochastic scheduling scheme, and determine the index using a deep reinforcement learning (DRL) method. The proposed scheduling scheme avoids the time-consuming policy exploration common in DRL scheduling approaches and makes effectual decisions with low complexity. Extensive simulation results demonstrate that the proposed indexed-based scheme can deliver computing results to the vehicles promptly while adapting to time-variant vehicle mobility.

关键词： Mobile Edge Computing   Devices priority   Resources allocation   Penalty function   Local search   Discrete particle swarm optimization  

摘要： Computation offloading within Mobile Edge Computing (MEC) networks is a promising new technique, especially in the 5G era. This technique offers leading-edge services to the users of Smart Mobile Devices (SMDs) to reduce the processing time and battery drain. Thus, SMDs tend to offload their heavy processing tasks to preserve battery power and benefit from an important processing power. However, in the era of the Internet of Things (IoT), several subscribers will compete for the available provided resources. Thus, we consider subscribers with a priority property fixed by their contracts with the service provider. In this work, we study a multi-server MEC network with multiple base stations where each one is equipped with a MEC server and provides offloading services to nearby users. Accordingly, we consider the energy consumption, the critical situations of radio resources' insufficiency as well as a penalty function based on the SMDs' priority. Therefore, we formulated a bi-objective optimization problem that jointly minimizes the overall energy consumption and the penalty function while allocating the local processing frequencies for the SMDs, their transmit powers and the radio resources allocated by the Base Station (BS). Consequently, based on the weighted aggregation approach, we propose and study a heuristic solution called Resources Allocation with Priority Devices (RAPD). Finally, simulation experiments were realized to study the RAPD solution performance compared to some effective state of the art solutions, and the simulation results in terms of decision-making time, energy and penalty are very promising. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Mobile handsets   Task analysis   Performance evaluation   Optimization   Medical services   Business   Mobile computing   boldsymbol Multi-Persona mobile computing   intelligent offloading   mobile edge computing   multi-objective optimization   resource management   computation cost  

摘要： Multi-persona mobile computing has begun to make its way to determine the battle about practical strategy for adopting personal devices in workplace. Though its competency, multi-persona performance and viability are critically threatened by the limited resources of mobile devices. In recent years, mobile edge computing (MEC) has risen as promising paradigm within the internet of things era bringing benefits to the proximity of mobile terminals, leveraging intelligent computations offloading services to address the severity of their resource scarcity. Yet, embracing mobile edge-based services to augment personas resources and performance raises new concerns including determining what computations to offload for serving the highest number of mobile devices and reducing the remote execution fees imposed on the institution. In this context, we propose new cost-effective MEC-based solution to address these issues. We develop two-level multi-objective optimization realized through an intelligent offloading decision model able to settle both concerns, by minimizing processing, memory and energy while augmenting virtual mobile instances performance on a wide range of physical devices with minimal offloading service fees. We also propose a redesigned smart genetic-based method able to accelerate and reduce the overhead of offloading decision evaluation. Extensive analysis is performed and the results show that our proposition can get more quickly the offloading strategy than other schemes. The results also demonstrate the ability to enforce the virtual mobile devices by reducing local processing, memory usage, energy consumption and execution time along with acceptable minimal additional fees compared to other techniques.

关键词： Blockchain   Security   Loading   Servers   6G mobile communication   Load modeling   Internet   Block stream loading and execution   blockchain   internet -of- vehicles (IoV)   mobile edge computing  

摘要： 6 G and mobile Internet-of-Vehicles (IoV) technology require a secure, open, and transparent system. Blockchain has the characteristics of decentralization, non-tampering, and traceability, which can improve the robustness, data privacy, and security transparency of the overall system. Therefore, blockchain will be the most promising technology to ensure the security and privacy of 6 G and vehicle networks. In this article, we propose a novel IoV block-streaming service awareness and trusted verification in 6 G. The edge node uploads the microservices and calling diagram of microservices to the blockchain network. The blockchain network serves as an intermediate verification platform for the edge node and the IoV equipment to record the evidence of interaction between the two ends. Moreover, combined with identity-based blind signature technology, we have designed a security scheme in which IoV devices anonymously request services from edge nodes, which can realize the mutual authentication of IoV devices and edge servers while keeping the user's real identity information confidential. In addition, an edge caching mechanism based on user requests and service awareness is designed to pre-compile services at edge nodes, improve the cache hit rate of service requests from vehicle users on the edge server, and achieve efficient processing of resource requests from users.

关键词： Internet of Vehicles   Minimizing Power Overhead   Mobile Edge Computing   Optimization Model   Simulated Annealing Algorithm   Task Offloading  

摘要： By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.