关键词： Wireless body area networks   Blockchain   Mobile edge computing   Hybrid signature algorithm   Merkle tree optimization model  

摘要： With the rapid development and widespread application of Wireless Body Area Networks(WBANs),the traditional centralized system architecture cannot handle the massive data generated by the edge ***,in order to ensure the security of physiological privacy data and the identity privacy of patients,this paper presents a privacy protection strategy for Mobile Edge Computing(MEC)enhanced WBANs,which leverages the blockchain-based decentralized MEC paradigm to support efficient transmission of privacy information with low latency,high reliability within a high-demand data security *** this basis,the Merkle tree optimization model is designed to authenticate nodes and to verify the source of physiological ***,a hybrid signature algorithm is devised to guarantee the node anonymity with unforgeability,data integrity and reduced *** security performance analysis and simulation results show that our proposed strategy not only reduces the delay,but also secures the privacy and transmission of sensitive WBANs data.

关键词： mobile edge computing   micro⁃task scheduling   multi⁃hop D2D  

摘要： Mobile Edge Computing(MEC)is a promising solution to tackle the upcoming computing tsunami in 5G/6G era by effectively utilizing the idle resource at the mobile *** this work,a multi⁃hop D2D⁃enabled MEC scenario was studied,where mobile devices at network edge connect and share resources with each other via multi⁃hop *** research focus was on the micro⁃task scheduling problem in the multi⁃hop D2D⁃enabled MEC system,where each task was divided into multiple sequential micro⁃tasks,such as data downloading micro⁃task,data processing micro⁃task,and data uploading micro⁃task,according to their functionalities as well as resource requirements.A joint Task Failure Probability and Energy Consumption Minimization(TFP⁃ECM)problem was proposed,aiming at minimizing the task failure probability and the energy consumption *** solve the problem,several linearization methods were proposed to relax the constraints and convert the original problem into an integer linear programming(ILP).Simulation results show that the proposed solution outperformed the existing solutions(with indivisible tasks or without resource sharing)in terms of both total cost and task failure probability.

关键词： Hate content   Mobile edge computing   Framework   Regulation   Social media  

摘要： Mobile devices with social media applications are the prevalent user equipment to generate and consume digital hate *** objective of this paper is to propose a mobile edge computing architecture for regulating and reducing hate content at the user's *** this regard,the profiling of hate content is obtained from the results of multiple studies by quantitative and qualitative *** resulted in different categories of hate content caused by gender,religion,race,and *** on this information,an architectural framework is developed to regulate and reduce hate content at the user's level in the mobile computing *** proposed architecture will be a novel idea to reduce hate content generation and its impact.

关键词： Computation offloading   Mobile edge computing   Smartphones   Energy consumption  

摘要： Computation offloading has emerged as one of the promising approaches to address the issues of restricted resources, leading to poor user experiences on smartphones in terms of battery life and performance when executing CPU intensive tasks. Meanwhile, an entire research domain has been initiated around this topic and several concepts have been studied touching both the smartphone and the cloud side. In this paper, we develop a categorization of fundamental aspects regarding computation offloading in heterogeneous cloud computing from the perspective of smartphone applications. We refer to heterogeneity in terms of the multitude of smartphone applications, the various uplink channels, and the variety of cloud solutions. We also survey state-of-the-art solutions for the identified categories. Finally, we conclude with a summary of the most important research challenges in making computation offloading reality.

关键词： traffic offloading service   mobile P2P communications   mobile edge computing   anchored k-core problem   adaptive allocation  

摘要： As the rapid growth of mobile social networks,mobile peer-to-peer(P2P)communications and mobile edge computing(MEC)have been developed to reduce the traffic load and improve the computation capacity of cellular ***,the stability of social network is largely ignored in the advances of P2P and MEC,which is related to the social relations between *** plays a vital role in improving the efficiency and reliability of traffic offloading *** this paper,we integrate an edge node and the nearby P2P users as a mobile P2P social network and introduce the problem of adaptive anchored(k,r)-core to maintain the stability of multiple mobile P2P *** aims to adaptively select and retain a set of critical users for each network,whose participation is critical to overall stability of the network,and allocate certain resource for them so that the maximum number of users of all networks will remain engaged and the traffic of cellular network can be *** called the retained users as anchor *** address it,we devise a peer-edge-cloud framework to achieve the adaptive allocation of *** also develop a similarity based onion layers anchored(k,r)-core(S-OLAK)algorithm to explore the anchor *** results based on a real large-scale mobile P2P data set demonstrate the effectiveness of our method.

关键词： Mobile edge computing   Content caching   MEC  

摘要： The demand for digital media services is increasing as the number of wireless subscriptions is growing *** order to meet this growing need,mobile wireless networks have been advanced at a tremendous pace over recent ***,the centralized architecture of existing mobile networks,with limited capacity and range of bandwidth of the radio access network and low bandwidth back-haul network,can not handle the exponentially increasing mobile ***,we have seen the growth of new mechanisms of data caching and delivery methods through intermediate caching *** this paper,we present a survey on recent advances in mobile edge computing and content caching,including caching insertion and expulsion policies,the behavior of the caching system,and caching optimization based on wireless *** of the important open challenges in mobile edge computing with content caching are identified and *** have also compared edge,fog and cloud computing in terms of *** of this paper will get a thorough understanding of recent advances in mobile edge computing and content caching in mobile wireless networks.

关键词： AP clustering   Dynamic user traffic   Mobile edge computing   Mobility-driven   ultra-dense Networks  

摘要： ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it is comparable with or surpasses the density of active mobile *** order to mitigate inter-AP interference and improve spectrum efficiency,APs in UDNs are usually clustered into multiple groups to serve different mobile users,***,as the number of APs increases,the computational capability within an AP group has become the bottleneck of AP *** this paper,we first propose a novel UDN architecture based on Mobile Edge Computing(MEC),in which each MEC server is associated with a user-centric AP cluster to act as a mobile *** addition,in the context of MEC-based UDN,we leverage mobility prediction techniques to achieve a dynamic AP clustering scheme,in which the cluster structure can automatically adapt to the dynamic distribution of user traffic in a specific *** results show that the proposed scheme can highly increase the average user throughput compared with the baseline algorithm using max-SINR user association and equal bandwidth allocation,while it guarantees at the same time low transmission delay.

关键词： Mobile edge computing   Internet of things search   Entity matching   Similarity calculation  

摘要： In recent years,a large number of intelligent sensing devices have been deployed in the physical world,which brings great difficulties to the existing entity *** the increase of the number of intelligent sensing devices,the accuracy of the search system in querying the entities to match the user’s request is reduced,and the delay of entity search is *** use the mobile edge technology to alleviate this problem by processing user requests on the edge side and propose a similar physical entity matching strategy for the mobile edge ***,the raw data collected by the sensor is lightly weighted and expressed to reduce the storage overhead of the observed ***,a physical entity matching degree estimation method is proposed,in which the similarity between the sensor and the given sensor in the network is estimated,and the matching search of the user request is performed according to the *** results show that the proposed method can effectively reduce the data storage overhead and improve the precision of the sensor search system.

关键词： Mobile Edge Computing   energy harvesting devices   offloading   integer linear programming   Internet of things  

摘要： Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from0.979%and gradually decreases to0.87%. Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.

关键词： mobile edge computing   resource allocation   task priority   Lyapunov stability  

摘要： With the emergence of increasingly computing-intensive and delay-sensitive tasks, the processing of computing tasks on cloud servers cannot meet the current needs any longer. The emergence of mobile edge computing (MEC) technology and the popularity of 5G applications can solve these demands. Offloading tasks to the MEC server reduces the energy consumption of local devices, and also has a lower latency than offloading to the cloud server. In this paper, an MEC-edge cloud server collaborative system model with energy harvesting technology is designed to minimize the processing delay of computing tasks by allocating computing resources. We propose an optimal integer linear programming (OILP) algorithm with two steps. Firstly, we propose a Lyapunov stability optimization algorithm based on task priority. With the constraints of local mobile device power stability, the divide-and-conquer idea is used for solving the target values of the processing tasks locally, and the MEC and edge cloud servers separately. Therefore, the objective problem is transformed into an integer linear programming problem, and then an integer linear programming algorithm based on CPU utilization optimization is proposed to obtain a resource allocation scheme. Simulation results show that the proposed OILP algorithm can further reduce the delay, improve the CPU's utilization of the MEC server, and reduce the number of the tasks that cannot be processed under the condition of the energy stability of the local device.