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   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： 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： 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： 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： 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： cloud computing   genetic algorithms   mobile computing   resource allocation   power aware computing   MEC   effective assistant technology   cloud computing   mobile devices   MD   power consumption   user-oriented use case   finite computation capabilities   energy consumption   multiuser multiapplication computation offloading method   multicloudlet environment   joint optimisation   mobile edge computing  

摘要： Mobile edge computing (MEC) is an effective assistant technology that can overcome some defects of cloud computing. For the sake of alleviating the clashes between the capability constraint of cloudlets and the needs of mobile devices (MDs) for reducing executing latency as well as decreasing the power consumption of MDs, a user-oriented use case in the MEC named computation offloading is taken into consideration. Computation offloading is capable of effectively making the MEC adapt to the resources of cloudlets and MDs in different environments, and it is very beneficial to the development of the internet of things. Owing to the finite computation capabilities of the MDs and the resources of cloudlets are heterogeneous and limited;a three-objective model is established to optimise the time consumption, and the energy consumption of MDs as well as the load balancing of cloudlets jointly. Technically, the authors propose an effective multi-user multi-application computation offloading method in the multi-cloudlet environment on the basis of improved non-dominated sorting genetic algorithm III. Finally, comprehensive experiments and analysis were conducted to validate the effectiveness and efficiency of the proposed method.

关键词： Mobile handsets   Bandwidth   Task analysis   Servers   Wireless communication   Computational modeling   Edge computing   Mobile edge computing   wireless caching   multicast   low-latency   bandwidth  

摘要： In this paper, we present a novel mobile edge computing (MEC) model where the MEC server has the input and output data of all computation tasks and communicates with multiple caching-and-computing-enabled mobile devices via a shared wireless link. Each task request can be served from local output caching, local computing with input caching, local computing without local caching or MEC downloading, each of which incurs a unique bandwidth requirement of the multicast link. Aiming to minimize the transmission bandwidth, we optimize the joint caching and computing policy at mobile devices subject to latency, caching, power and multicast transmission constraints. The joint policy optimization problem is shown to be NP-hard. To tackle the problem of intractability of priori knowledge of users' request popularity, we approximate the expectation via sampling. When all the output data size is smaller than the input data size, we reformulate the problem as minimization of a monotone submodular function over matroid constraints and obtain the optimal solution via a strongly polynomial algorithm of Schrijver. Otherwise, by leveraging concave convex procedure together with the alternating direction method of multipliers, we propose a low-complexity high-performance algorithm and prove it converges to a local minimum. Furthermore, in homogeneous case, we theoretically reveal how much bandwidth gain can be achieved from computing and caching resources at mobile devices or the multicast transmission. Our results indicate that exploiting the computing and caching resources at mobile devices as well as multicast transmission can provide significant bandwidth savings.

关键词： Task analysis   Mobile handsets   Processor scheduling   Power demand   Wireless communication   Optimal scheduling   Mobile edge computing   Markov decision process   Lyapunov optimization   power-latency tradeoff  

摘要： Mobile edge computing (MEC) is a promising technique to improve the quality of computation experience for mobile devices by providing computation resources in their close proximity. However, the design of scheduling policies for MEC systems inevitably encounters a challenging optimization problem that should take both transmissions and computations into consideration. In particular, how to jointly schedule transmissions and computations should adapt to the cross-layer system dynamics, i.e., random task arrivals and channel state variations. We formulate this scheduling problem as a joint optimization problem for both transmissions and computations in order to minimize the power consumption of mobile devices, while meeting the latency requirement. With given distributions of the system dynamics, Markov decision process (MDP) is used to model the system operations. Based on this model, the power-optimal scheduling policy can be obtained by converting the joint optimization problem to linear programming (LP) by using variable substitutions and thus the optimal power-latency tradeoff can be achieved. When the distribution information of the system dynamics is unknown, we exploit the Lyapunov optimization to present a low complexity scheduling policy. Our theoretical analysis and extensive simulation studies show that our approach can offer a good tradeoff between power consumption and latency.