关键词： Mobile edge computing  

摘要： Caching high-frequency reuse contents at the edge servers in the mobile edge computing (MEC) network omits the part of backhaul transmission and further releases the pressure of data traffic. However, how to efficiently decide the caching contents for edge servers is still an open problem, which refers to the cache capacity of edge servers, the popularity of each content, and the wireless channel quality during transmission. In this paper, we discuss the influence of unknown user density and popularity of content on the cache placement solution at the edge server. Specifically, towards the implementation of the cache placement solution in the practical network, there are two problems needing to be solved. First, the estimation of unknown users' preference needs a huge amount of records of users' previous requests. Second, the overlapping serving regions among edge servers cause the wrong estimation of users' preference, which hinders the individual decision of caching placement. To address the first issue, we propose a learning-based solution to adaptively optimize the cache placement policy without any previous knowledge of the user density and the popularity of the contents. We develop the extended multi-armed bandit (Extended MAB), which combines the generalized global bandit (GGB) and Standard Multi-armed bandit (MAB), to iteratively estimate both a global parameter, i.e., the user density, and individual parameters, i.e., the popularity of each content. For the second problem, a multi-agent Extended MAB based solution is presented to avoid the mis-estimation of parameters and achieve the decentralized cache placement policy. The proposed solution determines the primary time slot and secondary time slot for each edge server. The edge servers estimates expected satisfied user number of caching a content with the overlap information and determine the cache placement solution. The proposed strategies are proven to achieve the bounded regret according to the mathema

关键词： Mobile edge computing  

摘要： We investigate a cooperative federated learning framework among devices for mobile edge computing, named CFLMEC, where devices co-exist in a shared spectrum with interference. Keeping in view the time-average network throughput of cooperative federated learning framework and spectrum scarcity, we focus on maximize the admission data to the edge server or the near devices, which fills the gap of communication resource allocation for devices with federated learning. In CFLMEC, devices can transmit local models to the corresponding devices or the edge server in a relay race manner, and we use a decomposition approach to solve the resource optimization problem by considering maximum data rate on sub-channel, channel reuse and wireless resource allocation in which establishes a primal-dual learning framework and batch gradient decent to learn the dynamic network with outdated information and predict the sub-channel condition. With aim at maximizing throughput of devices, we propose communication resource allocation algorithms with and without sufficient sub-channels for strong reliance on edge servers (SRs) in cellular link, and interference aware communication resource allocation algorithm for less reliance on edge servers (LRs) in D2D link. Extensive simulation results demonstrate the CFLMEC can achieve the highest throughput of local devices comparing with existing works, meanwhile limiting the number of the sub-channels.. © 2021, CC0.

关键词： Mobile edge computing  

摘要： In the forthcoming 6G era, Future Public transport vehicles (F-PTV), such as buses, trains etc. will be designed to cater the communication needs of the commuters that will carry numerous smart devices (smartphones, e-bands etc.). These battery-powered devices need frequent recharging. Since recharging facilities are not readily available while commuting, we envision F-PTVs that will provide in-vehicle recharging via Wireless Energy Transfer (WET) through in-vehicle Access Points. F-PTV will also be internally coated with Intelligent Reflecting Surface (IRS) that reflect incident radio waves towards the intended device to improve signal strength at the receiver, for both information and energy transmissions. F-PTVs will also be equipped with Mobile Edge Computing (MEC) servers to also serve multiple purposes, including reduction in devices’ task completion latency and offering in-vehicle Cloud services. MEC-server offloading will also relieve smart devices’ processors off intensive tasks to preserve battery power. The challenges associated with the IRS-aided integrated MEC-WET model within F-PTV are also discussed. We present simulation to show the effectiveness of such a model for F-PTV. © 2021, CC BY.

关键词： Mobile edge computing  

摘要： To relieve the pressure of processing computation-intensive applications on mobile devices and avoid high latency during data transmission, edge computing is proposed to solve this problem. Mobile edge computing (MEC) allows the deployment of MEC servers at the edge of the network to interact with users on the premise of low transmission delay, thereby improving the quality of service (QoS) for users. However, due to the high mobility of users, with the continuous change of geographical location, when users exceed the signal range of the MEC server, the services they request on the MEC server will also be migrated to other MEC servers. The handoff process may involve high response delays caused by service forwarding, thereby greatly degrading QoS. For the above problems, in this paper, a service migration optimization method based on transmission power control is proposed. First, according to the transmission power of the MEC server, the user's activity range is divided into multiple subregions based on a Voronoi diagram. Therefore, there is one MEC server in each subregion, and the size of each subregion is adjusted by controlling the transmission power of the MEC server to minimize the number of wireless handoffs and the energy consumption of the MEC server. Then, the particle swarm optimization (PSO) is adopted to solve the above multiobjective optimization problem. Finally, the effectiveness of the proposed method is verified through extensive experiments.

关键词： Mobile edge computing  

摘要： Peer-to-Peer (P2P) Networking have a lot of practical applicability’s over the years. File storage and resource sharing are few key areas among the others where such peered network architecture is widely successful. The common building block for P2P networking is to store or locate an identifiable resource, for which there are basically 3 approaches namely (1) local storage/broadcast search (2) global storage/local search (3) distributed storage/distributed search. With the development of mobile hardware and wireless technology, it further became feasible to use mobile devices in these P2P networks. This computing architecture is widely used in Mobile Ad-hoc Network (MANET) for building content sharing applications. Search algorithm and file transfer schemes are the basic components of such content sharing systems. In this article, we provide a brief overview of the resource discovery approaches for peer-to-peer file sharing applications over MANET. We analyze and classify the search techniques into 4 broad schemes, mainly the flooding-based schemes, Distributed Hash Table (DHT) based schemes, advertisement-based schemes, and social network-based schemes. The pros and cons of each technique is summarized. Further, a one-to-one comparison is done across the classes for quick interpretation. We also outline the various issues, and complications that should be taken into consideration while designing any resource discovery algorithm. Further, we briefly discuss the security threats, and present state-of-the-art countermeasures for it. Also, we highlight some important guidelines that need to be focused while designing efficient file sharing applications and services in Mobile Edge Computing (MEC) enabled networks. Comprehensive and in-depth assessments of the related approaches are presented providing clear insights for the future research directions. © 2021, CC BY.

关键词： Mobile edge computing  

摘要： Given the proliferation of wireless sensors and smart mobile devices, an explosive escalation of the volume of data is anticipated. However, restricted by their limited physical sizes and low manufacturing costs, these wireless devices tend to have limited computational capabilities and battery lives. To overcome this limitation, wireless devices may offload their computational tasks to the nearby computing nodes at the network edge in mobile edge computing (MEC). At the time of writing, the benefits of MEC systems have not been fully exploited, predominately because the computation offloading link is still far from perfect. In this article, we propose to enhance MEC systems by exploiting the emerging technique of reconfigurable intelligent surfaces (RIS), which are capable of 'reconfiguring' the wireless propagation environments, hence enhancing the offloading links. The benefits of RISs can be maximized by jointly optimizing both the RISs as well as the communications and computing resource allocations of MEC systems. Unfortunately, this joint optimization imposes new research challenges on the system design. Against this background, this article provides an overview of RIS-assisted MEC systems and highlights their four use cases as well as their design challenges and solutions. Finally, their performance is characterized with the aid of a specific case study, followed by a range of future research ideas. © 2021, CC BY.

关键词： Cloud computing   energy efficient resource allocation   mobile edge computing   service rate   user equipment   utility function  

摘要： In mobile edge computing(MEC),one of the important challenges is how much resources of which mobile edge server(MES)should be allocated to which user equipment(UE).The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU *** paper presents a novel comprehensive utility function for resource allocation in *** utility function considers the heterogeneous nature of applications that a UE offloads to *** proposed utility function considers all important parameters,including CPU,RAM,hard disk space,required time,and distance,to calculate a more realistic utility value for ***,we improve upon some general algorithms,used for resource allocation in MEC and cloud computing,by considering our proposed utility *** name the improved versions of these resource allocation schemes as comprehensive resource allocation *** UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these *** utility function depends upon the UE requests and the distance between UEs and MES,and serves as a realistic means of comparison between different types of UE ***(or selecting)an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging *** show that MES resource allocation is sub-optimal if CPU is the only resource *** taking into account the other resources,i.e.,RAM,disk space,request time,and distance in the utility function,we demonstrate improvement in the resource allocation algorithms in terms of service rate,utility,and MES energy consumption.

关键词： mobile edge computing   adaptive bi-trate video streaming   flexible transcoding strategy   ADMM  

摘要： Adaptive bitrate video streaming(ABR)has become a critical technique for mobile video streaming to cope with time-varying network conditions and different user ***,there are still many problems in achieving high-quality ABR video streaming over cellular *** Edge Computing(MEC)is a promising paradigm to overcome the above problems by providing video transcoding capability and caching the ABR video streaming within the radio access network(RAN).In this paper,we propose a flexible transcoding strategy to provide viewers with low-latency video streaming services in the MEC networks under the limited storage,computing,and spectrum *** to the information collected from users,the MEC server acts as a controlling component to adjust the transcoding strategy flexibly based on optimizing the video caching placement ***,we cache the proper bitrate version of the video segments at the edge servers and select the appropriate bitrate version of the video segments to perform transcoding under jointly considering access control,resource allocation,and user *** formulate this problem as a nonconvex optimization and mixed combinatorial ***,the simulation results indicate that our proposed algorithm can ensure a low-latency viewing experience for users.

关键词： Mobile edge computing   Task scheduling   Computation offloading   Dynamic voltage and frequency scaling   Makespan  

摘要： Mobile edge computing (MEC) is a promising technology to support computation-intensive tasks for mobile devices which are usually associated with limited resources. Many researches from both scientific and industrial field have put focuses on MEC. However, most of them assume that in a MEC environment, the offloaded tasks are independent or that there is only one server in the MEC center. Nevertheless, in reality, tasks with dependencies take the majority and in a MEC center, there are usually multiple servers. Under this circumstance, previous methods no longer take effects. In this work, we consider offloading with precedence constraints among tasks, and try to minimize makespan over a MEC center with multiple servers. This problem becomes more complex given that a task can not start unless its predecessors are completed. To solve the problem, we jointly involve front end task offloading order and back end scheduling to optimize makespan, and propose a corresponding algorithm called joint re-ordering and frequency scaling (JRFS). Extensive experiments have been conducted. The results show that compared with several other methods, JRFS can achieve better makespan. (c) 2019 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   cloud-guided   feature extraction   image retrieval   edge servers  

摘要： Mobile Edge Computing (MEC) can facilitate various important image retrieval applications for mobile users by offloading partial computation tasks from resource-limited mobile devices to edge servers. However, existing related works suffer from two major limitations. (i) High network bandwidth cost: they need to extract numerous features from the image and upload these feature data to the cloud server. (ii) Low retrieval accuracy: they separate the feature extraction processes from the image data set in the cloud server, thus unable to provide effective features for accurate image retrieval. In this paper, we propose a cloud-guided feature extraction approach for mobile image retrieval. In the proposed approach, the cloud server first leverages the relationships among labeled images in the data set to learn a projection matrix P. Then, it uses the matrix P to extract discriminative features from the image data set and form a low-dimensional feature data set. Following that, the cloud server sends the matrix P to the edge server and uses it to multiply the image x. The result P-T x, i.e., image features, is uploaded to the cloud server to find the label of the image with the most similar multiplying result. The label is regarded as the retrieval result and returned to the mobile user. In the cloud-guided feature extraction approach, the matrix P can extract a small number of effective image features, which not only reduces network traffic but also improves retrieval accuracy. We have implemented a prototype system to validate the proposed approach and evaluate its performance by conducting extensive experiments using a real MEC environment and data set. The experimental results show that the proposed approach reduces the network traffic by nearly 93 percent and improves the retrieval accuracy by nearly 6.9 percent compared with the state-of-the-art image retrieval approaches in MEC.