关键词： Task analysis   Mobile handsets   Servers   Energy consumption   Computational modeling   Adaptation models   Mobile applications   Mobile edge computing   offloading   resource allocation   reinforcement learning   task dependency  

摘要： Mobile edge computing (MobEC) builds an Information Technology (IT) service environment to enable cloud-computing capabilities at the edge of mobile networks. To tackle the restrictions in the battery power and computation capability of mobile devices, task offloading for using MobEC is developed and used to reduce the service latency and to ensure high service efficiency. However, most of the existing schemes only focus on one-shot offloading, while taking less into consideration the task dependency. It is urgently needed a more comprehensive and adaptive way to take both the energy constraint and the inherent dependency of tasks into account, since modern communication networks have increasingly become complicated and dynamic. To this end, in this paper, we are motivated to study the problem of dependency-aware task offloading decision in MobEC, aiming at minimizing the execution time for mobile applications with constraints on energy consumption. To solve this problem, we propose a model-free approach based on reinforcement learning (RL), i.e., a Q-learning approach that adaptively learns to optimize the offloading decision and energy consumption jointly by interacting with the network environment. Simulation results show that our RL-based approach is able to achieve significant reduction on the total execution time with comparably less energy consumption.

关键词： Sharing economy   cognitive processing at the edge   mobile edge computing   Blockchain   smart city  

摘要： In this paper, we propose a Blockchain-based infrastructure to support security- and privacy-oriented spatio-temporal smart contract services for the sustainable Internet of Things (IoT)-enabled sharing economy in mega smart cities. The infrastructure leverages cognitive fog nodes at the edge to host and process offloaded geo-tagged multimedia payload and transactions from a mobile edge and IoT nodes, uses AI for processing and extracting significant event information, produces semantic digital analytics, and saves results in Blockchain and decentralized cloud repositories to facilitate sharing economy services. The framework offers a sustainable incentive mechanism, which can potentially support secure smart city services, such as sharing economy, smart contracts, and cyber-physical interaction with Blockchain and IoT. Our unique contribution is justified by detailed system design and implementation of the framework.

关键词： Unsaturated analysis model   real-time data self-organizing push   task collaborative scheduling model   mobile edge computing  

摘要： Wireless self-organizing networks have received extensive attention and research because they do not require fixed infrastructure support. Due to the complexity of data transmission in edge computing, the core cloud data center is very far away from the user, resulting in a very large delay in obtaining data from the mobile terminal. In view of this, this paper provides a nearby computing and processing service for user services by deploying multiple mobile edge computing servers on the base station side near the user's mobile core network edge. Firstly, this paper establishes an accurate DCF (Distributed Coordination Function) analysis model under unsaturated conditions. An analytical model for the statistical characteristics of service time is constructed. This model distinguishes the different effects of successfully transmitted packets and discarded packets. Secondly, this paper uses the maximum centrality theory of data aggregation transmission algorithm to determine the degree of influence of nodes by degree centrality theory to generate a fixed unbalanced spanning tree. On the basis of the spanning tree, a relatively large set of parallel nodes can be obtained, thereby reducing the delay time in the entire data transmission process. Finally, this paper studies two resource joint scheduling strategies of mobile terminals and MEC (Mobile Edge Computing) servers in mobile cellular networks based on MEC system. The performance of two collaborative scheduling strategies is verified by simulation. The simulation results show that the collaborative scheduling strategy proposed in this paper can effectively reduce the service cost of the system under the condition of ensuring the user service experience.

关键词： Mobile edge computing   multi-access edge computing   mobile edge computing video service   mobile cloud computing   virtual reality streaming   video streaming  

摘要： Recently, new services using virtual reality (VR)/augmented reality (AR) have appeared and then exploded in entertainment fields like video games and multimedia contents. In order to efficiently provide these services to users, an infrastructure for mobile cloud computing with powerful computing capabilities is widely utilized. However, existing mobile cloud system utilizes a cloud server located at a relatively long distance, so that there are problems that a user is not effectively provided with personalized immersive multimedia service. So, this paper proposes the home VR streaming system that can provide fast content access time and high immersiveness by using mobile edge computing (MEC).

关键词： Mobile edge computing   non-orthogonal multiple access   unmanned aerial vehicles   trajectory design  

摘要： Mobile edge computing (MEC) has been considered as a promising technique to address the explosively growing computation-intensive applications. Thanks to the flexibility of the unmanned aerial vehicles (UAVs), the UAV-assisted MEC can serve mobile terminals (MTs) effectively, i.e., the computing server installed on the UAV can flexibly change its location to serve MTs. Moreover, since non-orthogonal multiple access (NOMA) is able to accommodate massive connectivity, the NOMA-based and UAV-assisted MEC can provide flexible computing services for MTs in large-scale access networks (e.g., sensor networks and Internet of Things). However, due to the diversity of the UAV's trajectory and the interference among MTs introduced by NOMA, the performance (e.g., energy consumption and delay) of the NOMA-based and UAV-assisted MEC system is adversely affected. Therefore, in this paper, we formulate an optimization problem to minimize the largest energy consumption among MTs by jointly optimizing the trajectory, task data and computing resource allocations, and then propose an iterative algorithm to solve the optimization problem. Furthermore, to minimize the largest energy consumption among MTs with lower complexity, we propose a fixed point service scheme and optimize the location of the fixed point. The simulation results show that the proposed optimization algorithms can effectively reduce the largest energy consumption among MTs and ensure the fairness among MTs.

关键词： Computation offloading   mobile edge computing   non-orthogonal multiple access   offloading decision   resource allocation  

摘要： Mobile edge computing (MEC) is becoming a promising paradigm to provide computing services for smart mobile devices (SMDs) via offloading computation-intensive tasks to MEC servers deployed at the network edge. In this paper, in order to further improve the accessing capacity of MEC systems and minimize all users' computation overhead, taking advantage of the superior spectral efficiency of Non-Orthogonal Multiple Access (NOMA) technology, we introduce NOMA into MEC systems and investigate a multi-user computation offloading problem through jointly optimizing offloading decisions, communication and computation resources allocation. To tackle the formulated mixed integer nonlinear programming (MINLP) problem which is NP-hard, we iteratively update either the resource allocation or the offloading decision via fixing the other solution and efficiently solve it in polynomial time. Specifically, given a fixed offloading decision, the sub-channel assignment problem is solved via applying a many-to-one matching model with peer effects, the transmission power of SMDs is optimized by combing sequential convex programming and parametric convex programming, and the computation resources allocation is addressed by convex optimization. Furthermore, the results of resource allocation are applied to guide the offloading decision. Extensive simulations show that our proposed algorithm performs closely to the optimal solution, and compared with existing solutions, our algorithm can efficiently improve the accessing capacity of MEC systems and reduce the total computation overhead of all users.

关键词： Content delivery network   mobile edge computing   optimal caching strategy   zone-based cooperative content caching  

摘要： Mobile edge computing (MEC) is a promising solution to meet the latency requirement for delay-sensitive services in a 5G radio access network (RAN). Its key idea is to deploy computing and storage capacities at the edge of the RAN to quickly provision content and processing capacities as required by the users. Efficient content caching and delivery are key issues to ensure the success of this technique. This paper proposes a zone-based cooperative content caching and delivery scheme for a RAN supporting MEC (MEC-RAN), where the RAN is modelled as a zone and is further sub-divided into multiple sub-zones. Content items are cooperatively cached and delivered among multiple sub-zones. The caching problem is formulated as a mixed integer linear programming model. We also develop a heuristic cooperative content caching strategy to decide the content items to be cached in each MEC server. This novel strategy divides the storage space in each MEC server into two parts. The firrst part caches locally popular contents and the second part is used to cooperatively cache zone-wide popular items. We study the proposed scheme both through simulations and implementation on a testbed that consists of a subnetwork on our campus and commercial cloud service from Ali-Cloud. Both of these show that the proposed zone-based scheme performs better than other typical caching strategies in terms of average content delivery latency and balanced loading of the MEC servers.

关键词： Mobile edge computing   edge caching   joint optimization   convex optimization   block coordinate descent  

摘要： Mobile edge computing (MEC) has attracted extensive studies recently due to its ability to augment the computational capabilities of mobile devices. This paper considers a cache-enhanced multiuser MEC system where the task can be cached in the MEC servers to avoid the transmission of duplicate data. To further improve the energy efficiency and satisfy the users' requirement on delay, we jointly optimize caching, computation, and communication resources in this system. The formulated problem is a mixed integer non-convex optimization problem that is very challenging to solve. We thus propose an efficient iterative algorithm by jointly applying the block coordinate descent and convex optimization techniques, which is guaranteed to converge at least a suboptimal solution. Specifically, the formulated joint optimization problem is decomposed into two subproblems to optimize caching policy and resource allocation, respectively, which are alternately optimized by convex optimization in each iteration. To further speed up the algorithm convergence, an efficient initialization scheme based on the linear weighted method is proposed for caching policy. The extensive simulation results are provided to demonstrate that the proposed jointly optimizing caching, computation, and communication method can improve the energy efficiency with lower time cost compared with other benchmark methods.

关键词： Task analysis   Trajectory   Unmanned aerial vehicles   Three-dimensional displays   Wireless communication   Energy consumption   Servers   Unmanned aerial vehicle   Internet of Thing   mobile edge computing   3D trajectory design   cache deployment  

摘要： This paper studies an unmanned aerial vehicle (UAV)-enabled mobile edge network for Cyber-Physical System (CPS), where UAV with fixed-wing or rotary-wing is dispatched to provide communication and mobile edge computing (MEC) services to ground terminals (GTs). To minimize the energy consumption so as to extend the endurance of the UAV, we intend to jointly optimize its 3D trajectory and the task-cache strategies among GTs to save the energies spent on flight propulsion and GT tasks. Such joint trajectory-task-cache problem is difficult to be optimally solved, as it is non-convex and involves multiple constraints. To tackle this problem, we reformulate the optimizing of task offloading and cache into two tractable linear program (LP) problems, and the optimizing of UAV trajectory into three convex Quadratically Constrained Quadratically Program (QCQP) problems on horizontal trajectory, vertical trajectory and flight time of the UAV respectively. Then a block coordinate descent algorithm is proposed to iteratively solve the formed sub-problems through a successive convex optimization (SCO) process. A high-quality sub-optimal solution to the joint problem then will be obtained, after the algorithm converging to a prescribed accuracy. The numerical results show the proposed solution significantly outperforms the baseline solution.

关键词： Steganography   mobile edge computing   GAN   foreground object segmentation  

摘要： Mobile edge computing provides low-latency service computing for the Internet of Things (IoT). Considering the computational cost of high-quality image steganography in practical mobile applications, we believe that mobile edge computing could provide real-time service computing for covert communications. As a mainstream approach to convert communication, image steganographic algorithms prefer to hide secret data in well-textured regions in order to reduce the possibility of being detected. Recently, the generative adversarial networks (GAN) has become one of the most popular architectures for image steganography. However, the GAN-based image steganographic algorithms directly conduct the secret data embedding on the entire cover images and do not sufficiently take the regional texture complexity into account, which will compromise the anti-detection ability. To address this issue, we propose a novel image steganographic algorithm on the generated foreground object region with rich textures. More specifically, the foreground object region is generated onto a given cover image by the GAN, and the secret data is embedded in the foreground object region simultaneously during the generation of the region. The experimental results show that the proposed method can resist steganalysis effectively without significant degradation of image quality and achieve real-time processing.