关键词： mobile edge computing   task offloading   security   unmanned aerial vehicle   compression  

摘要： Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining popularity and attracting considerable attention in different fields of our daily life. Nevertheless, most of these applications and services require more powerful computational resources and energy, and their limited battery capacity and processing power make it difficult to run them on a single device. Edge-Cloud Computing (ECC) is emerging as a new paradigm to cope with the challenges of these applications, which moves computing resources to the edge of the network and remote cloud, thereby alleviating the overhead through task offloading. Even though ECC offers substantial benefits for these devices, the limited bandwidth condition in the case of simultaneous offloading via the same channel with increasing data transmission of these applications has not been adequately addressed. Moreover, protecting the data through transmission remains a significant concern that still needs to be addressed. Therefore, in this paper, to bypass the limited bandwidth and address the potential security threats challenge, a new compression, security, and energy-aware task offloading framework is proposed for the ECC system environment. Specifically, we first introduce an efficient layer of compression to smartly reduce the transmission data over the channel. In addition, to address the security issue, a new layer of security based on an Advanced Encryption Standard (AES) cryptographic technique is presented to protect offloaded and sensitive data from different vulnerabilities. Subsequently, task offloading, data compression, and security are jointly formulated as a mixed integer problem whose objective is to reduce the overall energy of the system under latency constraints. Finally, simulation results reveal that our model is scalable and can cause a significant reduction in energy consumption (i.e., 19%, 18%, 21%, 14.5%, 13.1% and 12%) with respect to other benchmarks (i.e., local, edg

关键词： mobile edge computing   IoT   deep learning   cluster head   K-means   energy efficient algorithm  

摘要： In mobile edge computing (MEC), it is difficult to recognise an optimum solution that can perform in limited energy by selecting the best communication path and components. This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. We utilised a CNN to determine the best-transferring strategy and the most efficient partitioning of a specific task. The MKM method has more than one cluster head in each cluster to lead. It also reduces the number of reclustering cycles, which helps to overcome the energy consumption and delay during the reclustering process. The proposed model determines a training dataset by covering all the aspects of cost function calculation. This training dataset helps to train the model, which allows for efficient decision-making in optimum energy usage. In MEC, clusters have a dynamic nature and frequently change their location. Sometimes, this creates hurdles for the clusters to form a cluster head and, finally, abandons the cluster. The selected cluster heads must be recognised correctly and applied to maintain and supervise the clusters. The proposed pairing of the modified k-means method with a CNN fulfils this objective. The proposed method, existing weighted clustering algorithm (WCA), and agent-based secure enhanced performance approach (AB-SEP) are tested over the network dataset. The findings of our experiment demonstrate that the proposed hybrid model is promising in aspects of CD energy consumption, overhead, packet loss rate, packet delivery ratio, and throughput compared to existing approaches.

关键词： UAV   Mobile edge computing   User cooperation   Computation offloading   Trajectory design  

摘要： Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems can provide flexible compu-tation services to user equipments (UEs). On the one hand, the UAV can be flexibly deployed to provide computation services to UEs in remote areas or where intensive computing is required. On the other hand, the UAV can approach UEs conveniently to enhance the offloading performance. However, when UEs are widely distributed, the UEs will consume high energy to offload tasks to the UAV, or the UAV will need to consume significant energy to fly close to different UEs to ensure reliable computation offloading, which is unfriendly to the energy-constrained UAV. To tackle these issues, in this paper, we propose a cooperative task offloading scheme for the UAV-enabled MEC systems. Specifically, we consider a UAV-enabled MEC system consisting of a UAV serving as a MEC server for multiple near and far UEs, where each UE has a dividable computation task to be computed and can offload a part of the task to the UAV for computing. The task offloading of each far UE is proposed to be assisted by an associated near UE, where each far UE first sends its task to the associated near UE, and then the near UE offloads its own task and the task from the far UE to the UAV. An iterative algorithm based on the block coordinate descent method is proposed to optimize the UAV's trajectory, the computation and communication resources for minimizing the weighted sum energy consumption of the UEs and the UAV. Specifically, the UAV's trajectory is optimized based on the successive convex approximation method, and the computation and communication resources are optimized via the Lagrangian dual method. Simulation results are presented to verify the effectiveness of the proposed algorithm. It is shown that compared to the state-of-the-art algorithms in existing literature, the proposed algorithm achieves much lower energy consumption, especially when the UEs carry more task data.

关键词： Mobile edge computing   Server placement   Service placement   Delay   Combining optimization  

摘要： With the rapid increase of applications in 5G and Internet of Things, mobile edge computing (MEC) has been proposed to reduce the burden of central cloud and decrease the users request delay by deploying edge servers and edge services close to users. Due to resources constraint of edge servers and disadvantage of standalone placement optimization of edge servers or edge services, the discussion focusing on the combining optimization of server placement and service placement has engendered. At present, the placement combination is studied with strict assumption constrains, such as homogeneous service and server with unlimited resources, which are not suitable for the reality scenarios. This paper proposes Placement Combination between Heterogeneous Services and heterogeneous capacitated Servers (PCHSS). PCHSS aims to minimize the delay in computation and transmission as well as to balance resources in edge servers. Because the placement combination optimization is a NP-hard problem, we propose two solution algorithms named FHPC and IUPC. Both algorithms have a two-layer iterative optimization structure with different convergence time and result performances. FHPC can converge to a good result quickly, and IUPC can achieve better results with a relatively higher computational complexity. Then we prove that both algorithms can converge in polynomial time. Extensive simulations demonstrate the significant effectiveness of the placement combination, and our algorithms can reduce the user request delay by up to 51% compared with baseline algorithms.

关键词： Task analysis   Servers   Relay networks (telecommunication)   Resource management   Computational modeling   Delays   Costs   Caching   mobile edge computing   stable matching   task offloading   ultra-dense relay network  

摘要： In recent years, ultra-dense relay network (UDRN) has become a hot research topic. Mobile edge computing (MEC) can improve the performance of intelligent interactive applications in terms of time delay and energy consumption. In traditional MEC, MEC server in the base station (BS) is responsible for all the computing in the network. However, the MEC deployed in the BS may lead to huge computation burden on the BS and heavy traffic load through the network. Deploying MEC server in the relays of the network, which helps computing and caching for the users, will lower the burden of the BS, reduce the demand for traffic transmission through the network and improve the quality of service for users. This paper studies the problem of task caching, offloading and stable matching (SM) in UDRNs with MEC deployed in the relays, which is formulated as a multi-variable integer programming problem. To enable task caching and offloading, we propose an improved branch-and-bound algorithm and a low-complexity suboptimal algorithm to obtain the optimal and suboptimal schemes, respectively. Then, according to the principle of minimizing individual costs, the mutual preference lists between the users and relays are established. Based on the mutual preference lists, we propose an improved SM algorithm to obtain the user-relay SM results. Simulation results show that the proposed algorithms can bring considerable performance gains compared with conventional algorithms.

关键词： Computational offloading   Mobile edge computing   Collaborative edge computing   Multi-access edge computing   Task offloading  

摘要： Many enterprise companies migrate their services and applications to the cloud to benefit from cloud computing advantages. Meanwhile, the rapidly increasing number of connected devices with the massive amount of generated data that use cloud services leads to high workload, congestion, and delay bottleneck in the centralized cloud architecture. Consequently, Mobile Edge Computing (MEC) is introduced as a new paradigm to expand cloud capabilities near the end devices. In addition, new technologies such as the Internet of Things (IoT), Autonomous Vehicles (AV), 5G, and Augmented Reality (AR) bring new demands and opportunities that MEC can make possible. Offloading delay-sensitive and computationally intensive tasks to nearby MEC nodes is an effective way that still is the most common open problem in MEC. The offloading problem in MEC has been widely studied in areas such as Vehicular Edge Computing (VEC), IoT, Radio Access Networks (RAN), and 5G but independently. Due to the high diversity of research areas, targeted issues, and adopted algorithms and tech-niques, finding the right research path in task offloading is highly demanding. To fill this gap, a comprehensive survey is conducted using the mixed-method systematic literature review involving qualitative and quantitative data from the studied papers. For each journal paper, the detailed information of the work area, targeted issue, formulation technique, optimization approach, adopted algorithms, evaluation techniques, performance matrices, dataset, utilized tools, and framework are extracted and analyzed using manual and automatic coding. Major offloading-related issues in MEC are investigated, and the taxonomy of journal papers based on adopted approaches is presented. For further future exploration, we suggest the potential areas of research, the contri-bution of the algorithms and technique, and the research direction in MEC. This review will give a quick and overall view of MEC's latest issues and solution

关键词： Mobile edge computing  

摘要： In this paper, we consider a Mobile Edge Computing (MEC) system with a source transmitter (ST) and multiple relay nodes (RNs). ST has sufficient computing resources, but lacks communication ability, while the situation of RN is the opposite. To tackle this resource imbalance problem, we propose a cooperative computation and communication resources sharing strategy using contract theory under complete and incomplete information scenarios. The ST offers a contract combining computation time and relay power, and the RNs forward the computational results for ST in exchange for the usage of computation capacity. For complete information scenario, we derive the optimal contract to maximize the ST’s utility and we show the existence and uniqueness of optimal contract. We further explore the properties of a feasible contract for incomplete information scenario where the channel gain and relay cost are the private information of RNs and not known by ST. We analyze the necessary and sufficient conditions for a contract to be feasible and propose a sequence optimization algorithm to obtain the optimal contract. Finally, we show the feasibility and optimality of the contract through simulation results. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Mobile edge computing   intelligent reflecting surface   wireless power transfer   energy beamforming   multiple-user detection  

摘要： This paper proposes a multiple intelligent reflecting surfaces (IRSs) assisted wireless-powered mobile edge computing (MEC) system, where the IRSs are deployed to assist both the downlink wireless power transfer (WPT) from the multi-antenna hybrid access point (HAP) to the wireless devices (WDs) and the uplink computation offloading from the WDs to the MEC server. To further improve the system performance, the energy beamforming and multiple-user detection (MUD) technologies are exploited. We consider both partial and binary offloading schemes and formulate the sum computation rate (SCR) maximization problems for them, respectively. To tackle the non-convexity of each problem, we propose an efficient alternating optimization (AO) method. Specifically, the Lagrange duality method is used to optimize the energy beamforming vector and the MUD matrix at the HAP, and the CPU frequencies and transmit power of the WDs. Then, we optimize the discrete phase shifts via the successive convex appropriation (SCA) method, the rank-one equivalents, and rounding method. Finally, the optimal time scheduling can be obtained via the one-dimensional search method. In addition, we propose a greedy algorithm with low complexity to optimize the computing modes for the binary offloading scheme. Numerical results show that our proposed schemes outperform the benchmarks.

关键词： mobile edge computing   machine learning   resource allocation   reinforcement learning  

摘要： In the 5G era, the amount of network data has grown explosively. A large number of new computation-intensive applications have created demand for edge computing in mobile networks. Traditional optimization methods are difficult to adapt to the dynamic wireless network environment because they solve the problem online, which is not suitable in edge computing scenarios. Therefore, in order to obtain a mobile network with better performance, we propose a network frame with a resource allocation algorithm based on power consumption, delay and user cooperation. This algorithm can quickly realize the optimization of a network to improve performance. Specifically, compared with heuristic algorithms, such as particle swarm optimization, ant colony algorithm, etc., commonly used to solve such problems, the algorithm proposed in this paper can reduce some aspects of network performance (including delay and user energy consumption) by about 10% in a network dominated by downlink tasks. The performance of the algorithm under certain network conditions was demonstrated through simulations.

关键词： Task analysis   Servers   Energy consumption   Face recognition   Computational modeling   Receivers   Time factors   Mobile Edge computing   workflow management   energy consumption   computation offloading   directed acyclic graph  

摘要： With the fast development of mobile edge computing (MEC), there is an increasing demand for running complex applications on the edge. These complex applications can be represented as workflows where task dependencies are explicitly specified. To achieve better Quality of Service (QoS), computation offloading is widely used in the MEC environment. However, many existing computation offloading strategies only focus on independent computation tasks but overlook the task dependencies. Meanwhile, most of these strategies are based on search algorithms which are often time-consuming and hence not suitable for many delay-sensitive complex applications in MEC. Therefore, a highly efficient graph-based strategy was proposed in our recent work but it can only deal with simple workflow applications with linear (namely sequential) structure. For solving these problems, a novel graph-based strategy is proposed for workflow applications in MEC. Specifically, this strategy can deal with complex workflow applications with nonlinear (viz. parallel, selective and iterative) structures. Meanwhile, the offloading decision plan with the lowest energy consumption of the end-device under deadline constraint can be found by using the graph-based partition technique. We have comprehensively evaluated our strategy on FogWorkflowSim platform for complex workflow applications. Extensive numerical results demonstrate that the end device's energy consumption can be effectively reduced by 7.81% and 9.51% compared with PSO and GA by the proposed strategy. Meanwhile, the strategy running time is 1% and 0.2% of PSO and GA, respectively.