关键词： Array signal processing   Resource management   Optimization   NOMA   Three-dimensional displays   Energy harvesting   Wireless communication   Hybrid beamforming   mobile edge computing   non-orthogonal multiple access   unmanned aerial vehicle   wireless power transfer  

摘要： Beamforming and non-orthogonal multiple access (NOMA) serve as two potential solutions for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is equipped with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. Our aim is to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The resultant optimization problem is non-convex involving joint optimization of the UAV's 3D placement and hybrid beamforming matrices as well as computation resource allocation in both partial and binary offloading patterns, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we develop an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. Two resource allocation algorithms for partial and binary offloading patterns are thereby proposed. Simulation results verify that our designed algorithms achieve a significant computation performance enhancement as compared to the benchmark schemes.

关键词： mobile edge computing   joint resource allocation   hierarchical game   resource sharing   QoS  

摘要： In this paper, we propose a hierarchical Stackelberg game based resource allocation algorithm (HGRAA) to jointly allocate the wireless and computational resources of a mobile edge computing (MEC) system. The proposed HGRAA is composed of two levels: the lower-level evolutionary game (LEG) minimizes the cost of mobile terminals (MTs), and the upper-level exact potential game (UEPG) maximizes the utility of MEC servers. At the lower-level, the MTs are divided into delay-sensitive MTs (DSMTs) and non-delay-sensitive MTs (NDSMTs) according to their different quality of service (QoS) requirements. The competition among DSMTs and NDSMTs in different service areas to share the limited available wireless and computational resources is formulated as a dynamic evolutionary game. The dynamic replicator is applied to obtain the evolutionary equilibrium so as to minimize the costs imposed on MTs. At the upper level, the exact potential game is formulated to solve the resource sharing problem among MEC servers and the resource sharing problem is transferred to nonlinear complementarity. The existence of Nash equilibrium (NE) is proved and is obtained through the Karush-Kuhn-Tucker (KKT) condition. Simulations illustrate that substantial performance improvements such as average utility and the resource utilization of MEC servers can be achieved by applying the proposed HGRAA. Moreover, the cost of MTs is significantly lower than other existing algorithms with the increasing size of input data, and the QoS requirements of different kinds of MTs are well guaranteed in terms of average delay and transmission data rate.

关键词： Unmanned aerial vehicles   Games   Task analysis   Energy consumption   Interference   Micromechanical devices   Edge computing   Unmanned aerial vehicle   mobile edge computing   hierarchical framework   Stackelberg game   exact potential game  

摘要： In this letter, we investigate the joint optimization of computation offloading, channel allocation and position deployment problem for coalition-based unmanned aerial vehicle (UAV) mobile edge computing networks. To characterize the hierarchical offloading feature in coalition-based UAV network, we propose the hierarchical framework and formulate a discrete multi-leader multi-follower energy minimization Stackelberg game. In addition, we prove that the multi-leader subgame and multi-follower subgame are exact potential games with Nash equilibrium. Then the existence of Stackelberg equilibrium (SE) is guaranteed. The spatial adaptive play-best response based hierarchical iterative learning (SAP-BRHIL) algorithm is proposed to achieve the SE. The simulation results show that the proposed SAP-BRHIL algorithm can achieve the lowest network energy consumption compared with some existing approaches.

关键词： Edge intelligence   mobile edge computing   machine learning   resource-constrained   IoT   low-power   deep learning   embedded   distributed computing  

摘要： Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the loT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

关键词： Energy efficiency   Data gathering   IoT networks   Mobile edge computing  

摘要： The Internet of Things (IoT) networks have been adopted ubiquitously to support domain applications. Specially, social robots have become an important part of IoT networks as smart devices and are widely used to support versatile domain applications. In this setting, gathering sensory data from social-aware mobile robots in an energy-efficient manner is of importance for prolonging the network lifetime and promoting proper decision-making. Considering the large-scale and spatial-temporal evolutional characteristic of IoT networks, social robots roam over time to deal with tasks, especially when considering fact that it may hardly be predicted for the regions and time durations that certain anomalies may occur. Therefore, this paper proposes to adopt mobile edge computing to support sensory data gathering. Edge nodes in edge networks gather sensory data from their subordinating social robots in a periodic manner. We design an edge network division method by constructing an improved Sort-Tile-Recursive (STR) tree, which can cluster the edge nodes and decrease unnecessary energy consumption. Experimental results show that our technique is more efficient than traditional ones in decreasing energy consumption.

关键词： Deep Q learning   Mobile edge computing   SDN   Edge caching   Service migration  

摘要： In the mobile edge computing environment, edge servers, edge base stations and other intermediate servers have limited computing, storage resources and limited coverage, resulting in increased content access delays and reduced user experience quality. Content caching methods based on file value and corresponding algorithms are proposed. This method uses Markov chains to predict the mobile user's trajectory in the next time slot, and active caching and cache replacement algorithms are designed to minimize content access latency. In addition, because edge servers are connected to many different access points or base stations, whether or not to migrate the ongoing edge services and where to migrate the services are the key issues to consider when any user moves outside the service area of the related edge servers. To reduce the overhead of service migration due to the mobility of users and the complex topological relationship between edge servers, a de-centralized service migration algorithm is proposed, which integrates the analysis and selection of the optimal edge base station and service placement decisions with the objective of minimizing the overhead of service migration. The experimental results show that the proposed edge caching and service migration methods reduce content access latency and service migration overhead.

关键词： mobile edge computing   NOMA   full offloading   partial offloading  

摘要： The development of mobile edge computing (MEC) is expected to offer better performance in mobile communications than the current cloud computing architecture. MEC involves offering the closest access to the data source or physical mobile network environment. The network services are able to respond faster, thus satisfying the demands of the mobile network industry when deploying various potential business applications in real-time. Since the harvested mobile data are transferred to the edge server to make calculations, data transfers and faults in the mobile network can be swiftly pinpointed and removed accurately. Nevertheless, there are still problems in the practical application of the systems, specifically in reducing delays and lessening energy consumption. Because of non-orthogonal multiple access (NOMA) superior spectrum efficiencies, it is best to combine NOMA with MEC for simultaneous support of multiple access for end users, thus reducing transmission latencies and lowering energy consumption. Combining MEC and NOMA would offer many advantages, including superior energy savings, reductions in latency, massive connectivity, and the potential of combining with additional transmission technologies, such as millimetre-wave (mmWave) and M-MIMO. In this paper, designing wireless resource allocation is crucial for an economically viable low-latency wireless network, which can be realised using the Karush-Kuhn-Tucker (KKT) approach to obtain the optimal solution for partial and full offloading network traffic scenarios to minimize the total latency of the MEC network. The convergence and performance for orthogonal multiple access (OMA), pure-NOMA (P-NOMA), and hybrid-NOMA (H-NOMA) are also compared under different network traffic offloading scenarios. The significant results from this study showed the convergence of the optimal resource allocation in the case of full and partial offloading. The results demonstrated that the P-NOMA reduces the total offloading dela

关键词： Task analysis   Unmanned aerial vehicles   Servers   Resource management   Decision making   Optimization   Edge computing   Mobile edge computing   5G   unmanned aerial vehicle   futures-based resource trading   power optimization  

摘要： Mobile edge computing (MEC) has emerged as one of the key technical aspects of the fifth-generation (5G) networks. The integration of MEC with resource-constrained unmanned aerial vehicles (UAVs) greatly enables flexible resource provisioning for supporting dynamic and computation-intensive UAV applications. Existing resource trading could facilitate this paradigm with proper incentives, which, however, may often incur unexpected negotiation latency and energy consumption, trading failures and unfair pricing, due to the unpredictable nature of the resource trading process. Motivated by these challenges, an efficient futures-enabled resource trading mechanism for edge computing-assisted UAV network is proposed, where a mutually beneficial and risk-tolerable forward contract is devised to promote resource trading between an MEC server (seller) and a UAV (buyer) with multiple tasks. Two key problems i.e. futures contract design before trading, and transmission power optimization during trading are studied. By analyzing historical statistics associated with future resource supply, demand, and air-to-ground communication quality, the contract design is formulated as a multi-objective optimization problem aiming to maximize both the seller's and the buyer's expected utilities, while estimating their acceptable risk tolerance. Accordingly, we propose an efficient bilateral negotiation scheme to help players reach a trading consensus on the amount of resources and the relevant price. For the power optimization problem, we develop a practical algorithm that enables the buyer to determine its optimal transmission power via convex optimization techniques. Comprehensive simulations demonstrate that the proposed mechanism offers mutually beneficial utilities to players, while achieving commendable performance on trading failures and fairness, negotiation latency and cost, comparing with baseline methods.

关键词： Trajectory   Monitoring   Heuristic algorithms   Throughput   Edge computing   COVID-19   Approximation algorithms   Dynamic scheduling   Unmanned aerial vehicles   Task analysis   UAV   mobile edge computing   trajectory design   task scheduling   5G communications  

摘要： Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

关键词： Mobile edge computing   Task offloading   Average delay   Energy consumption level   Cost function   Lagrangian function   Karush-Kuhn-Tucker condition  

摘要： With the development for the technology of mobile edge computing (MEC) and the grave situation for the shortage of global energy, the problem of computation offloading in a cloud computing framework is getting more attention by network managers. In order to improve the experience quality of users and increase the energy efficiency of the system, we focus on the issue of task offloading strategy in MEC system. In this paper, we propose a task offloading strategy in the MEC system with a heterogeneous edge. By considering the execution and transmission of tasks under the task offloading strategy, we present an architecture for the MEC system. We establish a system model composed of M/M/1, M/M/c and M/M/infinity queues to capture the execution process of tasks in local mobile device (MD), MEC server and remote cloud servers, respectively. Moreover, by trading off the average delay of tasks, the energy consumption level of the MD and the offloading expend of the system, we construct a cost function for serving one task and formulate a joint optimization problem for the task offloading strategy accordingly. Furthermore, under the constraints of steady state and proportion scope, we use the Lagrangian function and the corresponding Karush-Kuhn-Tucker (KKT) condition to obtain the optimal task offloading strategy with the minimum system cost. Finally, we carry out numerical experiments on the MEC system to investigate the influence of system parameters on the task offloading strategy and to obtain the optimal results. The experiment results show that the task offloading strategy proposed in this paper can balance the average delay, the energy consumption level and the offloading expend with the optimal allocation ratio.