Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) can provide mobile users (MU) with additional computing services and a wide range of connectivity. This paper investigates the joint optimization strategy of task offloading and resource allocation for UAV-assisted MEC systems in complex scenarios with the goal of reducing the total system cost, consisting of task execution latency and energy consumption. We adopt a game theoretic approach to model the interaction process between the MEC server and the MU Stackelberg bilayer game model. Then, the original problem with complex multi-constraints is transformed into a duality problem using the Lagrangian duality method. Furthermore, we prove that the modeled Stackelberg bilayer game has a unique Nash equilibrium solution. In order to obtain an approximate optimal solution to the proposed problem, we propose a two-stage alternating iteration (TASR) algorithm based on the subgradient method and the marginal revenue optimization method. We evaluate the effective performance of the proposed algorithm through detailed simulation experiments. The simulation results show that the proposed algorithm is superior and robust compared to other benchmark methods and can effectively reduce the task execution latency and total system cost in different scenarios.

Multi-access edge computing (MEC) is an emerging technology of 5G and beyond mobile networks which deploys computation services at edge servers for reducing service delay. However, edge servers may have not enough computation capabilities to satisfy the delay requirement of services. Thus, heavy computation tasks need to be offloaded to other MEC servers. In this paper, we propose an offloading solution, called optimal delay offloading (ODO) solution, that can guarantee service delay requirements. Specificially, this method exploits an estimation of queuing delay among MEC servers to find a proper offloading server with the lowest service delay to offload the computation task. Simulation results have proved that the proposed ODO method outperforms the conventional methods, i.e., the non-offloading and the energy-efficient offloading [10] methods (up to 1.6 times) in terms of guaranteeing the service delay under a threshold.

With the increase of the number of Mobile User Equipments (MUEs), numerous tasks that with high requirements of resources are generated. However, the MUEs have limited computational resources, computing power and storage space. In this paper, a joint coverage constrained task offloading and resource allocation method based on deep reinforcement learning is proposed. The aim is offloading the tasks that cannot be processed locally to the edge servers to alleviate the conflict between the resource constraints of MUEs and the high performance task processing. The studied problem considers the dynamic variability and complexity of the system model, coverage, offloading decisions, communication relationships and resource constraints. An entropy weight method is used to optimize the resource allocation process and balance the energy consumption and execution time. The results of the study show that the number of tasks and MUEs affects the execution time and energy consumption of the task offloading and resource allocation processes in the interest of the service provider, and enhances the user experience.

With maturation of 5G technology in recent years, multimedia services such as live video streaming and online games on the Internet have flourished. These multimedia services frequently require low latency, which pose a significant challenge to compute the high latency requirements multimedia tasks. Mobile edge computing (MEC), is considered a key technology solution to address the above challenges. It offloads computation-intensive tasks to edge servers by sinking mobile nodes, which reduces task execution latency and relieves computing pressure on multimedia devices. In order to use MEC paradigm reasonably and efficiently, resource allocation has become a new challenge. In this paper, we focus on the multimedia tasks which need to be uploaded and processed in the network. We set the optimization problem with the goal of minimizing the latency and energy consumption required to perform tasks in multimedia devices. To solve the complex and non-convex problem, we formulate the optimization problem as a distributed deep reinforcement learning (DRL) problem and propose a federated Dueling deep Q-network (DDQN) based multimedia task offloading and resource allocation algorithm (FDRL-DDQN). In the algorithm, DRL is trained on the local device, while federated learning (FL) is responsible for aggregating and updating the parameters from the trained local models. Further, in order to solve the not identically and independently distributed (non-IID) data problem of multimedia devices, we develop a method for selecting participating federated devices. The simulation results show that the FDRL-DDQN algorithm can reduce the total cost by 31.3% compared to the DQN algorithm when the task data is 1000 kbit, and the maximum reduction can be 35.3% compared to the traditional baseline algorithm.

With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on to which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues such as different applications' deadlines, monetary cost and energy consumption, how the MU decides whether to offload their traffic to a complementary wireless LAN is an important issue. Previous studies assume the MU's mobility pattern is known in advance, which is not always true. In this paper, we study the MU's policy to minimize his monetary cost and energy consumption without known MU mobility pattern. We propose to use a kind of reinforcement learning technique called deep Q-network (DQN) for MU to learn the optimal offloading policy from past experiences. In the proposed DQN based offloading algorithm, MU's mobility pattern is no longer needed. Furthermore, MU's state of remaining data is directly fed into the convolution neural network in DQN without discretization. Therefore, not only does the discretization error present in previous work disappear, but also it makes the proposed algorithm has the ability to generalize the past experiences, which is especially effective when the number of states is large. Extensive simulations are conducted to validate our proposed offloading algorithms.

With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues, MUs should be able to decide whether to offload their traffic to a complementary wireless LAN. Our previous work studied single-flow wireless LAN offloading from a MU's perspective by considering delay-tolerance of traffic, monetary cost and energy consumption. In this paper, we study the multi-flow mobile data offloading problem from a MU's perspective in which a MU has multiple applications to download data simultaneously from remote servers, and different applications' data have different deadlines. We formulate the wireless LAN offloading problem as a finite-horizon discrete-time Markov decision process (MDP) and establish an optimal policy by a dynamic programming based algorithm. Since the time complexity of the dynamic programming based offloading algorithm is still high, we propose a low time complexity heuristic offloading algorithm with performance sacrifice. Extensive simulations are conducted to validate our proposed offloading algorithms.

In this letter, we investigate the computation offloading problem in cloud based multi-robot systems, in which user weights, communication interference and cloud resource limitation are jointly considered. To minimize the system cost, two offloading selection and resource allocation algorithms are proposed. Numerical results show that the proposed algorithms both can greatly reduce the overall system cost, and the greedy selection based algorithm even achieves near-optimal performance.

Wireless caching is one of the promising technologies to mitigate the traffic burden of cellular networks and the large cost of deploying a higher volume of wired backhaul by introducing caching storage. In the manner of “cutting” wired equipments, all types of vehicles can be readily leveraged as serving access points with caching storage, where their moving nature should be taken into account to improve latency and data throughput. In this paper, we consider a mobility-aware vehicular caching which has a role in offloading delay-tolerable contents from cellular networks. We first clarify the influence of mobility in cellular caching networks, then set the mobility-aware optimization problem of vehicular caching to carry on delay-tolerable contents. Trace-driven numerical results based on rural and urban topographies show that, in presence of individual demand for delay-tolerable contents, the proposed vehicular caching scheme enhances the quality-of-service (QoS) (maximally twofold) relying on the contents delivery being centrally or distributedly controlled.

In this letter, for offloading traffic to Wireless Local Area Network (WLAN) with transport layer mobility where WLAN service is intermittently available, we propose a novel scheme to freeze and melt the timeout handling procedure of SCTP. Simulation results show that the proposed scheme significantly improves the performance in terms of file transfer completion time.