关键词： Internet of Things   edge servers   multiserver edge networks   energy-latency tradeoff   subtask offloading   mobile edge computing   resource-limited devices   scheduling   logical dependency   resource allocation   dependency guarantees   IoT tasks   edge computing   energy consumption   telecommunication power management   mobile devices   key promising method   Internet   IoT applications   computer network security   cloud computing   mobile computing   network   dependent graphs   offloading scheme   resource-constrained devices   dependent task offloading   task scheduling  

摘要： With the rapid development of Internet-of-Things (IoT) and mobile devices, the IoT applications become more computation-intensive and latency-sensitive, which bring severe challenges to the resource-limited devices. Mobile Edge Computing has served as a key promising method to enhance the network's computing capability by enabling resource-constrained devices to offload tasks to the edge servers. A major challenge, which has been overlooked by most existing works on task offloading, is the dependencies among tasks and subtasks. In this paper, the subtask offloading with logical dependency for IoT applications is focused on. Specifically, subtask dependent graphs are employed to explore the dependency of subtasks and consider the priority of task scheduling. Further, an offloading scheme is put forward for minimizing both task latency and energy consumption of the device with dependency guarantees for all IoT tasks in multi-server edge networks. Finaly, the simulation results demonstrate that the overall reduction rate is around 14% and relatively stable can effectively reduce task latency in multi-server edge networks.

关键词： Servers   Visual analytics   Delays   Optimization   Streaming media   Bayes methods   Wireless communication   Mobile edge computing   video analytics   offloading   online learning   Bayesian optimization  

摘要： Video analytics demand intensive computation resources, which means long processing delay when running on mobile devices. Although offloading computation to the cloud can partially solve the problem, transferring videos to the cloud introduces high transmission delay. With mobile edge computing, computation can be offloaded to the nearby edge servers to reduce the delay. However, the computation resources of the edge servers are usually limited and highly dynamic, and then server selection should be adaptive in order to improve the performance of video analytics. Also, frame resolution should be selected to achieve a better tradeoff between accuracy and frame processing rate. In this paper, we study the server resource-aware offloading problem for video analytics, where the goal is to maximize the utility which is a weighted function of accuracy and frame processing rate. The major challenge to solve this problem is the lack of server and network knowledge and the dynamic system environment. To overcome these challenges, we formulate the problem as a contextual Multi-armed Bandit problem, and propose a Bayesian Optimization based online learning algorithm to gradually learn the server status and the optimal solution, and make it adaptable for time-varying environments. Both theoretical analysis and evaluation results demonstrate the superior performance of our proposed algorithm.

关键词： total consumed task communication time   MEC   mobile edge computing   DQN-SN   moving direction-based adaptive task migration   multiple edge servers   MDP   reinforcement learning   resource allocation   training process   deep learning (artificial intelligence)   edge computing   Markov processes   credit-based scheme   telecommunication scheduling   resource-limited users   mobility process   Markov decision process   greedy algorithms   trusted edge environment   mobile computing   low-latency services   vehicle network   deep Q network   moving process   telecommunication computing  

摘要： Edge computing is expected to be a promising paradigm to provide low-latency services. Tasks from resource-limited users can be offloaded to edge servers for efficient execution within a limit time. This innovative technique has attracted widespread attention. Task migration is one of the important problems in mobile edge computing (MEC). Taking vehicle network as an example, during the moving process, a vehicle passes through multiple edge servers and decisions about where to migrate the task need to be made. The moving direction should be emphasized since it directly determines a vehicle's trajectory. Nevertheless, few existing works take the moving direction into consideration. In this paper, task migration issue during the vehicle's mobility process is investigated and the moving direction is specifically considered. The direction helps exclude meaningless selections. The moving process can be formulated as a Markov decision process (MDP) and effort is made to design an adaptive algorithm with direction consideration, aiming at minimizing the total communication time while satisfying the deadline of each task. Based on deep Q network (DQN), we devise a Soft update and parameter Noise applied algorithm DQN-SN, trying to enlarge action exploration space and stabilize the target network's parameter placement in training process. Besides, with the goal of building credible MEC, a credit-based scheme is also introduced to establish a trusted edge environment. Extensive experiments are conducted to evaluate the performance of our proposed algorithm. Compared to Greedy algorithm and DQN, the total consumed task communication time of DQN-SN shows 10-20% reduction. Furthermore, the algorithms with the direction factor always outperform the algorithms without direction consideration.

关键词： mobile edge computing   task offloading   deep Q-learning   optimization  

摘要： Even though small portable devices are becoming increasingly more powerful in terms of processing power and power efficiency, there are still workloads that require more computational capacity than these devices offer. Examples of such workloads are real-time sensory input processing, video game streaming, and workloads relating to IoT devices. Some of these workloads such as virtual reality, however, require very small latency;hence, the workload cannot be offloaded to a cloud service. To tackle this issue, edge devices, which are closer to the user, are used instead of cloud servers. In this study, we explore the problem of assigning tasks from mobile devices to edge devices in order to minimize the task response latency and the power consumption of mobile devices, as they have limited power capacity. A deep Q-learning model is used to handle the task offloading decision process in mobile and edge devices. This study has two main contributions. Firstly, training a deep Q-learning model in mobile devices is a computational burden for a mobile device;hence, a solution is proposed to move the computation to the connected edge devices. Secondly, a routing protocol is proposed to deliver task results to mobile devices when a mobile device connects to a new edge device and therefore is no longer connected to the edge device to which previous tasks were offloaded.

关键词： Task analysis   Stochastic processes   Energy consumption   Resource management   Three-dimensional displays   Autonomous aerial vehicles   Servers   Communication and computation resource allocation   mobile edge computing   stochastic optimization   3D trajectory planning   unmanned aerial vehicle  

摘要： In this paper, we consider a mobile edge computing (MEC) network where multiple cellular-connected unmanned aerial vehicles (UAVs) can offload their computation tasks to multiple ground base stations (GBSs). In practice, the UAVs are generally unable to master stochastic information of task arrival and channel changes in advance, which may cause a severe issue in terms of energy consumption. Therefore, we formulate a stochastic optimization problem with the goal of minimizing the average weighted sum energy consumption, by jointly optimizing UAV-GBS associations, communication and computation resource allocation, and three-dimensional (3D) UAV trajectories, during which a velocity-triggered penalty term (VTPT) is designed to suppress a large amount of the energy consumption of the UAVs. To handle the stochastic problem, we propose an online resource allocation and trajectory optimization algorithm with outer and inner structures. The outer structure transforms the original problem to a deterministic one by applying the Lyapunov-based optimization framework. The inner structure solves the obtained deterministic problem via the Lagrange duality method and the successive convex approximation technique, based on the block coordinate descent framework. Numerical results demonstrate that: 1) VTPT dramatically decreases the UAVs' energy consumption, and 2) the proposed algorithm not only reduces the energy consumption but also ensures the computation queue stability compared with other benchmark schemes.

关键词： Mobile Edge Computing   UAV   formation control   leader election   path planning   obstacle avoidance   artificial intelligence   multi-agent systems  

摘要： Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader-follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework.

关键词： Internet of things devices   Mobile edge computing   Energy-saving service management   Long short-term memory model   Reinforcement learning   Dynamic power management  

摘要： The purpose is to solve the problems of high transmission rate and low delay in the deployment of mobile edge computing network, ensure the security and effectiveness of the Internet of things (IoT), and save resources. Dynamic power management is adopted to control the working state transition of Edge Data Center (EDC) servers. A load prediction model based on long-short term memory (LSTM) is creatively proposed. The innovation of the model is to shut down the server in idle state or low utilization in EDC, consider user mobility and EDC location information, learn the global optimal dynamic timeout threshold strategy and N-policy through trial and error reinforcement learning method, reasonably control the working state switching of the server, and realize load prediction and analysis. The results show that the performance of AdaGrad optimization solver is the best when the feature dimension is 3, the number of LSTM network layers is 6, the time series length is 30-45, the batch size is 128, the training time is 788 s, the number of units is 250, and the number of times is 350. Compared with the traditional methods, the proposed load prediction model and power management mechanism improve the prediction accuracy by 4.21%. Compared with autoregressive integrated moving average (ARIMA) load prediction, the dynamic power management method of LSTM load prediction can reduce energy consumption by 12.5% and realize the balance between EDC system performance and energy consumption. The system can effectively meet the requirements of multi-access edge computing (MEC) for low delay, high bandwidth and high reliability, reduce unnecessary energy consumption and waste, and reduce the cost of MEC service providers in actual operation. This exploration has important reference value for promoting the energy-saving development of Internet-related industries.

关键词： Device-to-device communication   Transceivers   Spread spectrum communication   Servers   Wireless networks   Wireless sensor networks   Time-frequency analysis   Mobile edge computing   wireless MapReduce   multi-level over-the-air (OTA) aggregation   multihop D2D communications   transceiver optimization  

摘要： In this paper, we consider a wireless multihop device-to-device (D2D) based mobile edge computing (MEC) system, where the destination wireless device (WD) is scheduled to compute nomographic functions. Under the MapReduce framework and motivated by reducing communication resource overhead, we propose a new multi-level over-the-air (OTA) aggregation scheme for the destination WD to collect the individual partially aggregated intermediate values (IVAs) for reduction from multiple source WDs in the data shuffling phase. For OTA aggregation per level, the source WDs employ a truncated channel-inverse structure multiplied by their individual transmit coefficients in transmission over the same time-frequency resource blocks, and the destination WD finally uses a receive filtering factor to construct the aggregated IVA. Under this setup, we develop a unified transceiver design framework that minimizes the mean squared error (MSE) of the aggregated IVA at the destination WD subject to the source WDs' individual power constraints, by jointly optimizing the individual transmit coefficients of the source WDs and the receive filtering factor of the destination WD. The formulated power-constrained MSE minimization problem is non-convex. First, based on the primal decomposition method, we derive the closed-form solution under the special case of a common transmit coefficient. This shows that the common transmit coefficient of the source WDs is determined by the minimal transmit power budget among them. Next, for the general case, we transform the original problem into a quadratic fractional programming problem, and then develop a low-complexity algorithm to obtain the (near-) optimal solution by leveraging Dinkelbach's algorithm along with the Gaussian randomization method. Numerical results are provided to demonstrate the significant performance gains achieved by the proposed multi-level OTA aggregation scheme over various existing schemes.

关键词： Survey   Distributed ledger technologies   Blockchain   Vehicle   Mobile edge computing  

摘要： Blockchain-based systems, coined by distributed ledger technologies (DLTs), have rapidly received tremendous interest from academia, industries, and governments. Recent literature has revealed many research and developments on applying DLTs to the Internet of things (IoT), cloud-edge computing. In this survey, we conduct a comprehensive survey of the newly appeared concepts, theories, platforms, and DLTs-facilitated applications of vehicular networks and mobile edge computing (MEC). We also review the selections of the available DLTs related platforms and tools. Future research directions and issues are discussed, including security, privacy, scalability issues, and multiple applications in various domains.

关键词： battery level aware   energy efficiency   heterogeneous network   mobile edge computing   task scheduling  

摘要： This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem;this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.