关键词： Energy consumption   Satellites   Costs   Simulation   Precoding   Transforms   Resource management   Lyapunov optimization   mobile edge computing   MIMO   resource allocation   dense satellite  

摘要： A dense satellite-terrestrial integrated mobile-edge computing network (SATIMECN) architecture is developed to meet the computing demands for next generation networks. We formulate an average weighted sum energy consumption minimization problem by jointly considering task ratio allocation of computing or offloading at local and the gateway (GW), ground user terminal (GUT)-satellite association relation, GUT multiple-input and multiple-output (MIMO) precoding, and computation resource allocation at local and the GW. Due to the stochastic property of the optimization problem, we adopt Lyapunov optimization theory to transform it into a deterministic one. Then, we decompose the optimization problem into four subproblems and solve each one iteratively. Specifically, task ratio allocation of computing or offloading at local and the GW is obtained in a closed-form expression using the delay constraint. Then, the binary GUT-satellite association subproblem is solved by the weighted minimum mean-squared error and quadratic transform based fractional programming (QTFP) methods. Moreover, the MIMO precoding subproblem is solved by QTFP and interior point methods. Finally, the computation resource allocation subproblem for local and edge computing is derived in closed-form expressions. Simulation results demonstrate that the tradeoff between the average weighted sum energy consumption and the average queue length can be realized by adjusting the Lyapunov control parameter. Moreover, the proposed MIMO communication and frequency reuse schemes for dense satellite network can realize efficient computation offloading with relative low cost.

关键词： Task analysis   NOMA   Roads   Propagation losses   Decoding   Servers   Probability density function   Mobile edge computing   non-orthogonal multiple access   vehicular networks   task offloading latency   speed  

摘要： In vehicular networks, task offloading performance for mobile edge computing (MEC) is highly coupled with the mobility of vehicle terminal (VT), as the VT speed and arrival rate determine the number of VTs and their sojourn time on each road segment. Generally, the communication and computation resources of the MEC server are limited, and thus more VTs leads to less resources for each VT. In this paper, we develop an analytical non-orthogonal multiple access (NOMA) assisted MEC model to characterize VT mobility and analyze the impact of VT mobility on task offloading performance. In this model, each VT with random speed starts its offloading at any time and hopefully completes the task offloading before leaving the current road segment. The expressions of average latency and task loss probability are derived and validated by simulation results. Furthermore, the impact of VT mobility, especially the average VT speed, on latency performances is analyzed. It is also shown that task loss probability is mainly affected by VT arrival rate, and is approximately independent of average VT speed.

关键词： Robots   Task analysis   Servers   Resource management   Delays   Robot kinematics   Processor scheduling   Robot team   multi-robot system   mobile edge computing   interdependent computing   user association   resource allocation   multi-agent deep reinforcement learning  

摘要： A group of robots can be assigned with different roles to collaboratively conduct interdependent tasks. The robots form a multi-robot system (MRS), where one robot's decision or action relies on the others'. This paper addresses the sequential decision problem of user association and resource allocation in a mobile edge computing (MEC)-enabled, wirelessly-connected MRS to maximize the time-averaged completion rate of interdependent computing tasks. The problem is challenging due to the partial observability of the network environment, and the delicate delay requirements of interdependent computing tasks. A new decentralized partially observable Markov decision process (Dec-POMDP) problem is reformulated, where edge servers act as intelligent agents and can make decentralized decisions about user association and resource management with their local information of the network state. By leveraging the multi-agent deep deterministic policy gradient (MADDPG) theory, a new cooperative multi-agent deep reinforcement learning (MADRL) model is developed to enable interdependent computing. Simulations show the merits of our approach in terms of task completion rate compared to existing techniques.

关键词： Computation offloading   Dynamic multi-objective optimization   Mobile edge computing   Multi-objective evolutionary algorithm   (MOEA)  

摘要： For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, terminal equipment is dynamic, and their number, mobility, and continuous changes in wireless channels will affect the balance between the mentioned energy consumption and delay. Different from available works, we model the COP in MEC as a dynamic multi-objective problem (DMOP) in this paper, and propose an improved dynamic multi-objective evolutionary optimization based on decomposition (DMOEA/D) to solve it, namely DMOEA/D-COPMEC. In the proposed algorithm, the environmental change is detected by a fixed detector, and whether the current change is similar to the historical change is determined. If so, the difference prediction is used to re-locate the population individual in the new MEC environment, otherwise, the memory -based strategy is used to response environmental change. In MOEA/D, an adaptive weight adjustment strategy based on chain segmentation (CS) is adopted to generate a set of uniformly distributed weight vectors. The simulation results show that the proposed algorithm can better balance the application delay and the terminal energy consumption if there is environment change. The solution set is closer to reality and better than the related algorithms.

关键词： Task analysis   Energy consumption   Delays   Computational modeling   Trajectory   Servers   Resource management   UAV communication   mobile edge computing   reinforcement learning   secure transmission  

摘要： UAV communication has received widespread attention in MEC systems due to its high flexibility and line-of-sight transmission. Users can reduce their local computing pressures and computation delay by offloading tasks to the UAV as an edge server. However, the coverage capability of a single UAV is very limited. Moreover, the data offloaded to the UAV will be easily eavesdropped. Thus, in this paper, we propose two secure transmission methods for multi-UAV-assisted mobile edge computing based on the single-agent and multi-agent reinforcement learning, respectively. In the proposed methods, we first utilize the spiral placement algorithm to optimize the deployment of UAVs, which covers all users with the minimum number of UAVs. Then, to reduce the information eavesdropping by a flying eavesdropper, we utilize the reinforcement learning to optimize the secure offloading to maximize the system utility by considering different types of users' tasks with diverse preferences for residual energy of computing equipment and processing delay. Simulation results indicate that compared with the single-agent method and the benchmark, the multi-agent method can optimize the offloading in a better manner and achieve larger system utility.

关键词： Tactile Internet   Cloud   5G   Mobile edge computing   Latency  

摘要： Internet of Things (IoT) is one of the promising technologies, announced as one of the primary use cases of the fifth-generation cellular systems (5G). It has many applications that cover many fields, moving from indoor applications, e.g., smart homes, smart metering, and healthcare applications, to outdoor applications, including smart agriculture, smart city, and surveillance applications. This produces massive heterogeneous traffic that loads the IoT network and other integrated communication networks, e.g., 5G, which represents a significant challenge in designing IoT networks;especially, with dense deployment scenarios. To this end, this work considers developing a novel artificial intelligence (AI)-based framework for predicting traffic over IoT networks with dense deployment. This facilitates traffic management and avoids network congestion. The developed AI algorithm is a deep learning model based on the convolutional neural network, which is a lightweight algorithm to be implemented by a distributed edge computing node, e.g., a fog node, with limited computing capabilities. The considered IoT model deploys distributed edge computing to enable dense deployment, increase network availability, reliability, and energy efficiency, and reduce communication latency. The developed framework has been evaluated, and the results are introduced to validate the proposed prediction model.

关键词： Task analysis   Servers   Resource management   Device-to-device communication   Wireless communication   Mobile handsets   Energy consumption   Computation offloading   deep reinforcement learning   Index Terms   device-to-device   mobile edge computing   resource allocation  

摘要： Computation offloading via device-to-device communications can improve the performance of mobile edge computing by exploiting the computing resources of user devices. However, most proposed optimization-based computation offloading schemes lack self-adaptive abilities in dynamic environments due to time-varying wireless environment, continuous-discrete mixed actions, and coordination among devices. The conventional reinforcement learning based approaches are not effective for solving an optimal sequential decision problem with continuous-discrete mixed actions. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) framework to solve the joint computation offloading and resource allocation problem. The proposed HDRL framework has a hierarchical actor-critic architecture with a meta critic, multiple basic critics and actors. Specifically, a combination of deep Q-network (DQN) and deep deterministic policy gradient (DDPG) is exploited to cope with the continuous-discrete mixed action spaces. Furthermore, to handle the coordination among devices, the meta critic acts as a DQN to output the joint discrete action of all devices and each basic critic acts as the critic part of DDPG to evaluate the output of the corresponding actor. Simulation results show that the proposed HDRL algorithm can significantly reduce the task computation latency compared with baseline offloading schemes.

关键词： narrowband Internet of Things   mobile edge computing   deep reinforcement learning   compute offload   resource allocation  

摘要： Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient comput-ing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algo-rithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200 KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%,and when the execution task volume is 600 KBit, the total cost of system execution tasks can be reduced by 16.7% at most.

关键词： Blockchain   industrial internet   mobile edge computing   task scheduling   reinforcement learning  

摘要： With the rapid development of the Internet of Things (IoT) and Internet technology, the product of the combination of the two, the Industrial Internet, has also received extensive attention and there are more and more research achievements related to the Industrial Internet. In the industrial Internet system, the communication network system composed of sensors, communication nodes, controllers and other intelligent devices can realize efficient and convenient data interaction between people and machines, providing an important infrastructure and technical support for industrial big data analysis and intelligent production. However, in the current industrial Internet system, industrial equipment users generally have the problem of low computing energy efficiency, and the collected industrial data has a high-security risk in the transmission, processing and other processes. At the same time, the size and scale of the industrial Internet equipment group is huge, and the lack of rational resource allocation leads to excessive waste of computing resources in the system, which is also a prominent problem of the current industrial Internet *** response to the above questions, this paper, on the basis of reading a large number of documents, integrates the improved DRL algorithm, End-Edge-Cloud architecture and blockchain to form a new industrial Internet architecture. The architecture realizes computing offload through the three-tier structure of terminal layer, edge layer and cloud layer, and guarantees the security of industrial data through the decentralized feature of blockchain, ultimately achieving the goal of reducing energy consumption, computing overhead and trusted computing. In the architecture proposed in this paper, the dynamic unloading of industrial data and computing tasks is achieved through a three-tier architecture. The MDP is used to build an optimization problem model, and the improved DRL algorithm is used to iteratively solve the optimal comput

关键词： Mobile edge computing   Compute-intensive applications   User mobility   Computation offloading   Service migration  

摘要： With the increase in the use of compute-intensive applications, the demand to continuously boost the efficiency of data processing increases. Offloading the compute-intensive application tasks to the edge servers can effectively solve problems for resource-constrained mobile devices. However, the computation offloading may increase network load and transmission delay, which will influence the user experience. On the other hand, the unceasing distance change between the local device and edge server could also affect the service quality due to user mobility. This paper proposes the offloading and service migration methods for compute-intensive applications to deal with these issues. First, the fine-grained computation offloading algorithm based on a non-cooperative game is proposed. The overhead on both the local side and edge side is analyzed. Moreover, the service migration path selection based on the Markov decision process is proposed by considering user mobility, energy cost, migration cost, available storage, and bandwidth. The optimal service migration path is selected according to the Markov decision process, which can improve service quality. Experiment results show that our proposed offloading strategy performs better in reducing energy consumption by more than 10% and latency by more than 6.2%, compared with other baseline algorithms, and saving mobile device energy and reducing task response time, saving over 10% of time and energy consumption compared to similar algorithms. The proposed service migration scheme can reduce migration times and maintain a success rate of more than 90% while guaranteeing service continuity in a multi-user scenario.