关键词： Mobile edge computing   Task offloading   Optimization   Deep Q-learning  

摘要： In a mobile edge computing (MEC) network, mobile devices could selectively offload tasks to the edge server(s) to save time and energy. However, we should consider many dynamic factors in task offloading optimization, which increases the complexity of this problem. The traditional optimization approaches could require solving complex models to derive the optimal solution. This type of optimization problems are often NP-hard and will cause a considerable overhead on optimization. In contrast, a well-trained empirical model, such as an artificial neural network could be more efficient in decision making. In this research, considering the potential uneven spatial distribution of mobile devices in a MEC network with multiple wireless edge gateways, we allow an edge gateway to offload tasks to a nearby edge gateway further. We propose a deep Q-learning-based joint optimization approach for both device-level and edge-level task offloading. We also design a centralized mathematical programming solution for exploring the optimal trade-off performance. Simulation results show that the proposed approach achieves a satisfactory task delay performance and a better trade-off between the task delay and the energy consumption on tasks.

关键词： 5G   Internet of things(IoT)   Mobile edge computing   Feistel architecture   SCESW   GRC algorithm  

摘要： With the large-scale application of 5G in industrial production,the Internet of Things has become an important technology for various industries to achieve efficiency improvement and digital transformation with the help of the mobile edge *** the modern industry,the user often stores data collected by IoT devices in the cloud,but the data at the edge of the network involves a large of the sensitive information,which increases the risk of privacy *** order to address these two challenges,we propose a security strategy in the edge *** security strategy combines the Feistel architecture and short comparable encryption based on sliding window(SCESW).Compared to existing security strategies,our proposed security strategy guarantees its security while significantly reducing the computational *** our GRC algorithm can be successfully deployed on a hardware platform.

关键词： IoT   LoraWAN   Mobile Edge Computing   Q-learning Internet of things   Reinforcement Learning   Simulation   Solar-powered   Task-offloading   Wireless power transmission  

摘要： Typical IoT(Internet of things) devices have a limited lifetime because they use a battery as an energy source. To fundamentally solve this problem, various environmental energy harvesting IoT is being researched around solar energy. Meanwhile, many studies are attempting to improve the quality of experience (QoE) in terms of the energy problem and delay time of IoT devices. Many of these studies on QoE improvement are approaching it as a cost minimization problem, especially an energy use minimization problem. However, such an energy cost minimization approach due to limited energy is not appropriate for energy harvesting IoT devices, which can continuously harvest energy. Since many energy-harvesting IoT devices can continuously harvest energy, we should approach in the direction of cost optimization that can utilize energy to the maximum rather than cost minimization approach. This study proposes a reinforcement learning-based dynamic task offloading scheme that can enhance user QoE while maximizing the utilization of harvested energy in an energy harvesting IoT edge computing environment. The proposed scheme modeled Q-learning using the information of dynamically changing amount of energy harvesting and the information of IoT devices and edge nodes. It can perform very simple, yet efficiently operates compared to complex models of the conventional minimization problem. © 2021, Korean Institute of Communications and Information Sciences. All rights reserved.

关键词： Delays   Mobile handsets   Resource management   Energy consumption   Power demand   Task analysis   Cloud computing   Offloading   resource allocation   mobile cloud computing   mobile edge computing  

摘要： A promising technique to provide mobile applications with high computation resources is to offload the processing task to the cloud. Utilizing the abundant processing capabilities of the clouds, mobile edge computing enables mobile devices with limited batteries to run resource hungry applications and to save power. However, it is not always true that edge computing consumes less power compared to device computing. It may take more power for the mobile device to transmit a file to the cloud than running the task itself. This paper investigates the power minimization problem for the mobile devices by data offloading in multi-cell multi-user OFDMA mobile edge computing networks. We consider the maximum acceptable delay as QoS metric to be satisfied in our network. We formulate the problem as a mixed integer nonlinear problem which is converted into a convex form using D.C. approximation. To solve the converted optimization problem, we have proposed centralized and distributed algorithms for joint power allocation and channel assignment together with decision-making. Simulation results illustrate that by utilizing the proposed algorithms, considerable power savings can be achieved, e.g., about 60 percent for large bit stream size compared to local computing baseline.

关键词： Task analysis   Bandwidth   Programming   Edge computing   Servers   Mobile handsets   Computational modeling   Coded caching   mobile edge computing   multicast   bandwidth allocation   virtual reality  

摘要： Edge caching and computing have been regarded as an efficient approach to tackle the wireless spectrum crunch problem. In this paper, we design a general coded caching with device computing strategy for computational tasks, e.g., virtual reality (VR) rendering, to minimize the average transmission bandwidth under the quality of service guarantee. Because both coded data and stored data can be the data before or after computing, the proposed scheme has numerous edge computing and caching paths corresponding to different bandwidth requirement. We thus formulate a joint coded caching and computing optimization problem to decide whether a mobile device stores the data before computing or the data after computing, which tasks to be coded cached and which tasks to be computed locally. The optimization problem is shown to be 0-1 non-convex non-smooth programming, and can be decomposed into a computation offloading programming and a coded caching programming. For a computation offloading subproblem, we proposed an algorithm which applies the convergence of the alternating direction method of multipliers (ADMM) under a non-convex programming and the wide application of the concave-convex procedure (CCCP) for difference of convex (DC) programming to obtain a stationary point, and numerical results verify the convergence of the proposed algorithm and its suboptimality. For the coded cache programming, we design a low complexity algorithm to obtain an acceptable solution. Numerical results demonstrate that the proposed scheme provides a significant bandwidth saving by taking full advantage of the caching and computing capability of mobile devices.

关键词： Task analysis   Energy consumption   NOMA   Unmanned aerial vehicles   Uplink   Trajectory   Minimization   Computation capacity   mobile edge computing   NOMA   time allocation   UAV  

摘要： An unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks fulfil the on-demand computation services for the mobile terminals (MTs) with its high mobility and easy deployment. Thus, reduction in the latency is observed but the energy efficiency is still a major issue in this network, as both the UAV and MTs have limited energy storage batteries. Also, to support the massive connectivity, and to handle the data traffic generated by the MTs in an uplink scenario, using nonorthogonal multiple access (NOMA) is accomodated. The NOMA-based MEC provides flexible computing services to MTs. In this article, our goal is to minimize the total energy consumption of the NOMA-based MEC networks underlaying UAV with time, computation capacity, and UAV trajectory. The formulated model is a nonlinear programming problem;for the solution, it is divided into two subproblems: joint time allocation and task computation capacity, and UAV trajectory optimization. To solve the joint time allocation and computation task capacity, we proposed an iterative algorithm with low complexity, where closed-form solutions are obtained in each step. Next, to optimize the trajectory of the UAV, we used the successive convex approximation technique. Numerical results demonstrated that the proposed scheme achieves better results as compared to the orthogonal multiple access and equal resource allocation schemes.

关键词： Task analysis   Reliability   Computational modeling   Resource management   Servers   Reinforcement learning   Collaboration   Internet of Vehicles   mobile edge computing   collaborative computing   ultra-reliable and low-latency communications   multi-objective reinforcement learning  

摘要： Ultra reliability and low latency communications (URLLC) are considered as one of the most important use cases for computation tasks in the Internet of Vehicles (IoV) edge computing networks. To meet the task requirements in IoV networks, communication and computing resources are allocated in an effective and efficient way. Thus, this paper proposes a multi-objective reinforcement learning strategy, called intelligent communication and computation resource allocation (ICCRA), which combines communication and computing resource allocation to reduce the total system cost consisting of latency and reliability. Specifically, this strategy can be decomposed into three algorithms. The algorithm called joint computation offloading and collaboration is a general framework of the strategy, it first uses the K-nearest neighbor method to select offloading layers for computation tasks, such as cloud computing layer, mobile edge computing layer, and local computing layer. Then, when selecting the local computing layer to perform the task, the algorithm called collaborative vehicle selection is used to find the target vehicle to execute collaborative computing. The allocation of communication and computing resources is defined as two independent objectives, the algorithm called multi-objective resource allocation uses the reinforcement learning to achieve the optimal solution, at the mobile edge computing layer. Simulation results show that compared with local computing, edge computing, and random computing strategies, the proposed strategy reduces the total system cost effectively. For instance, compared with edge computing strategy, the total system cost of the proposed strategy can be reduced about 50% on average with the different number of vehicle tasks.

关键词： Blockchains   Servers   Edge computing   Wireless communication   Resource management   Games   Bitcoin   Mobile edge computing   blockchain   IoT   nonce ordering   non-cooperative game  

摘要： Deploying a mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of the MEC server. To address this problem, we first propose an untrusted MEC proof of work (PoW) scheme in mobile blockchain networks where plenty of nonce hash computing demands can be offloaded to the MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the user's nonce selection strategy as a non-cooperative game, where utilities of the individual user are maximized in the untrusted MEC-aided mobile blockchain networks. We also prove the existence of Nash equilibrium and analyze that the cooperation behavior is unsuitable for blockchain-enabled IoT devices by using the repeated game. Finally, we design the blockchain's difficulty adjustment mechanism to ensure stable block times during a long period of time. Compared with the weighted round-robin algorithm, our proposed nonce ordering algorithm can provide fairer computation resources and optimal nonce selection strategies for all mobile users. Network stability is gained through the proposed blockchain's difficulty adjustment mechanism. The analysis and optimization results provide valuable design insights for practical mobile blockchain IoT systems.

关键词： Edge/fog computation offloading   Drone video analytics   Mobile edge computing   Learning-based scheme   Data processing in fog computing  

摘要： Multi-Unmanned Aerial Vehicle (UAV) systems with high-resolution cameras have been found useful for operations such as smart city and disaster management. These systems feature Flying Ad-Hoc Networks (FANETs) that connect the computation edge with UAVs and a Ground Control Station (GCS) through air-to-ground wireless network links. Leveraging the edge/fog computation resources effectively with energy-latency-awareness, and handling intermittent failures of FANETs are the major challenges in supporting video processing applications. In this paper, we propose a novel "DroneCOCoNet" framework for drone video analytics that coordinates intelligent processing of large video datasets using edge computation offloading and performs network protocol selection based on resource-awareness. We present two edge computation offloading approaches, i.e., heuristic-based and reinforcement learning-based approaches. These approaches provide intelligent task sharing and co-ordination for dynamic offloading decision-making among UAVs. Our scheme handles the problem of computation offloading tasks in two separate ways: (i) heuristic decision-making process, and (ii) Markov decision process;wherein we aim to minimize the total computation costs as well as latency in the edge/fog resources while minimizing video processing times to meet application requirements. Our experimental results show that our heuristic-based offloading decision-making scheme enables lower scheduling time and energy consumption for low drone-to-ground server ratios. In comparison, our dynamic reinforcement learning-based decision-making approach increases the accuracy and saves overall time periodically. Notably, these results also hold in various other multi-UAV scenarios involving largely different numbers of detected objects in e.g., smart farming, transportation traffic flow monitoring and disaster response. (C) 2021 Elsevier B.V. All rights reserved.

关键词： 5G Network   Mobile edge computing   Base stations   Network topology   Geographic location   Random graphs   Network parameters  

摘要： Mobile Edge Computing (MEC) is vital to support the numerous, future applications that are envisioned in the 5G and beyond mobile networks. Since computation capabilities are available at the edge of the network, applications that need ultra low-latency, high bandwidth and reliability can be deployed more easily. This opens up the possibility of developing smart resource allocation approaches that can exploit the MEC infrastructure in an optimized way and, at the same time, fulfill the requirements of applications. However, up to date, the progress of research in this area is limited by the unavailability of publicly available true MEC topologies that could be used to run extensive experiments and to compare the performance on different solutions concerning planning, scheduling, routing etc. For this reason, we decided to infer and make publicly available several synthetic MEC topologies and scenarios. Specifically, based on the experience we have gathered with our experiments Xiang et al. [1], we provide data related to 3 randomly generated topologies, with increasing network size (from 25 to 100 nodes). Moreover, we propose a MEC topology generated from OpenCellID [2] real data and concerning the Base Stations' location of 234 LTE cells owned by a mobile operator (Vodafone) in the center of Milan. We also provide realistic reference parameters (link bandwidth, computation and storage capacity, offered traffic), derived from real services provided by MEC in the deployment of 5G networks. (C) 2021 Published by Elsevier Inc.