关键词： Device-to-device communication   Resource management   5G mobile communication   Interference   Quality of service   Optimization   Edge computing   Network slicing   5G cellular networks   mobile edge computing   device-to-device   distributed optimization   consensus algorithm  

摘要： Fifth Generation (5G) cellular networks aim to overcome the pressing demands posed by dynamic Quality of Service (QoS) constraints, which have primarily remained unaddressed using conventional network infrastructure. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that readily meet throughput requirements. The idea of combining Device-to-Device (D2D), Mobile Edge Computing (MEC), and Network slicing (NS) can improve spectrum utilization with better performance and scalability. This work presents a spectrum efficiency optimization problem in D2D based 5G-Heterogeneous Cellular Network (5G-HCN) with NS. Owing to the shortage of resources, we propose an underlay model where macro-cell users (MUs), small-cell users (SUs), and D2D users (DUs) reuse the resources while considering the effects of interference. The goal is to maximize the average network spectrum efficiency (SE) and throughput without degrading the system performance. The problem at hand is naturally a non-convex mixed-integer non-linear programming (MINLP) problem that is intractable. Therefore, we have suggested a distributed resource allocation strategy with an edge computing (DRA-EC) approach to find the sub-optimal solution. In distributed augmented Lagrange method, each edge router located at BS will solve its problem locally, and the consensus algorithm will find the global solution using these local estimates. The central slice controller will cut the customized network slices according to the bandwidth requirements of each user type with optimized spectrum information. The simulation outcomes prove that our proposed method is near the central optimization scheme with low computational complexity. It is much better because it reduces the computational time and system overhead.

关键词： Task analysis   Servers   Cloud computing   Smart homes   Edge computing   Energy consumption   Internet of Things   Deep learning   integer linear programming   mobile edge computing   smart home   task offloading  

摘要： Mobile edge computing which provides computing capabilities at the edge of the radio access network can help smart home reduce response time. However, the limited computing capacity of edge servers is the bottlenecks for the development of edge computing. We integrate cloud computing and edge computing in the Internet of Things to expand the capabilities. Nevertheless, the cost of leasing computing resources has been seldom considered. In this paper, we study the joint transmission power and resource allocation to minimize the users' overhead which is measured by the sum of energy consumption and cost leasing servers. We formulate the problem as a Mixed Integer Linear Programming which is NP-hard and present the Branch-and-Bound to solve it. Due to high complexity, a learning method is proposed to accelerate the algorithm. The branching policy can be learned to reduce the time-cost of the Branch-and-Bound algorithm. Simulation results show our approach can improve the Branch-and-Bound efficiency and performs closely to the traditional branching scheme.

关键词： Task analysis   Computational modeling   Servers   Energy consumption   Trajectory   Reinforcement learning   Unmanned aerial vehicles   Computation offloading   deep learning   energy saving   mobile edge computing   reinforcement learning   unmanned aerial vehicle  

摘要： Mobile Edge Computing (MEC) has been widely employed to support various Internet of Things (IoT) and mobile applications. By leveraging the advantages of easily deployed and flexibility of Unmanned Aerial Vehicle (UAV), one of MEC primary functions is employing UAVs equipped with MEC servers to provide computation supports for the offloaded tasks by mobile users in temporally hotspot areas or some emergent scenarios, such as sports game areas or destroyed by natural disaster areas. Despite the numerous advantages of UAV carried with a MEC server, it is restricted by its limited computation resources and sensitive energy consumption. Moreover, due to the complexity of UAV-assisted MEC system, its computational resource optimization and energy consumption optimization cannot be achieved well in traditional optimization methods. Furthermore, the computational cost of the MEC system optimization is often exponentially growing with the increase of the MEC servers and mobile users. Therefore, it is considerably challenging to control the UAV positions and schedule the task offloading ratio. In this paper, we proposed a novel Deep Reinforcement Learning (DRL) method to optimize UAV trajectory controlling and users' offloaded task ratio scheduling and improve the performance of the UAV-assisted MEC system. We maximized the system stability and minimized the energy consumption and computation latency of UAV-assisted the MEC system. The simulation results show that the proposed method outperforms existing work and has better scalability.

关键词： Streaming media   Protocols   Internet   Servers   Quality of service   Quality of experience   Overlay networks   Internet of Multimedia Things   multihoming   multipath   mobile edge computing   fog computing   VR 360 video  

摘要： The focus of this survey is to study the protocols, mechanisms, and the latest standards proposed in the literature for improving the performance and quality of video content in multipath and multihomed overlay networks. Multipath is a broader term, but in the context of this survey, we define multipath as enhancing network routing technique by using various paths that are not necessarily completely disjoint. Multipath can furnish a variety of advantages such as reliability,connection persistence, increased perceived throughput and load balancing. On the other hand, multihoming is the ability to use multiple network interfaces when connecting to the Internet to increase reliability, resilience, and performance. Most existing surveys are specialized in one specific domain area related to multipath or multihoming. This study covers the research proposals at the different layers/sublayers of an overlay network from transport to the application and extends to cover the latest technologies like machine learning, Fog and Mobile Edge computing, VR 360 video, and the Internet of Multimedia Things (IoMT). As such, our work tries to be as comprehensive as possible to relate multipath and multihoming research solutions for video streaming to the current and emerging video streaming technologies.

关键词： computer vision   deep learning   mobile augmented reality   mobile edge computing   object detection  

摘要： Mobile augmented reality (AR) is a technology that integrates virtual information with the real world on the mobile intelligent terminal, therefore the ability to accurately detect the to-be-enhanced objects in the environment directly determines the performance of mobile AR systems. With the rapid advancement of deep learning, a large number of deep learning-based methods have been proposed for better detection. However, such problems as limited computing power, high energy consumption, large model size, and offloading latency make it difficult to combine deep learning-based object detection with mobile AR. This paper first summarized previous studies on deep learning-based object detection from both aspects of two stages and one stage, then categorized the object detection systems for mobile AR, and analyzed the approaches based on local, cloud, or edge ends, as well as collaboration. Finally, both the advantages and limitations of these methods were summarized, and predictions were made on the problems to be solved and the future development of object detection in mobile AR. © 2021, Editorial of Board of Journal of Graphics. All rights reserved.

关键词： Task analysis   Cloud computing   Processor scheduling   Scheduling   Energy consumption   Computational modeling   Wireless communication   Mobile edge computing   task offloading   resource scheduling   optimization algorithm   deep reinforcement learning  

摘要： Computation-intensive mobile applications are explosively increasing and cause computation overload for smart mobile devices (SMDs). With the assistance of mobile edge computing and mobile cloud computing, SMDs can rent computation resources and offload the computation-intensive applications to edge clouds and remote clouds, which reduces the application completion delay and energy consumption of SMDs. In this paper, we consider the mobile applications with task call graphs and investigate the task offloading and resource scheduling problem in hybrid edge-cloud networks. Due to the interdependency of tasks, time-varying wireless channels, and stochastic available computation resources in the hybrid edge-cloud networks, it is challenging to make task offloading decisions and schedule computation frequencies to minimize the weighted sum of energy, time, and rent cost (ETRC). To address this issue, we propose two efficient algorithms under different conditions of system information. Specifically, with full system information, the task offloading and resource scheduling decisions are determined based on semidefinite relaxation and dual decomposition methods. With partial system information, we propose a deep reinforcement learning framework, where the future system information is inferred by long short-term memory networks. The discrete offloading decisions and continuous computation frequencies are learned by a modified deep deterministic policy gradient algorithm. Extensive simulations evaluate the convergence performance of ETRC with various system parameters. Simulation results also validate the superiority of the proposed task offloading and resource scheduling algorithms over baseline schemes.

关键词： Computation   energy efficiency   Internet of Things   mobile edge computing   offloading   resource allocation   UAVs  

摘要： With the emergence of computation-intensive and delay-sensitive applications, such as face recognition, virtual reality, augmented reality, and Internet of Things (IoT) devices;Mobile Edge Computing (MEC) allows the IoT devices to offload their heavy computation tasks to nearby edge cloud network rather than to compute the tasks locally. Therefore, it helps to reduce the energy consumption and execution delay in the ground mobile users. Flying Unmanned Aerial Vehicles (UAVs) integrated with the MEC server play a key role in 5G and future wireless communication networks to provide spatial coverage and further computational services to the small, battery-powered and energy-constrained devices. The UAV-enabled MEC (U-MEC) system has flexible mobility and more computational capability compared to the terrestrial MEC network. They support line-of-sight (LoS) links with the users offloading their tasks to the UAVs. Hence, users can transmit more data without interference by mitigating small-scale fading and shadowing effects. UAVs resources and flight time are very limited due to size, weight, and power (SWaP) constraints. Therefore, energy-aware communication and computation resources are allocated in order to minimize energy consumption. In this paper, a brief survey on U-MEC networks is presented. It includes the brief introduction regarding UAVs and MEC technology. The basic terminologies and architectures used in U-MEC networks are also defined. Moreover, mobile edge computation offloading working, different access schemes used during computation offloading technique are explained. Resources that are needed to be optimized in U-MEC systems are depicted with different optimization problem, and solution types. Furthermore, to guide future work in this area of research, future research directions are outlined. At the end, challenges and open issues in this domain are also summarized.

关键词： Drones   Authentication   Elliptic curve cryptography   Internet of Things   Public key   Hash functions   Smart grids   Authenticated key agreement   CK adversarial model   certificateless AKA   elliptic curve cryptography   Internet of Drones   mobile edge computing   ProVerif   UAS   UAV  

摘要： Security and privacy are among the most critical challenges on the internet of drones (IoD) network. The communication entities of the IoD network can communicate securely with the use of authenticated key agreement (AKA) based techniques. However, the design of such techniques must balance the tradeoff between security and lightweight features. Recently, Chen et al. proposed an authentication and key sharing scheme for IoD deployment. It is, however, realized after scrutiny that the proposed technique is vulnerable to security attacks under the well-accepted Canetti-Krawczyk (CK) adversary model. Moreover, the scheme applies to the IoD network with only one drones' flying zone. To solve these challenges, this paper proposed a secure lightweight proven authenticated key agreement (SLPAKA) technique for IoD deployment. The technique is free from all the problems identified in the scheme of Chen et al. To ensure the reliability of the SLPAKA, the security of the technique has been assessed from a theoretical method and formal way using the ProVerif cryptographic protocol verification tool. Apart from comparing the performance of SLPAKA with the benchmarking schemes in terms of security, computational cost, and communication cost, the SLPAKA and the technique proposed by Chen et al. are implemented using a python programming language to evaluate and compare their performance in terms of energy consumption and computational time metrics. The results show that the SLPAKA outperforms the technique of Chen et al. and all the other benchmarking techniques in terms of security and lightweight features.

关键词： Training   Power system dynamics   Machine learning   Reliability theory   Collaborative work   Delays   Edge computing   Mobile edge computing   machine learning   federated learning   smart grid   link reliability  

摘要： Smart grid applications, such as predicting energy consumption, grid user behavior analysis and predicting energy theft, etc., are data-driven applications that require machine learning with a wealth of data generated from Internet of Things (IoT) based metering devices. However, traditional methods of uploading this huge data to the remote cloud for data analytics may be low efficient due to the non-negligible network transmission delay. By deploying a number of computing-enabled devices at the network edge, edge computing supports the implementation of machine learning close to the power grid environment. Considering the limited computing resources of edge devices and non-independent and identical (non-IID) data source, federated learning is a feasible edge computing based machine learning model. In federated learning, distributed mobile clients and a federated server collaborate to perform machine learning. Generally, the more clients to join the federated learning, the faster to obtain learning convergence and the higher resource utility. However, the communications between clients and the server in training rounds of federated learning may fail due to time-varying link reliability properties in a wireless network of smart grid, which not only slows down the model convergence rate but also wastes resources, such as energy consumption for invalid local training. This paper studies a dynamic federated learning problem in a power grid mobile edge computing (GMEC) environment, considering the high dynamic of link reliability. We design a delay deadline constrained federated learning framework to avoid extremely long training delay, and then formulate a dynamic client selection problem for computing utility maximization in such learning framework. Two online client selection algorithms, including cli-max greedy and uti-positive guarantee, are proposed to address the problem. The theoretical analysis and simulation results are conducted to illustrate the efficiency of

关键词： Deep Reinforcement Learning   Mobile Edge Computing   Power Allocation   Task Offloading   UAV  

摘要： Mobile edge computing (MEC) can provide computing services for mobile users (MUs) by offloading computing tasks to edge clouds through wireless access networks. Unmanned aerial vehicles (UAVs) are deployed as supplementary edge clouds to provide effective MEC services for MUs with poor wireless communication condition. In this paper, a joint task offloading and power allocation (TOPA) optimization problem is investigated in UAV-assisted MEC system. Since the joint TOPA problem has a strong non-convex characteristic, a method based on deep reinforcement learning is proposed. Specifically, the joint TOPA problem is modeled as Markov decision process. Then, considering the large state space and continuous action space, a twin delayed deep deterministic policy gradient algorithm is proposed. Simulation results show that the proposed scheme has lower smoothing training cost than other optimization methods.