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Rescue assignments strategy are crucial for multiple Autonomous Underwater Vehicle (multi-AUV) systems in three dimensional (3-D) complex underwater environments. Considering the requirements of rescue missions, multi-AUV systems need to be cost-effective, fast-rescuing, and less concerned about the relationship between rescue missions. The real-time rescue plays a vital role in the multi-AUV system with the characteristics mentioned above. In this paper, we propose an efficient Reward acting on Reinforcement Learning and Particle Swarm Optimization (R-RLPSO), to provide a strategy of real-time rescue assignment for the multi-AUV system in the 3-D underwater environment. This strategy consists of the following three parts. Firstly, we present a reward-based real-time rescue assignment algorithm. Secondly, we propose an Attraction Rescue Area containing a Rescue Area. For the waypoints in each Attraction Rescue Area, the reward is calculated by a linear reward function. Thirdly, to speed up the convergence of the R-RLPSO and mark the rescue states of Attraction Rescue Area and rescue area, we develop a Reward Coefficient based on the reward of all Attraction Rescue Areas and Rescue Areas. Finally, simulation results show that the system based on R-RLPSO is more cost-effective and time-saving than that of based on comparison algorithms ISOM and IACO. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has been recognized as a viable technology to satisfy low-delay computation requirements for resource-constrained Internet of things (IoT) devices. Nevertheless, the broadcast feature of wireless electromagnetic communications may lead to the security threats to IoT devices. In order to enhance the task offloading security, this paper proposes a reconfigurable intelligent surface (RIS)-assisted secure MEC network framework. Furthermore, we investigate the max-min computation efficiency problem under the secure computation rate requirements, by jointly optimizing the local computing frequencies and transmission power of IoT devices, time-slot assignment, and phase beamforming of the RIS. To solve the formulated non-convex problem, we further develop an iterative algorithm, in which the Dinkelbach-type method and block coordinate descent (BCD) technique are utilized to tackle the fractional objective function and coupled optimization variables, respectively. In particular, the successive convex approximation (SCA) and penalty function-based methods are exploited to solve the transmit power control and reflecting beamforming optimization subproblems, respectively, and the closed-form expression for local computing frequencies optimization subproblem is derived. Numerical results quantify the performance gain achieved by the proposed RIS-assisted secure MEC networks, when compared to existing benchmark methods. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a popular edge learning approach that utilizes local data and computing resources of network edge devices to train machine learning (ML) models while preserving users’ privacy. Nevertheless, performing efficient learning tasks on the devices and achieving longer battery life are primary challenges faced by federated learning. In this paper, we are the first to study the application of heterogeneous computing (HC) and wireless power transfer (WPT) to federated learning to address these challenges. Especially, we propose a heterogeneous computation and resource allocation framework based on a heterogeneous mobile architecture to achieve effective implementation of FL. To minimize the energy consumption of smart devices and maximize their harvesting energy simultaneously, we formulate an optimization problem featuring multidimensional control, which jointly considers time splitting for WPT, dataset size allocation, transmit power allocation and subcarrier assignment during communications, and processor frequency of processing units (central processing unit (CPU) and graphics processing unit (GPU)). However, the major obstacle is how to design a proper algorithm to solve this optimization problem efficiently. For this purpose, we decouple the optimization variables so as to achieve high efficiency in deriving its solution. Particularly, we first compute the optimal processor frequency and dataset size allocation via employing the Lagrangian dual method, followed by finding the closed-form solution to the optimal time splitting allocation, and finally attain the optimal subcarrier assignment as well as transmit power for transmissions through an iteration algorithm. To evaluate the performance of our proposed scheme, we set up four baseline schemes as comparison, and simulation results show that the proposed scheme converges quite fast and better enhance the energy efficiency of the wireless powered FL system compared with the baseline schemes. [ABSTRACT FROM AUTHOR]

Multi-UAV is a complete system represented by multiple unmanned aerial vehicles (UAV) through collaborative technology. Besides, the UAV group has the advantages in the number and functions that a single UAV does not have. In a complex environment, a single UAV cannot complete a large number of material transportations and escort functions. This paper, from the perspective of cooperative control and material escort of the UAV group, solves the escort problem in the UAV group cruise. Then, an autonomous cooperative flocking algorithm for heterogeneous UAV swarm (ACHF) is proposed. Firstly, the movement control factor of the escort UAV is proposed, and the escort UAV is deployed in the periphery of the transport UAV in the flocking process. Secondly, the system loss function including position and angle is proposed to make the escort UAV deployed as evenly as possible in the periphery of the transport UAV. Finally, in the case of different numbers of UAVs and different environments, the effectiveness of ACHF is supported by theoretical analysis and demonstrated by experiments. The results show that the escort UAVs can be evenly distributed in the periphery of the transport UAVs, forming a group of UAVs with autonomous cooperative capability. [ABSTRACT FROM AUTHOR]

The combination of wireless energy transfer (WET) and mobile edge computing (MEC) has been proposed to satisfy the energy supply and computation requirements of resource-constrained Internet of Things (IoT) devices. However, the energy transfer efficiency and task offloading rate cannot be guaranteed when wireless links between the hybrid access point (HAP) and IoT devices are hostile. To address this problem, this paper aims at utilizing the intelligent reflecting surfaces (IRS) technique to improve the efficiency of WET and task offloading. In particular, we investigate the total computation bits maximization problem for IRS-enhanced wireless powered MEC networks, by jointly optimizing the downlink/uplink phase beamforming of IRS, transmission power and time slot assignment used for WET and task offloading, and local computing frequencies of IoT devices. Furthermore, an iterative algorithm is presented to solve the non-convex non-linear optimization problem, while the optimal transmission power and time allocation, uplink phase beamforming matrixes and local computing frequencies are derived in closed-form expressions. Finally, extensive simulation results validate that our proposed IRS-enhanced wireless powered MEC strategy can achieve higher total computation rate as compared to existing baseline schemes. [ABSTRACT FROM AUTHOR]

Space-aerial-assisted computation offloading has been recognized as a promising technique to provide ubiquitous computing services for remote Internet of Things (IoT) applications, such as forest fire monitoring and disaster rescue. This article considers a space-aerial-assisted mixed cloud-edge computing framework, where the flying unmanned aerial vehicles (UAVs) provide IoT devices with low-delay edge computing service and satellites provide ubiquitous access to cloud computing. We aim to minimize the maximum computation delay among IoT devices with the joint scheduling for association control, computation task allocation, transmission power and bandwidth allocation, UAV computation resource, and deployment position optimization. Through exploiting block coordinate descent and successive convex approximation, we develop an alternating optimization algorithm with guaranteed convergence, to solve the formulated problem. Extensive simulation results are provided to demonstrate the remarkable delay reduction of the proposed scheme than existing benchmark methods. [ABSTRACT FROM AUTHOR]

As a promising new paradigm, Vehicular Edge Computing (VEC) can improve the QoS of vehicular applications by computation offloading. However, with more and more computation-intensive vehicular applications, VEC servers face the challenges of limited resources. In this paper, we study how to effectively and economically utilize the idle resources in volunteer vehicles to handle the overloaded tasks in VEC servers. First, we present a model of volunteer assisted vehicular edge computing, in which the cost and utility functions are defined for requesting vehicles and VEC servers, and volunteer vehicles are encouraged to assist the overloaded VEC servers via obtaining rewards from VEC servers. Then, based on Stackelberg game, we analyze the interactions between requesting vehicles and VEC servers, and find the optimal strategies for them. Furthermore, we prove theoretically that the Stackelberg game between requesting vehicles and VEC servers has a unique Stackelberg equilibrium, and propose a fast searching algorithm based on genetic algorithm to find the best pricing strategy for the VEC server. In addition, to maximize the reward of volunteer vehicles, we propose the volunteer task assignment algorithm for optimal mapping between the tasks and volunteer alliances. Finally, the effectiveness of the proposed scheme is demonstrated through a large number of simulations. Compared with other schemes, the proposed scheme can reduce the offloading cost of vehicles and improve the utility of VEC servers. [ABSTRACT FROM AUTHOR]

The satellite–terrestrial integrated networks (STINs) have gradually become a new class of effective ways to satisfy the requirements of a higher capacity and stronger connection in the future communications. In contrast with terrestrial networks, the fast periodic motion of satellites results in the dynamic time-varying features of STIN, which further leads to frequent changes in the connectivity of satellite–terrestrial links and the backhaul capacities of satellite networks. To balance the accessible capacity of STIN under the intermittent connectivity and dynamic backhaul capacity, an effective user association mechanism is needed. In this article, a dynamic user association (DUA) mechanism with task classification is proposed to meet the requirements of load balancing and the user task processing. First, a STIN model is constructed with low earth orbit satellites and the three types of base station, which are a macro base station, small cell base station, and low earth orbit based base station. After that, the optimization problem is formulated via jointly considering the task classification, the load condition of base stations, and the backhaul capacity of low earth orbit based base stations. Then, the DUA mechanism is proposed to find the most suitable base station serving each user. In DUA, a dynamic cell range extension algorithm is developed to adjust the load of STIN in terms of the resilient backhaul capacity, and a greedy-based user-centric user association with task classification algorithm is proposed to find the base station, which has the maximum rate and minimum load for each user and to meet the requirements of user task processing. The simulation results show that the proposed DUA can enhance the load balance and guarantee the task processing demand of STIN compared with the reference signal receiving power association and the max-sum rate association algorithms. [ABSTRACT FROM AUTHOR]

The explosive growth of wireless devices motivates the development of the internet-of-things (IoT), which is capable of interconnecting massive and diverse "things" via wireless communications. This is also called massive machine type communications (mMTC) as a part of the undergoing fifth generation (5G) mobile networks. It is envisioned that more sophisticated devices would be connected to form a hyperconnected world with the aids of the sixth generation (6G) mobile networks. To enable wireless accesses of such IoT networks, artificial intelligence (AI) can play an important role. In this article, the frameworks of centralized and distributed AI-enabled IoT networks are introduced. Key technical challenges, including random access and spectrum sharing (spectrum access and spectrum sensing), are analyzed for different network architectures. Deep reinforcement learning (DRL)-based strategies are introduced and neural networks-based approaches are utilized to efficiently realize the DRL strategies for system procedures such as spectrum access and spectrum sensing. Different types of neural networks that could be used in IoT networks to conduct DRL are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient user scheduling and power control problem in downlink multicell multiuser orthogonal frequency division multiple access networks, where only the channel distribution information is available. The resulting optimization problem is a combinatorial problem, which belongs to the class of cardinality constrained fractional programming problems subject to a maximum outage probability constraint. To solve the original problem, we derive a centralized joint user scheduling and power control solution satisfying the generalized Karush–Kuhn–Tucker conditions by resorting to the successive convex approximation (SCA). In particular, the proposed scheme can be used to solve the resource allocation problem subject to each user's minimum throughput constraint even if all the subcarriers have the same channel gain. A decentralized power control scheme is proposed to maximize the energy efficiency through combining SCA and alternating direction method of multipliers. Simulation results demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods. [ABSTRACT FROM AUTHOR]