Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks.

Monitoring vital signs is a key part of standard medical care for cancer patients. However, the traditional methods have instability especially when big fluctuations of signals happen, while the deep-learning-based methods lack pertinence to the sensors. A dual-path micro-bend optical fiber sensor and a targeted model based on the Divided-Frequency-CNN (DFC) are developed in this paper to measure the heart rate (HR) and respiratory rate (RR). For each path, features of frequency division based on the mechanism of signal periodicity cooperate with the operation of stable phase extraction to reduce the interference of body movements for monitoring. Then, the DFC model is designed to learn the inner information from the features robustly. Lastly, a weighted strategy is used to estimate the HR and RR via dual paths to increase the anti-interference for errors from one source. The experiments were carried out on the actual clinical data of cancer patients by a hospital. The results show that the proposed method has good performance in error (3.51 (4.51 %) and 2.53 (3.28 %) beats per minute (bpm) for cancer patients with pain and without pain respectively), relevance, and consistency with the values from hospital equipment. Besides, the proposed method significantly improved the ability in the report time interval (30 to 9 min), and mean / confidential interval (3.60/[-22.61,29.81] to -0.64 / [-9.21,7.92] for patients with pain and 1.87 / [-5.49,9.23] to -0.16 / [-6.21,5.89] for patients without pain) compared with our previous work.

Mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, combines the benefits of these two techniques, which can further improve the communication performance. However, high-quality air-ground channel links are more vulnerable to both the adversarial eavesdropping and the malicious jamming. Therefore, this paper proposes a reliable and secure communication approach assisted by the UAV-RIS to maximize the secrecy rate, while ensuring the quality of service (QoS) requirement of the legitimate user against both the eavesdroppers and the jammer. Specifically, with the imperfect channel state information and behaviors of mixed attacks, we try to maximize the achievable worst-case secrecy rate by jointly designing the transmit beamforming, artificial noise, UAV-RIS placement, and RIS’s passive beamforming. As the optimization problem is non-convex and the environment is highly dynamic, a post-decision state deep Q-network combined with Fourier feature mapping algorithm (called PDS-DQN-FFM) is further designed to effectively achieve the robust anti-attack transmission strategy. Simulation results demonstrate that our proposed learning based reliable and secure transmission approach significantly enhances both the secrecy rate and QoS satisfaction level as compared with existing approaches.

With the development of telecommunication technologies and the proliferation of network applications in the past decades, the traditional cloud network architecture becomes unable to accommodate such demands due to the heavy burden on the backhaul links and long latency. Therefore, edge computing, which brings network functions close to end-users by providing caching, computing and communication resources at network edges, turns into a promising paradigm. Benefit from its nature, edge computing enables emerging scenarios and use cases, such as augmented reality (AR) and Internet of Things (IowT). However, it also creates complexities to efficiently orchestrate heterogeneous services and manage distributed resources in the edge network. In this survey, we make a comprehensive review of the research efforts on service orchestration and resource management for edge computing. We first give an overview of edge computing, including architectures, advantages, enabling technologies and standardization. Next, a comprehensive survey of state-of-the-art techniques in the management and orchestration of edge computing is presented. Subsequently, the state-of-the-art research on the infrastructure of edge computing is discussed in various aspects. Finally, open research challenges and future directions are presented as well.

Collaborative inference in mobile edge computing (MEC) enables mobile devices to offload the computation tasks for the computation-intensive perception services, and the inference policy determines the inference latency and energy consumption. The optimal inference policy depends on the inference performance model of deep learning, the data generation model and the network model that are rarely known by mobile devices in time. In this paper, we propose a multi-agent reinforcement learning (RL) based energy-efficient MEC collaborative inference scheme, which enables each mobile device to choose both the partition point of deep learning and the collaborative edge of each mobile device based on the image quantity, the channel conditions and the previous inference performance. A learning experience exchange mechanism exploits the Q-values of the neighboring mobile devices to accelerate the inference policy optimization with less energy consumption. We also provide a deep multi-agent RL based inference scheme to accelerate learning for large-scale MEC networks, in which an actor network yields the collaborative inference policy probability distribution and a critic network guides the weight update of the actor network to enhance sample efficiency. We provide the inference performance bound and analyze the computational complexity. Both simulation and experimental results show that our proposed schemes reduce the inference latency and save the MEC energy consumption.

Visible light communication (VLC) is an emerging wireless technology to support high transmission rate for indoor devices by using existing lighting infrastructure, and VLC-based indoor localization is capable of providing high-accuracy localization. However, current VLC-based localization systems suffer from several key challenges such as sensitivity to random tilting of the receiver, which limits its full potential in real-world applications. In this paper, we design an integrated visible light communication and localization (VLCL) system to simultaneously support accurate real-time localization and communication services for indoor devices. To achieve this, an advanced differential phase difference of arrival (A-DPDOA) localization design is developed to simplify hardware and improve tracking robustness. In addition, a joint adaptive modulation, subcarrier and power allocation scheme is also proposed, which aims to improve the communication data rate and localization accuracy. Extensive experiments are performed to demonstrate that the proposed integrated VLCL system achieves higher localization accuracy and transmission data rate, compared to existing systems and schemes. Experiments also illustrate that the localization algorithm is more robust against the random tilting of the receiver under device movement in two-dimensional and three-dimensional scenarios.

Active eavesdropper sends jamming signals to raise the transmit power of base stations and steal more information from cellular systems. Network coding resists the active eavesdroppers that cannot obtain all the data flows, but highly relies on the wiretap channel states that are rarely known in wireless networks. In this paper, we present a reinforcement learning (RL) based random linear network coding scheme for drone-aided cellular systems to address eavesdropping. In this scheme, the network coding policy, including the encoded packet number, the packet and power allocation, is chosen based on the measured jamming power, previous transmission performance and BS channel states. A virtual model generates simulated experiences to update Q-values besides real experiences for faster policy optimization. We also propose a deep RL version and design a hierarchical architecture to further accelerate the policy exploration and improve the anti-eavesdropping performance, in terms of the intercept probability, the latency, the outage probability and the energy consumption. We analyze the computational complexity, drone deployment, secure coverage area and the performance bound of the proposed schemes, which are verified via simulation results.

With the advancement of the 5G network and Internet of Things (IoT) devices, Multi-access Edge Computing (MEC) proposed by ETSI provides multiple devices to access with low latency through heterogeneous networks such as smart factories and vehicular networks. In addition, video streaming and online gaming have become more popular and consume more than half of the traffic on the internet. Thus, there will be more edge servers deployed on the edge of the network for offloading the core network. However, the edge server is more vulnerable because of its proximity to the user equipment (UE). Attackers can quickly launch distributed denial-of-service (DDoS) attacks with plenty of infected IoT devices. In this paper, we propose Tradeoffs Between Security and Application QoE (TBSA) system to solve the security and resource management problems on the edge server. First, we deploy video streaming, online gaming, and network security applications on the edge server. We use Intrusion Detection and Protection Services (IDPS) to perform DDoS mitigation and design resource allocation algorithm to allocate the computing resources to the edge computing applications. Then, we compare different attack rates in the user scenarios and analyze multiple models under the resource limit condition. The experiments show that we can improve the Quality-of-Experience (QoE) of applications by edge computing resources management.

Recently, Multi-access Edge Computing (MEC) has become a promising enabler to support emerging applications in vehicular networks by offloading compute-intensive tasks from vehicles to proximate MEC servers. However, the high mobility of vehicles brings difficulties to provide reliable services in the MEC system due to potential outages of communication in the process of offloading. Also, load balancing of the MEC system is seldom considered in previous offloading schemes, which may increase the risk of system failure and reduce Quality of Service (QoS) of vehicles due to congestions. Currently, we still lack a low-complexity method to address these issues. In this paper, we aim to promote QoS of vehicular applications by taking vehicles' mobility and latency requirements into account while guaranteeing load balancing of the MEC system. Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. Then, by taking advantage of both Deep Neural Network (DNN) and Particle Swarm Optimization (PSO), we propose a novel framework to effectively address the problem, where PSO accelerates the training by providing high quality labeled data to DNN. Finally, simulation results show that our proposed method outperforms traditional heuristic algorithms in terms of QoS and runtime.

The industrial Internet of Things (IIoT) has been widely deployed to provide autonomous inspection on current production status and quality of products for modern manufacturing. However, the IIoT sensors generally are short of computing capabilities and therefore could not offer acceptable latency for computation-intensive inspection tasks. Besides, the mission-critical industrial applications are extremely sensitive to inspection failure, which may lead to serious manufacturing problems or accidents. In this paper, we propose a risk-aware cloud-edge computing framework for the delay-sensitive inspections of autonomous manufacturing. Due to the uncertainty of 802.11ax, we utilize the conditional value-at-risk (CVaR) to measure the inspection risk basing on the distribution of channel access delay. We develop a branch-and-check (BNC) approach to optimally and efficiently deploy the decomposable inspection tasks with the minimum operation cost and acceptable latency. The extensive simulations guide the operational use for future IIoT and the results show that the proposed system can save a large amount of unnecessary operation cost by enabling the processor sharing strategy.

With the rapid development of deep learning technology, the modern Internet-of-Things (IoT) cameras have very high demands on communication, computing, and memory resources so as to achieve low latency and high accuracy live video analytics. Thanks to the mobile-edge computing (MEC), intelligent offloading to the MEC nodes can bring a lot of benefits, especially when the decomposable pipeline is adopted in the cloud-edge architecture. In this article, we provide decomposable intelligence on a cloud-edge IoT (DICE-IoT) framework to support joint latency- and accuracy-aware live video analytic services. Specifically, the intelligent framework enables the pipeline-sharing mechanism to reduce MEC resource usage. A Nash bargaining is proposed to incentivize cooperative computing provision between the MEC and the cloud, and a generalized benders decomposition (GBD)-based approach is utilized to optimize the social welfare. The results show that the proposed DICE-IoT framework can achieve a win–win–win solution to the IoT device, the MEC, and the cloud stratum.

Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending the fog computing to conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a VFC system by combining both PVA and smart parking. A single- round multi-item parking reservation auction is proposed to guide the on-the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatible, individual rational and budget balance. The simulation results confirmed the win-win performance enhancement to fog node controller (FNC), vehicles, and parking places from the proposed design.

With the large expected demand of wireless communication, Device-to-Device (D2D) communication has been proposed as a promising technology to enhance network performance. Nevertheless, the selfish nature of potential D2D users may impale the performance of D2D-enabled network. In this paper, we propose a D2D-enabled cellular network framework, which support a novel group D2D mode under overlay D2D communication. The group-based design is derived from the discussions of two common D2D modes, divided and shared D2D modes, regarded as special cases. The proposed framework provides a pricing-based dynamic Stackelberg game for optimal mode selection and spectrum partitioning. We propose the incentive compatible pricing strategy to provide proper incentive for these selfish potential D2D pairs to make optimal choices in mode selection. Our results show that the pricing and spectrum partition strategy effectively prevents selfish potential D2D users from harming the system performance while fully exploits the potential of D2D communication.