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Recently, many deep reinforcement learning (DRL)-based task scheduling algorithms have been widely used in edge computing (EC) to reduce energy consumption. Unlike the existing algorithms considering fixed and fewer edge nodes (servers) and tasks, in this paper, a representation model with a DRL based algorithm is proposed to adapt the dynamic change of nodes and tasks and solve the dimensional disaster in DRL caused by a massive scale. Specifically, 1) we apply the representation learning models to describe the different nodes and tasks in EC, i.e., nodes and tasks are mapped to corresponding vector sub-spaces to reduce the dimensions and store the vector space efficiently. 2) With the space after dimensionality reduction, a DRL-based algorithm is employed to learn the vector representations of nodes and tasks and make scheduling decisions. 3) The experiments are conducted with the real-world data set, and the results show that the proposed representation model with DRL-based algorithm outperforms the baselines 18.04% and 9.94% on average regarding energy consumption and service level agreement violation (SLAV), respectively.

Driver fatigue is a leading factor in road accidents that can cause severe fatalities. Existing fatigue detection works focus on vision and electroencephalography (EEG) based means of detection. However, vision-based approaches suffer from view-blocking or vision distortion problems and EEG-based systems are intrusive, and the drivers have to use/wear the devices with inconvenience or additional costs. In our work, we propose a novel Wi-Fi signals based fatigue detection approach, called WiFind to overcome the drawbacks as associated with the current works. WiFind is simple and (wearable) device-free. It can detect the fatigue symptoms in the vehicle without relying on any visual image or video. By applying self-adaptive method, it can recognize the body features of drivers in multiple modes. It applies Hilbert-Huang transform (HHT) based pattern extract method results in accuracy increase in motion detection mode. WiFind can be easily deployed in a commodity Wi-Fi infrastructure, and we have evaluated its performance in real driving environments. The experimental results have shown that WiFind can achieve the recognition accuracy of 89.6 percent in a single driver scenario.

This letter proposes a novel design of reconfigurable intelligent surface (RIS) to enhance the physical layer security (PLS) in the RIS-aided non-orthogonal multiple access (NOMA) network. Under the design of the RIS, the problem of increasing the number of RIS elements damaging the secrecy performance is solved. Besides, it also ensures that the networks can use traditional channel coding schemes to achieve secrecy. Our results show that the novel design of the RIS is ready for enhancing secrecy performance.

Fog Computing (FC) is a flexible architecture to support distributed domain-specific applications with cloud-like quality of service. However, current FC still lacks the mobility support mechanism when facing many mobile users with diversified application quality requirements. Such mobility support mechanism can be critical such as in the industrial internet where human, products, and devices are moveable. To fill in such gaps, in this paper we propose novel container migration algorithms and architecture to support mobility tasks with various application requirements. Our algorithms are realized from three aspects: 1) We consider mobile application tasks can be hosted in a container of a corresponding fog node that can be migrated, taking the communication delay and computational power consumption into consideration; 2) We further model such container migration strategy as multiple dimensional Markov Decision Process (MDP) spaces. To effectively reduce the large MDP spaces, efficient deep reinforcement learning algorithms are devised to achieve fast decision-making and 3) We implement the model and algorithms as a container migration prototype system and test its feasibility and performance. Extensive experiments show that our strategy outperforms the existing baseline approaches 2.9, 48.5 and 58.4 percent on average in terms of delay, power consumption, and migration cost, respectively.

With the development of drone technology and oblique photogrammetry technology, the acquisition of oblique photogrammetry models and basemap becomes more and more convenient and quickly. The increase in the number of basemap leads to excessively redundant basemap tiles requests in 3D GIS when loading oblique photogrammetry models, which slows down the system. Aiming at improving the speed of running system, this paper proposes a dynamic strategy for loading basemap tiles. Different from existing 3D GIS which loading oblique photogrammetry models and basemap tiles inde-pendently, this strategy dynamically loads basemap tiles depending on different height of view and the range of loaded oblique photogrammetry models. We achieve dynamic loading of basemap tiles by predetermining whether the basemap tiles will be covered by the oblique photogrammetry models. The experimental results show that this strategy can greatly reduce the num-ber of redundant requests from the client to the server while ensuring the user’s visual requirements for the oblique photogrammetric model.

Reconfigurable Intelligent Surface (RIS) technology is emerging as a promising performance enhancement for next-generation wireless networks in terms of the quality of service and radio connectivity. Inspired by the promising potential of RIS technology, we investigate the secrecy performance of the downlink RIS-aided non-orthogonal multiple access network. To characterize the network's performance, the expectation of the new channel statistics for the reflected links with Nakagami-m fading is derived. Furthermore, the performance of the proposed network is evaluated according to the secrecy outage probability (SOP). The closed-form expressions of the SOP are derived. To obtain further insights, the asymptotic SOP and secrecy diversity orders are obtained. Our analytical results demonstrate that: 1) the expectation of channel gain for the reflected links is determined by the number of RISs and the Nakagami-m fading parameters; 2) Both the SOP of user Bob1 and the SOP of user Bob2 are 1 when the number of RISs is sufficiently large; 3) The secrecy diversity orders are affected by the number of RISs and Nakagami-m fading parameters.

The dependent task offloading problem for one single job in edge computing (EC) has drawn attention widely. Unlike most existing approaches that only focus on a single job, we aim to solve the dependent task offloading problem for multiple jobs, which is more general in the real world. To solve this problem, we propose a deep reinforcement learning (DRL) based multi-job dependent task offloading algorithm. Specifically, 1) we model edge nodes, jobs, and tasks in a resource-limited EC scenario, where the dependent tasks of multiple jobs are offloaded to the nodes to be processed. Then we model the task offloading decision as a Markov decision process (MDP) problem to minimize the transmission cost and computation cost. 2) To represent the state space of MDP and to accelerate decision-making in EC, we propose a DRL-based algorithm with the aid of graph convolutional network (GCN) to extract the dependency information of different tasks and then improve the action selection process. 3) We conduct experiments with real-world trace, demonstrating our algorithm outperforms the baseline algorithms 13.78% on average in regarding to offloading cost.

In the context of Edge Computing (EC) and Internet of Things (IoT), numerous tasks are offloaded from mobile users and sensor devices to edge nodes for further processing to reduce delay and solve the problem of insufficient local computation resources. These tasks can be mainly divided into delay-sensitive and computation-oriented tasks. The former tasks depend on the service provided by the container, while the latter tasks are submitted as a batch with task dependencies. Considering the heterogeneity of edge nodes, joint task scheduling can effectively improve resource utilization. However, relatively few researches consider the different characteristics of tasks like container constraints and task dependencies in joint task scheduling in EC. In order to fill in this gap, we propose a deep deterministic policy gradient (DDPG) based online joint task scheduling (OJTS) algorithm. Specifically, 1) We first model the problem of joint scheduling of delay-sensitive and computation-oriented tasks in resource-constrained EC scenario with the goals of maximizing system utility and minimizing system cost (weighted sum of the number and duration of unfinished tasks). 2) Then, we propose a deep reinforcement learning (DRL) algorithm to solve the above problem and make appropriate adjustments to the original network structure according to the scheduling decision. 3) Through validation on real-world trace, OJTS can improve the system utility by 26.0% and overall reward by 51.2% compared with baselines and meet real-time decision-making requirements.

Fog Computing (FC - similarly edge computing) as new computing paradigm can support distributed domain-specific or area-specific applications with cloud-like quality of service (QoS). This promising paradigm thus can find its wide applications in various industrial scenarios and smart cities in which the resource requirements will be divided into peak-hour or non-peak-hour. To deal with such features of applications, a flexible resource allocation approach based on pricing model can be critical for the success of such paradigm. To the best of our knowledge, we have not seen such pricing based resource allocation approach ever been reported for FC scenarios. In this paper, we propose a novel pricing based dynamic resource allocation model through overbooking mechanism, and it is realized through three steps: 1) According to different QoS requirements of user tasks, methods of on-demand billing, daily billing, and auction billing are designed, in which we allow the resource to be overbooked; 2) For auction billing, we design an auction approach including pricing rule and winner determination rule. We prove that our auction approach guarantees individual rationality, computational efficiency, and truthfulness. 3) To overbook as much resource as possible with a high degree of QoS satisfaction of on-demand and daily billing, we overbook the resource based on a resource utilization prediction using neural network and service level agreement violation feedback. In the end, we validate the mechanism with real-world data trace. Experimental results show that our auction approach achieves desirable properties, and our dynamic resource overbooking mechanism maximizes the profit of nodes with a high degree of QoS satisfaction of on-demand and daily billing and a high resource utilization prediction accuracy rate.