In scenarios with extremely harsh channel conditions and severely limited communication resources, the reliability and effectiveness of semantic communication require urgent enhancement to satisfy the increasing demands of 6G technology. To address this issue, we propose an importance prioritized framework that integrates both message importance and feature importance to identify critical semantics for reliable and efficient semantic transmission. Considering service personalization and task intelligence, we analyze the message importance by factoring the receiver’s preferences and the communication tasks requirements. Specifically, a large AI model is introduced to quantify message importance, while an importance-based metric for semantic accuracy is established to evaluate the overall reliability of semantic communication. To safeguard significant messages in harsh channel conditions, an unequal error protection strategy based on message importance is employed. Furthermore, we propose a novel approach for feature importance analysis based on loss variation to accurately identify critical features. A feature importance prediction network is designed for algorithm deployment. Additionally, a semantic compression strategy based on feature importance is utilized to prioritize the transmission of essential features in limited communication resources scenarios. Extensive experimental results demonstrate substantial performance advantages of our framework and methods, especially in low signal-to-noise ratio and communication resource shortages, providing a reliable and efficient solution for semantic communication in adverse communication environments.

Leveraging their mobility and feasibility, Unmanned Aerial Vehicles (UAVs) present a promising solution for assisting covert communications to mitigate the risk of eavesdropping. However, existing studies mainly rely on passive optimization, where the UAV adjusts its transmit parameters according to the channel state, without actively balancing covertness constraints and average system throughput. To solve the above challenge, we propose for the first time a UAV relay-assisted covert communication framework with a buffer. Specifically, we derive the optimal detection threshold for the eavesdropper with mobility and uncertain locations, and obtain a closed-form solution for the lowest detection error probability. To solve the formulated average system throughput maximization problem, we transform the covertness constraint into a tractable analytical form, and obtain the optimal transmit power for both the UAV relay and the friendly UAV jammer. Then, through a rigorous theoretical analysis of upper and lower bounds on average system throughput, we prove the existence of optimal UAV trajectories. Finally, optimal transmission and reception decisions of the UAV relay are derived under covertness and buffer size constraints. Numerical results and theoretical analysis demonstrate the effectiveness of the proposed scheme in terms of average system throughput and covert performance.

Enabled by 6G wireless technologies, Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) create a new dimension for optimizing performance in Uncrewed Aerial Vehicle (UAV) communications through fullspace signal coverage. However, existing research on STAR-RIS-assisted UAV secure communications still faces critical challenges, including the reliance on ideal Channel State Information (CSI) assumptions, amplitude optimization complexity under mode switching protocols, and limited scalability to meet multi-user communication demands. To address these challenges, we propose a robust and secure STAR-RIS-assisted UAV communication approach for a multi-user and multi-eavesdropper scenario. By jointly optimizing user scheduling, transmitting and reflecting coefficients of STAR-RIS, transmit power and flight trajectory of UAV, we aim to maximize the average worst-case achievable secrecy rate. To tackle the non-convexity and coupled decision variables of the formulated problem, we propose an alternating optimization framework, with a Lagrange multiplier method for power allocation, a deterministic model reformulated via S-procedure for CSI uncertainty quantification and robust handling, and a penalty-based double-loop iterative algorithm forcing the phase-shift matrix toward a rank-one solution. Finally, theoretical analysis and simulation results validate the superior secrecy performance of the proposed algorithm over other representative algorithms.

In this paper, we propose a dynamically segmented Intelligent Reflecting Surface (IRS)-assisted Unmanned Aerial Vehicle (UAV) Computing Power Networks (CPNs) with tightly integrated communication and computing power resources. The IRS can be dynamically segmented and allocated to users, with computing resources allocated accordingly to satisfy their delay constraints. Considering the energy limitations of UAVs, we formulate a multi-objective optimization problem to minimize user delay and UAV energy consumption. To solve the problem, we propose a new scheme jointly considering UAVtrajectory,computing power allocation, reflecting element allocation,phase shift, and UAV-userassociation (TCPA) scheme. The phase alignment theory is utilized to determine the IRS phase shift control and decompose the problem into three subproblems based on the coupling of variables. Specifically, we use channel optimal matching to solve the first subproblem to obtain user association decisions. Then, we formulate a computing power communication matching subproblem, and propose a successive convex approximation scheme to solve it. The trajectory subproblem is optimized by a multi-agent deep reinforcement learning-based method. The evaluation results demonstrate that our proposed TCPA achieves high performance in terms of reward and system delay. Additionally, it demonstrates that integrating IRS and CPNs can effectively reduce the total system delay with only a marginal increase in energy consumption.

Due to its capability to ensure communication security and improve spectral efficiency, Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS)-assisted covert communications in Rate-Splitting Multiple Access (RSMA) networks have drawn widespread attention. However, existing studies often overlook the impact of the mobility of wardens and users on long-term covert performance, and lack joint optimization of covert rate and energy consumption. Meanwhile, introducing STAR-RIS significantly increases system parameter dimensionality, rendering traditional methods inefficient. Although reinforcement learning offers advantages, it still faces challenges in training efficiency and resource overhead within high-dimensional state-action spaces. Therefore, this paper proposes a quantum reinforcement learning-based algorithm, named QUERC, for STAR-RIS-assisted covert communications in RSMA networks. Specifically, we first formulate a long-term covert energy efficiency maximization problem under a dynamic environment with mobile users and wardens. Then, we propose a novel hybrid quantum neural network architecture to solve this problem. This architecture integrates a fully connected layer, a variational quantum circuit, an action post-processing mechanism, and a regularized objective, enhancing policy stability and generalization in high-dimensional action spaces. Finally, extensive experiments demonstrate that, compared with TPG, QPG, RIS-NOMA, RPS, and greedy approaches, QUERC algorithm achieves superior average covert energy efficiency and offers significant advantages in computational and inference performance.

Low Earth Orbit (LEO) satellite networks are expected to become a key component of Sixth Generation (6G) communication networks, to relieve the communication burden on ground networks. Driven by the rapid advancement of communication technologies and intelligent applications, dense traffic flow in the Internet of Vehicles (IoV) inevitably leads to a surge in task generation and an increased demand for network resources. The requirement for low latency further intensifies this challenge, making it difficult to rely solely on ground network resources to process tasks efficiently and promptly. Conversely, relying only on satellite networks for task processing results in high costs. Therefore, flexibly integrating LEO satellite links based on real-time traffic conditions, task demands, and the real-time state of ground network resources becomes an effective solution. However, achieving such a goal poses significant challenges in efficient allocation and balance between ground and LEO satellite network resources. Therefore, we propose a dynamic multi-task partial offloading algorithm based on LEO satellite-ground network collaboration to efficiently allocate resources between ground and satellite networks in real time. We first introduce the utility gain as a metric to evaluate task scheduling preference and design an improved iterative algorithm to jointly optimize the offloading ratio and channel allocation to maximize system utility. Finally, based on the real-world dataset of Shanghai (China), we demonstrate the significant advantages of the proposed strategy over representative methods in terms of delay, vehicle satisfaction, and system utility.

The integration of Intelligent Reflecting Surfaces (IRSs) and Unmanned Aerial Vehicles (UAVs) is promising for providing flexible and intelligent communications to users in urban areas. Existing studies are founded either on the complete Line of Sight (LoS) or complete Non-LoS (NLoS) communication scenarios, while ignoring their coexistence. To solve the above challenge in complicated and dynamic communication scenarios, we formulate an average system sum rate maximization problem with the optimization of joint IRS-user association, multi-UAV trajectory optimization, IRS phase shifts and transmit power allocation. Since the highly complex and coupled variables, we propose a Multi-Agent Deep Reinforcement Learning (MADRL)-based scheme to maximize the average system sum rate. First, we derive two composite channel power gains for different communication conditions. Then, phase alignment theory is utilized to obtain optimal phase control. To guarantee long-term optimization, we propose a scheme based on Multi-Agent Proximal Policy Optimization (MAPPO) and Successive Convex Approximation (SCA) method to jointly optimize multi-UAV trajectories, multi-IRS association and transmit power allocation. Finally, experimental results reveal that the proposed MGBA shows considerable advantages in both the convergence speed and the average system sum rate.

Integrated Sensing and Communication (ISAC) can assist Uncrewed Aerial Vehicle (UAV) secure communications by acquiring information about eavesdroppers. However, existing studies have not systematically investigated ISAC beamforming for simultaneously sensing the channel information of ground eavesdroppers, jamming eavesdropping links, and communicating with users, which poses significant challenges in ensuring both sensing accuracy and communication confidentiality. To address this issue, we propose a UAV-ISAC secure communication algorithm to maximize average secrecy rate by jointly optimizing communication and sensing beamforming, user scheduling, sensing time allocation, and UAV trajectory. We address the formulated NP-hard problem by decomposed it into three subproblems. We first relax binary user scheduling and sensing time allocation by a penalty-based successive convex approximation approach. The UAV trajectory is then iteratively optimized while beamforming is designed using semidefinite relaxation, with matrix lifting applied to handle the rank-one constraint. A triple-layer iterative algorithm is constructed by integrating these steps to achieve a suboptimal solution. Numerical experiment results and theoretical analysis validate the superiority of the proposed algorithm in terms of average secrecy rate, convergence and computational complexity.

The flexible deployment of Unmanned Aerial Vehicle (UAV) swarms holds significant potential for low-altitude economy, but their communication security is severely threatened by malicious jamming. Generally, existing anti-jamming methods often overlook multi-user interference in swarm scenarios and fail to exploit the full potential of Intelligent Reflecting Surface (IRS) architectures. To solve the above challenges, we propose for the first time an anti-jamming framework for UAV swarm communications assisted by a Hybrid-IRS-assisted UAV (H-UAV). We jointly optimize the H-UAV’s trajectory, the hybrid IRS’s beamforming and active/passive element allocation of IRSs, and Non-Orthogonal Multiple Access (NOMA) communication strategy under imperfect jammer Channel State Information (CSI), to maximize average system transmission rate while minimizing communication energy consumption. To handle the formulated highly-coupled non-convex problem, we decompose it into three sub-problems. Specifically, we employ Successive Convex Approximation (SCA) to optimize the H-UAV’s trajectories. The IRS beamforming and element allocation are then transformed into a semi-definite programming problem by a designed penalty-based approach. Finally, the NOMA decoding order and power allocation are optimized via a dynamic ordering scheme and an SCA-based algorithm. Compared to existing representative schemes, the proposed framework can achieve higher average transmission rates and lower energy consumption.

Over-the-Air Federated Edge Learning (OTA-FEEL) has emerged as a promising paradigm for collaborative AI model training across heterogeneous edge devices. Despite its advantages in communication efficiency and privacy preservation, OTA-FEEL faces critical challenges, including channel fading, energy constraints of edge devices, and non-i.i.d data distributions. This paper is the first to investigate a joint impact of local data distribution heterogeneity and transmission distortion on model convergence of OTA-FEEL. Accordingly, we analyze the gap between global expected and optimal losses, and formulate the gap minimization problem under long-term energy consumption constraints. To solve this problem, we propose an energy-aware alternating resource allocation algorithm based on Lyapunov optimization framework, jointly addressing transmit power control and device sampling rate selection. Specifically, we transform the non-convex problem based on inverse convex optimization. Then, we employ first-order Taylor expansion to linearize the non-convex constraint, and also develop an iterative framework based on block coordinate descent and successive convex approximation to enable rapid convergence. Extensive simulations under three types of non-i.i.d data distributions validate the effectiveness of the proposed EARA algorithm, which consistently outperforms representative algorithms by achieving test accuracy approaching the theoretical upper bound, while maintaining significantly low energy consumption.

Due to the ability to improve data transmission efficiency and extend coverage, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and autonomous aerial vehicle (AAV)-assisted Internet of unmanned agents (IUAs) has become an attractive paradigm. However, in multi-AAV and multi-STAR-RIS coexistence scenarios, device data transmission and energy harvesting (EH) processes are highly coupled with phase and amplitude optimization of STAR-RISs, which involve a large number of coupled variables that need to be carefully decoupled and processed. Therefore, to address the above challenges, we propose a distributed scheduling algorithm, MINT, for multi-STAR-RIS and multi-AAV assisted IUA, by jointly scheduling AAV trajectories, AAV association variable, charging time allocation, and STAR-RIS coefficient matrices to maximize system energy-efficiency. First, by considering device energy constraint, AAV energy consumption, and time constraints, we formulate the energy-efficiency maximization problem and model it as a Markov decision process. Second, we design a multiagent deep reinforcement learning-based MINT algorithm to solve the formulated optimization problem. Finally, experimental results demonstrate that MINT algorithm outperforms the existing algorithms regarding energy-efficiency, the number of uploaded bits, and convergence performance.

The Industrial Internet of Things (IIoT) faces serious data privacy issues, such as the risk of data leakage during aggregation and transmission. However, existing studies rarely consider data privacy protection from the perspective of 5G core networks (CNs). This article proposes a federation chain-based data privacy protection system for the control plane in 5G CN to facilitate secure and decentralized communications between IIoT devices and other networks components, enhancing data integrity and confidentiality. Using XPRO instrument, 5G CN signaling storm simulation test platform, Free5GC, UERANSIM simulator, and Kali platform, the system simulates and generates realistic control plane data streams in IIoTs. To prevent unauthorized data access, we design an authentication algorithm based on the Bulletproofs zero-knowledge proof technique. Additionally, we implement a data encryption and decryption algorithm based on the Paillier partially homomorphic encryption for user privacy. We design a foundation model-based anomaly flow detection and analysis module to improve the security of the system for anomalous signaling flows. The feasibility and effectiveness of the system are validated on a 5G CN simulation testing platform, and experimental results show that the proposed approach ensures robust and scalable IIoT data privacy protection.

As distributed edge intelligence (DEI) advances within the Internet of Electric Vehicles (IoEV), the deployment of mobile charging stations (MCSs) offers a solution to the uneven distribution of fixed charging stations (FCSs), enhancing energy access in remote areas. However, MCS faces the problem of passive scheduling, limiting effective resource utilization and prolonging charging waiting time. This article proposes a hybrid charging model algorithm (HCMA) to address the above challenge, particularly in regions with limited available FCS. We first formulate a multiobjective optimization problem to optimize electric vehicle (EV) charging modes, volumes, and MCS scheduling arrangements. Then, we decompose the original problem into two subproblems. By determining EV charging locations and EV charging mode, the two subproblems are solved, respectively. Finally, simulations based on real-world data demonstrate that HCMA performs better compared to several representative methods, including random working, ARMM, and RBA, in terms of average charging waiting time, extra traveling distance, average unit price of energy, and number of MCS schedules.

With the explosive growth in Industrial Internet of Things (IIoT) devices, the volume of multimedia data in the field of flexible manufacturing has also increased significantly in recent years, especially the vast amount of unlabelled image data. Image annotation provides machines with a more natural way to interact with users, enhancing the level of intelligence in IIoT flexible manufacturing. This article proposes a multifeature fusion multikernel learning image annotation method to tackle imbalanced label distribution, image weak labeling, and varying representational abilities of features. Initially, oversampling techniques with synthetic minority class samples address the influence of minority classes, while a label enhancer extends label vectors to overcome the influence of weak labeling. Subsequently, the integration of traditional visual features with deep features based on multikernel learning is investigated to enhance feature representation capability. This approach combines complementary information from multiple features, establishing intrinsic connections between images and annotated keywords. Experimental evaluations are conducted on three benchmark datasets, comparing our method with several classical methods. Evaluation results demonstrate that our proposed method captures semantic information more accurately and comprehensively. By effectively accomplishing automatic image annotation, our method can enhance human-machine-interaction to improve the level of intelligence in IIoT flexible manufacturing.

Large AI models, connected to terminal devices via high-speed mobile communication networks, enable task collaboration and resource sharing, forming an intelligent framework for the Symbiotic Internet of Things (SIoT) paradigm in industry applications. Despite large language models hold significant potential for psychological intervention, their emotional interaction capabilities remain limited. This paper introduces a SIoT framework for psychological intervention and proposes an emotion-enhanced human-machine interaction architecture incorporating behavioral information captured by IoT devices. The system leverages ubiquitous sensing devices such as cameras and microphones, along with technologies like speech recognition and generation, as well as hyper-realistic digital humans, to create a natural interaction interface. Additionally, we introduce the end-of-utterance detection method and the behavior pattern control algorithm to facilitate smoother and more goal-oriented conversations. The proposed methods and prototype system have been validated through subjective and objective experiments, with results demonstrating their feasibility and suggesting that this approach could become one of the primary forms of humanmachine interaction for psychological intervention in the future.

To address the problem of real-time path planning for autonomous aerial vehicle (AAV) formations in complex environments, this article proposes a hybrid path planning control system tailored for AAV-enabled Internet of Things (IoT) utilizing near-field communication (NFC) among them. We model the AAV formation problem as an undirected graph, and first design a virtual leader-follower method based on the consistency principle of distributed systems. After that, we present an innovative hybrid path planning method (named ASAP-A*), combining A* and artificial potential field methods. It effectively reduces redundant points in the path and optimizes the AAV trajectory by B-spline smoothing to meet AAV trajectory requirements. Finally, we construct a user-friendly AAV swarms formation control system based the proposed methods, tackling the complexity of operating existing systems, and verifying the effectiveness of our solutions.

In response to the critical need for rapid post-disaster assessments, this article introduces an innovative application of artificial intelligence (AI) in unmanned aerial vehicles (UAVs) for disaster relief. A lightweight distributed learning algorithm (namely, YO-FR), is designed to enable multiple UAV agents to share and process environmental data, highlighting the importance of data and knowledge-empowered distributed learning. Moreover, we create a real-world mini-data set collected by UAVs for post-disaster road defects (mini-UPRDs), followed by a data enhancement technology to facilitate feature extraction and promote knowledge-driven learning. The viability of YO-FR is underscored by its enhanced detection precision and processing speed, as evidenced by its performance on the enhanced mini-UPRD data set, surpassing that of existing algorithms. By implementing AI algorithms on UAV platforms, this research offers a theoretical and practical foundation for the practical deployment of IUA in critical application areas, such as emergency management and disaster response.

Unmanned aerial vehicle (UAV) assisted communication has emerged as a powerful technology for reliable and flexible emergency communications (e.g., earthquakes, hurricanes and floods), especially when the mobile infrastructure is seriously damaged. UAV assisted mobile edge computing (UAV-MEC) system can be deployed in the natural disaster area as communication relay or air mobile base stations to resume communication and provide computing resources for the users in disaster areas. However, the optimized resource allocation performance of UAV-MEC system can be further guaranteed with the human-fusion decision making. In this paper, we construct an emergency human-machine fusion UAV-MEC system consisting of multiple UAVs equipped with computing resources, and the human-machine decision makings are fused for UAV deployment. In order to solve the resource allocation problem of human-machine fusion UAV-MEC system, we establish an human-machine deep integration model for UAV-MEC system, and the UAVs are dispatched reasonably through human-machine fusion decision makings to maintain efficient communication in emergency communication areas. To minimize task latency and improve the computation efficiency in emergency human-machine fusion UAV-MEC system, we consider the number of dispatched UAVs, deployment plans, flight plans, and simultaneously optimize the task allocation scheme, priority order, and task offloading ratio. We propose a reinforcement learning framework combined with evolutionary algorithms, which is named as multi-agent Q-net enhanced cooperative genetic algorithm (MQCGA), for resource allocation of UAV. Based on neural network forecasts, the greedy rate during training processing can be dynamically controlled, and the learning ability of different agents can be strengthened. Simulation experiments are conducted to evaluate the proposed framework, and the results show that our proposed MQCGA algorithm is significantly superior to other algorithms in terms of latency and energy consumption. Note to Practitioners—With the development of MEC and the popularity of UAVs, the potential of UAV-assisted MEC draw much attention from industrial field. Considering the lack of communication capabilities in a certain area in an unexpected situation, UAVs can be quickly deployed to corresponding locations and provide computing services. In this paper, an UAV-MEC system that integrates human-machine decision-making for emergency communication situations, named human-machine fusion UAV-MEC system, is considered. The system divides the scene into regions, models users and UAVs, and provides detailed deployment schemes, maximizing the practicality and applicability of the scene. In order to improve the communication efficiency in the case of emergency communication, this paper proposes a new resource scheduling algorithm and adds human-machine decision-making to enable UAVs to continuously provide efficient services for a certain area. The experimental results provide practitioners with a theoretical basis, such as the task completion time, UAV energy consumption and computing resource scheduling. Applying the system to actual scenarios also requires two preconditions of the system, one is the information collected by the large UAV, and the other is the communication among the UAVs. These two preconditions facilitate the human-machine fusion UAV-MEC system deployment in practical applications.

In the academic research field, identifying suitable collaboration partners and selecting appropriate journals for publication remain significant challenges for researchers. Existing academic recommendation systems often fail to provide personalized, accurate, and efficient recommendations. To address these issues, this article proposes an innovative academic recommendation system that incorporates multisimilarity features. By constructing an academic collaboration network and optimizing the transfer probability matrix to reflect scholars’ relationships, the system captures scholars’ collaborative tendencies and potential connections. A key innovation of this work is the proposed Muls-IRWR algorithm, which improves traditional random walk with restart (RWR) by integrating various similarity measures. Using a subset of the DBLP citation data, we develop our academic collaboration network to calculate precise scholar similarities. Experimental results demonstrate that our system significantly outperforms existing models in terms of recommendation accuracy and efficiency, highlighting its practical value and potential for use in real-world academic applications.

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we proposeSR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposedSR-CL. Compared to benchmark methods, theSR-CLachieves superior convergence and delay performance under various scenarios.

The development of Internet of Things (IoT) technology has led to the emergence of a large number of Intelligent Sensing Devices (ISDs). Since their limited physical sizes constrain the battery capacity, wireless powered IoT networks assisted by Unmanned Aerial Vehicles (UAVs) for energy transfer and data acquisition have attracted great interest. In this paper, we formulate an optimization problem to maximize system energy efficiency while satisfying the constraints of UAV mobility and safety, ISD quality of service and task completion time. The formulated problem is constructed as a Constrained Markov Decision Process (CMDP) model, and a Multi-agent Constrained Deep Reinforcement Learning (MCDRL) algorithm is proposed to learn the optimal UAV movement policy. In addition, an ISD-UAV connection assignment algorithm is designed to manage the connection in the UAV sensing range. Finally, performance evaluations and analysis based on real-world data demonstrate the superiority of our solution.

In this paper, we focus on computation-aware offloading for executing deep neural network (DNN) inference tasks in a mobile edge computing (MEC) system. To cope with the challenges of insufficient wireless resources during task offloading, we resort to semantic communications (SCs), through which the users can offload the compressed task data to the edge server for remote execution. Specifically, we establish the relationship between the compression ratio and computation ratio for different DNN tasks. To achieve energy-efficient offloading, we formulate an optimization problem to minimize the energy consumption of all users by jointly optimizing the compression ratio, computation allocation, uploading time, and DNN layer selection. We first consider a special case with the preconfigured time scheduling and derive closed-form solutions to computation allocation and offloading time, which yield a threshold-based structure determined by users’ channel conditions and local computation consumption. Inspired by the characteristics of these optimal solutions, a general low-complexity iterative algorithm is then designed to solve the original non-convex problem. Simulation results demonstrate that our proposed SC-based computation -offloading scheme can substantially reduce users’ energy consumption compared to the conventional offloading and full offloading, especially with scarce wireless resources.

Age of Information (AoI) has received much attention from researchers as the latest metric to quantify the freshness of data. It is necessary to jointly schedule Unmanned Aerial Vehicles (UAVs) and sensors to reduce the system AoI in wireless powered Internet of things. However, constraints on UAV flight time, charging time, and data collection time, as well as constraints of half-duplex hardware for sensors make it difficult to efficiently jointly schedule UAVs and sensors by traditional methodes. Thus, we design a multi-agent Deep Reinforcement Learning (DRL)-based UAV cooperative scheduling algorithm that jointly optimizes sensor charging time, UAV trajectories and sensor update scheduling with AoI as the optimization objective. Initially, we define the AoI minimization problem, portraying it as a Markov decision process. Then, we design a multi-agent DRL algorithm founded on factorizing value functions to address this issue. Finally, experiments demonstrate that the MAPLE algorithm can effectively coordinate the scheduling of UAVs and sensors.

With the scale of Internet of Things (IoT) continually expanding, the topology is growing rapidly and the probability of cascading collapse due to node failures or malicious attacks is increasing. The decrease in the Quality of Service (QoS) of IoT could be mitigated by robust topology. Existing optimization strategies usually use heuristic algorithms to enhance topology robustness. However, when the scale of topology is large, these algorithms involve a significant amount of iterative searching for the optimal solution, which is time-consuming and prone to getting stuck into local optimum. To tackle this situation, this study introduces arithmetic encoding and proposes a novel probability-based robust topology generation model that can quickly generate IoT robust topology. We losslessly compress robust topologies using arithmetic encoding and extract their features. Based on the extracting features, we design a unique probability-based topology generation approach that avoids the time overhead of iterative calculations. Experimental results demonstrate that the proposed solution in this paper can construct robust topologies in less time for different network scales.

In existing graph data, the connection relationships often exhibit uniform weights, leading to the model aggregating neighboring nodes with equal weights across various connection types. However, this uniform aggregation of diverse information diminishes the discriminability of node representations, contributing significantly to the over-smoothing issue in models. In this paper, we propose the Flexible Graph Neural Diffusion (FGND) model, incorporating latent class representation to address the misalignment between graph topology and node features. In particular, we combine latent class representation learning with the inherent graph topology to reconstruct the diffusion matrix during the graph diffusion process. We introduce the sim metric to quantify the degree of mismatch between graph topology and node features. By flexibly adjusting the dependency level on node features through the hyperparameter, we accommodate diverse adjacency relationships. The effective filtering of noise in the topology also allows the model to capture higher order information, significantly alleviating the over-smoothing problem. Meanwhile, we model the graphical diffusion process as a set of differential equations and employ advanced partial differential equation tools to obtain more accurate solutions. Empirical evaluations on five benchmarks reveal that our FGND model outperforms existing popular GNN methods in terms of both overall performance and stability under data perturbations. Meanwhile, our model exhibits superior performance in comparison to models tailored for heterogeneous graphs and those designed to address oversmoothing issues.

With the rapid development of ubiquitous networks and smart devices, artificial intelligence-based unmanned aerial vehicles (UAVs) are drawing more and more attention. The rise in popularity of deep neural networks (DNNs) has spawned a research effort to deploy various kinds of DNN models on vehicles. They have been used to accomplish complicated vehicular tasks and enable the construction of intelligent vehicular networks. Despite the promising prospects, how to train and run them on resource-limited and hardware-constrained UAVs faces huge challenges. Furthermore, the tradeoff between accuracy and latency needs to be considered while reducing the computational cost of DNN training.

Ground-air cooperative package distribution is a promising delivery method, especially during Corona virus Disease 2019. It can extend the coverage of vehicles by exploring the flexibility of unmanned aerial vehicles (UAVs), expand the distribution of vehicles and reduce carbon emissions. Most existing studies focus on their trajectory optimization, while often overlooking their coordination for global information, and the complexity and reliability of collaborative delivery problem. To address the above issues, we first formulate an optimization problem to minimize the service cost of both UAVs and vehicles. To ensure service reliability, the constraints of UAVs during takeoff, service, and landing phases are comprehensively considered. We then propose a lightweight reinforcement learning solution to minimize the flight distance of UAVs and the number of required vehicles. Finally, theoretical analysis and performance evaluations show that compared with other representative algorithms, the designed algorithm has advantages in terms of robustness, effectiveness and stability.

The emerging Space-Air-Ground Integrated Networks (SAGIN) empower Mobile Edge Computing (MEC) with wider communication coverage and more flexible network access. However, the fluctuating user traffic and constrained computing architecture seriously hinder the Quality-of-Service (QoS) and resource utilization in SAGIN. Existing solutions generally depend on prior knowledge or adopt static resource provisioning, lacking adaptability and resulting in serious system overheads. To address these important challenges, we propose THOAS, a novel Traffic-aware lightweight Hierarchical Offloading framework towards Adaptive Slicing-enabled SAGIN. First, we innovatively separate SAGIN into Communication Access Platforms (CAPs) and Computation Offloading Platforms (COPs). Next, we design a new self-attention-based prediction method to accurately capture the traffic changes on each platform, enabling adaptive slice resource adjustments. Finally, we develop an improved deep reinforcement learning method based on proximal clipping with dynamic confidence intervals to reach optimal offloading. Notably, we employ knowledge distillation to compress offloading policies into lightweight networks, enhancing their adaptability in resource-limited SAGIN. Using real-world datasets of user traffic, extensive experiments are conducted. The results show that the THOAS can accurately predict traffic and make adaptive resource adjustments and offloading decisions, which outperforms other benchmark methods on multiple metrics under various scenarios.

To support the running of human-centric metaverse applications on mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Mobile Edge Computing (WPMEC) is promising to compensate for limited computational capabilities and energy supplies of mobile devices. The high-speed computational processing demands and significant energy consumption of metaverse applications require joint resource scheduling of multiple devices and UAVs, but existing WPMEC solutions address either device or UAV scheduling due to the complexity of combinatorial optimization. To solve the above challenge, we propose a two-stage alternating optimization algorithm based on multi-task Deep Reinforcement Learning (DRL) to jointly allocate charging time, schedule computation tasks, and optimize trajectory of UAVs and mobile devices in a wireless powered metaverse scenario. First, considering energy constraints of both UAVs and mobile devices, we formulate an optimization problem to maximize the computation efficiency of the system. Second, we propose a heuristic algorithm to efficiently perform time allocation and charging scheduling for mobile devices. Following this, we design a multi-task DRL scheme to make charging scheduling and trajectory design decisions for UAVs. Finally, theoretical analysis and performance results demonstrate that our algorithm exhibits significant advantages over representative methods in terms of convergence speed and average computation efficiency.

Due to the epidemic COVID-19 spread and Intelligent Transportation System (ITS) development, investigators are now to conduct their research over the generated Transportation Big Data (TBD) in many critical areas, such as medical supplies, food supplies, as well as logistics supplies. At present, Vehicular Edge Computing (VEC) is an emerging paradigm to integrate resources from vehicles, road-site units, base stations, and cloud center to promote the performance of TBD tasks scheduling and running. In this paper, we design a three-layered TBD task processing architecture with a federated learning mechanism for credit priority-based task scheduling and running. In our design, we consider the efficiency of task offloading and misbehavior attack problems simultaneously. We propose a vehicular federated learning framework combined with Multi-Layer Perceptron (MLP) credit measurement, which can preserve the privacy of vehicles and obtain the related features for vehicular credit prediction. We also propose a task offloading algorithm to solve the optimization problem for credit priority task offloading between edge computing servers and vehicles. The proposed solution can prioritize tasks and assign sufficient resources for reliable and active task requesters. Experimental results expose that the proposed mechanism outperforms the state-of-the-art solutions when considering efficiency and attack simultaneously for TBD tasks scheduling and running.

Due to the revolution of communication technology, the rapidly increasing number of mobile devices in edge networks generates various real-time service requests, requiring a considerable volume of heterogeneous resources all the time. However, edge devices with limited resources cannot afford substantial learning cost, while migrating services requires heterogeneous resources, especially for dynamic networks. To address these issues, we first establish a cooperative service migration framework and formulate a bi-objective optimization problem to optimize service performance and cost. By analyzing the optimal migration ratio of service cooperative migration, we propose an offline expert policy based on global states to provide optimal expert demonstrations. To realize real-time service migration based on observable states, we design a lightweight online agent policy to imitate expert demonstrations and leverage meta update to accelerate the model transfer. Experimental results show that our algorithm is exceptional in training cost and accuracy, and has significant superiors in multiple metrics such as the service latency and payment under different workloads, compared to other representative algorithms.

Driven by the increasing computational demand of real-time mobile applications, Unmanned Aerial Vehicle (UAV) assisted Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges and constructing high-throughput line-of-sight links for ground users. Most exsiting studies consider simplified scenarios, such as a single UAV, Service Provider (SP) or service type, and centralized UAV trajectory control. In order to be more in line with real-world cases, we intend to achieve distributed trajectory control of multiple UAVs in UAV-assisted MEC networks with multiple SPs providing differentiated services. Our objective is to minimize the short-term computational costs of ground users and the long-term computational cost of UAVs, simultaneously based on incomplete information. We first solve the formulated problem by reaching the Nash Equilibrium (NE) of the game among SPs based on complete information. We further formulate a Markov game model and propose a Deep Reinforcement Learning (DRL)-based UAV trajectory optimization algorithm, where only local observations of each UAV are required for each SP's flying action execution. Theoretical analysis and performance evaluation demonstrate the convergence, efficiency, scalability, and robustness of our algorithm compared with other representative algorithms.

Current mobile networks are facing dramatic growth in wireless traffics due to the prosperity of streaming media services. Cooperative edge caching, enabling multiple edge nodes to cache and share contents by exploiting the spatial/temporal user request differentiation, is regarded as a promising method to enhance Quality of Experience (QoE). However, frequent content sharing between BSs consumes operation cost such as the usage of cross-edge bandwidth and energy consumption. Therefore, new challenges incurred by performance-cost trade-off arise. In this paper, we propose a user preference-aware content caching and migration (PACM) scheme for video content delivery in a cost-efficient edge network. In this scheme, the dynamic user request preference and the long-term content migration cost budget are considered for content placement and delivery. To navigate a good performance-cost trade-off, we formulate the content caching and migration to be a long-term optimization problem. Then, the Lyapunov optimization method is used to decompose the problem into a series of real-time optimizations. As the decomposed problem is NP-hard, we design a novel collective reinforcement learning (CRL) algorithm that can realize online efficient decision-making by interacting with training experience. Simulation results show that the CRL algorithm has a high convergence rate and the proposed scheme can achieve quasi-optimal performance in terms of user-perceived latency, cache hit rate, and video stalling rate.

Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs, whereas it is extremely challenging for joint multi-IRS multi-user association in UAV communications with constrained reflecting resources and dynamic scenarios. To address the aforementioned challenges, we propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation to maximize system energy efficiency. We first propose an inverse soft-Q learning-based algorithm to optimize multi-IRS multi-user association. Then, successive convex approximation (SCA) and Dinkelbach-based algorithm are leveraged to optimize UAV trajectory followed by the optimization of SIC decoding order scheduling and power allocation. Finally, theoretical analysis and performance results show significant advantages of the designed algorithm in convergence rate and energy efficiency.

Graph convolutional neural network (GCN) is a powerful deep learning framework for network data. However, variants of graph neural architectures can lead to drastically different performance on different tasks. Model comparison calls for a unifying framework with interpretability and principled experimental procedures. Based on the theories from graph signal processing (GSP), we show that GCN’s capability is fundamentally limited by the uncertainty principle, and wavelets provide a controllable trade-off between local and global information. We adapt wavelet denoising filters to the graph domain, unifying popular variants of GCN under a common interpretable mathematical framework. Furthermore, we propose WaveThresh and WaveShrink which are novel GCN models based on proven denoising filters from the signal processing literature. Empirically, we evaluate our models and other popular GCNs under a more principled procedure and analyze how trade-offs between local and global graph signals can lead to better performance in different datasets.

With the rapid advance of Internet of Things (IoT), it is difficult for cloud-centric computing to meet the requirements of low latency and ease of use. As an open and distributed system, edge computing integrates computing, networking, storage, and applications. It provides intelligent services on the edge of an IoT. The edge network is composed of various wireless and wired networks, and the computing and storage resources of edge nodes are limited. These conditions make the edge network expose to a variety of cyber attacks. Additionally, it is difficult for an IoT edge node to support large-scale network data collection and detection for IoT security. Although big data-enabled intrusion detection algorithms can ensure the high accuracy of intrusion detection systems, it is stressful for resource-limited edge nodes to implement those algorithms in IoT. Motivated by these challenges, we propose an intelligent intrusion detection algorithm implemented by big data mining based on a fuzzy rough set, generative adversarial network (GAN), and convolutional neural network (CNN). In our method, we first propose a fuzzy rough set-based algorithm to perform feature selection for big data via IoT. Then, we take advantage of the efficient feature extraction capabilities of CNN for implementing intrusion detection based on selected features. Furthermore, after combining CNN and GAN, we propose an intelligent algorithm to realize intrusion detection in a variety of scenarios. Finally, the proposed method is compared with existing methods for evaluation. Simulation results show that our method has up to 4% higher accuracy than existing methods.

Driven by the increasing demand of real-time mobile application processing, Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges. In this paper, we investigate an MEC network enabled by Unmanned Aerial Vehicles (UAV), and consider both the multi-user computation offloading and edge server deployment to minimize the system-wide computation cost under dynamic environment, where users generate tasks according to time-varying probabilities. We decompose the minimization problem by formulating two stochastic games for multi-user computation offloading and edge server deployment respectively, and prove that each formulated stochastic game has at least one Nash Equilibrium (NE). Two learning algorithms are proposed to reach the NEs with polynomial-time computational complexities. We further incorporate these two algorithms into a chess-like asynchronous updating algorithm to solve the system-wide computation cost minimization problem. Finally, performance evaluations based on real-world data are conducted and analyzed, corroborating that the proposed algorithms can achieve efficient computation offloading coupled with proper server deployment under dynamic environment for multiple users and MEC servers.

Blockchain has been leveraged to secure transactions for the m-commerce. However, the intensive computation in the mining process restricts the participation of mobile devices. Currently, some studies have deployed edge computing services to support the mining process, where edge servers managed by one Service Provider (SP) are considered. This paper investigates a more practical scenario with multiple SPs, where servers managed by different SPs have distinct capacities and prices, making miners’ offloading decisions rather complicated. To tackle the above challenges, we consider task offloading, block propagation and miner mobility comprehensively to maximize utilities of miners. Specifically, we first formulate a Markov game, and then design a learning-based offloading algorithm for off-chain computation, where a novel learning model is constructed by integrating Deep Reinforcement Learning (DRL) and Mean Field Theory (MFT) to guarantee a Nash equilibrium. Different from existing studies, each miner merely needs to respond to the average effect from others in our system, insteading of knowing policies of others. Finally, both theoritical and performance results show that our designed algorithm has superiority on average miner utilities and algorithm convergence time compared with other representative algorithms.

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed$\epsilon$-Nash equilibrium. Performance results show that our algorithm has significant superiority in terms of average profits, utilities and execution time compared with other representative algorithms.

Vehicular Ad-Hoc Networks (VANETs), as the cru-cial support of Intelligent Transportation Systems (ITS), have received a great attention in recent years. Network traffic prediction is useful for network management and security in VANETs, such as network planning and anomaly detection. Due to the movement of nodes, the traffic flow in VANETs consists of a great number of irregular fluctuations, which is the main challenge for network traffic prediction. This paper proposes a novel algorithm, which combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic prediction. We use DQN to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on two real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.

Recently, the rapid developments in the Internet of Medical Things (IoMT) enable smart devices to generate and transmit massive personal electronic medical records (EMRs). However, there are many sensitive attributes in an EMR, which could be accessed by external or internal unauthorized users for malicious purposes. In this article, we present a triple subject purpose-based access control (TS-PBAC) model, which is compatible with a blockchain-enabled reliable transaction network, and design an individual-centric security and privacy-preserving mechanism for access control with different purposes and roles in IoMT scenarios. Specifically, we design hierarchical purpose tree (HPT) and related policies to guarantee the legality of an external user with different purposes. To improve the privacy for sensitive attributes against an internal attacker, we design a local differential privacy (LDP)-based policy and role-based access control scheme in an edge computing paradigm to grant fine-granularity rights for authorized users. In addition, we introduce mutual evaluation metrics to evaluate data quality from a patient-and-medical-service level in an open anonymous network, only using logs kept in the blockchain. We test our approach by real-world EMRs with 100000 patients. The experimental results show that the proposed privacy-preserving scheme can better protect patient’s privacy than traditional access control policies in IoMT environments, and can make reliable and stable access control decisions between data publishers and data requesters with different purposes.

The emerging applications for smart cities intend to promote the quality of citizens’ life. Among them, ubiquitous user connectivity and real-time computation offloading are significant for the ever-increasing requirements of delay-sensitive and mission-critical applications. By integrating human social behaviors (such as relationship, similarity, community, and social ties) with physical Internet of Things (IoT) systems, social IoT systems are promising to provide ubiquitous connectivity among users. As the applications of social IoT systems are transferring from information dissemination to user entertainment (such as image identification, online games, and augmented reality), computation offloading is significant to reduce the execution delay of applications.

Network virtualization techniques are promising to overcome the obstacle of applying and expanding costly traditional networks in the industrial Internet of things (IIoT). Artificial intelligence (AI)-enhanced distributed resource management in edge networks has aroused researchers’ widespread attention. However, dynamically arrived service requests and limited edge resources complicate the service scheduling issue. In this article, we establish a dynamic network virtualization technique enabled service function chain (SFC) orchestration framework in IIoT, formulate the joint optimization problem to maximize total utility and decompose it into two subproblems, i.e., SFC selection and dynamic SFC orchestration. A dynamic orchestration of SFC (DOS) scheme, consisting of resource-aware matching algorithm and averaged multistep double deep q-network algorithm, is designed to embed SFC requests distributedly on the optimal virtualized network function chains. At last, we validate the superiority of our proposed DOS scheme by experimental results.

With the rapid advance of industrial Internet of Things (IIoT), to provide flexible access for various infrastructures and applications, software-defined networks (SDNs) have been involved in constructing current IIoT networks. To improve the quality of services of industrial applications, network traffic prediction has become an important research direction, which is beneficial for network management and security. Unfortunately, the traffic flows of the SDN-enabled IIoT network contain a large number of irregular fluctuations, which makes network traffic prediction difficult. In this article, we propose an algorithm based on multitask learning to predict network traffic according to the spatial and temporal features of network traffic. Our proposed approach can effectively obtain network traffic predictors according to the evaluations by implementing it on real networks.

The digitization of Electronic Medical Record (EMR) provides potential access to a wealth of medical information, but also presents new challenges in privacy-preserved EMR exchanging and sharing. In this paper, we propose a blockchain-based smart healthcare system with fine-grained privacy protection for reliable data exchanging and sharing among different users. We design a blockchain-enabled dynamic access control framework combined with Local Differential Privacy (LDP) strategies to provide the attribute-based privacy protection in transaction workflow. We design four types of smart contracts in the framework to meet the requirements of anonymous transaction, dynamic access control, beneficial matching decision, and evaluation of published data in an open network. To satisfy fine-grained privacy protection, we classify sensitive attributes of EMRs into different levels and set differential privacy budgets to randomize attributes before data publishing. Also, we design data quality function to depict the disturbance incurred by LDP-based privacy preferences at the requester view, and present appropriate many-to-many matching decisions among participants for beneficial transactions. Finally, we develop a prototype system and test our approach using 200,000 real-world EMRs. Experimental results show that the proposed privacy-preserved scheme can make stable and reliable transactions between EMR publishers and requesters. The prototype system achieves individual-centric privacy configuration at the patient site, while providing error-guaranteed statistics at the requester site. Additionally, the access control policies, logs of anonymous transaction are kept in the blockchain to provide system-level traceability.

It is critical to reduce the operating cost of shared buses for bus companies and improve the user experience of passengers. However, existing studies focus on either bus scheduling or route planning, which cannot accomplish the above mentioned goals concurrently. In this paper, we construct a joint bus scheduling and route planning framework to maximize the number of passengers, minimize the total length of routes and the number of required buses, as well as guarantee good user experience of passengers. First, we establish a system model based on a real-world scenario and formulate a multi-objective combinational optimization problem. Then, based on the extracted traffic topology of urban traffic networks and the generated candidate line set, we propose an offline algorithm to cope with the similar passenger flow distributions, e.g., morning or evening peak of every day. In order to cope with dynamic real-time passenger flows, an online algorithm is designed. Experiments are carried out based on real-word scenarios. The results show that the proposed algorithms can greatly reduce the operating cost of bus companies and guarantee good user experience based on real-world scheduling data in comparison with several existing methods.

Mobile social networks include a large number of social members who forward messages cooperatively. However, spammers post links to viruses and advertisements, or follow a large number of users, which produces many misleading messages in mobile social networks. In this paper, we propose an adaptive social spammer detection (ASSD) model. We build a spammer classifier by using a small number of labeled patterns and some unlabeled patterns. The prediction accuracy is high compared with some conventional supervised learning methods. Moreover, the time and energy required to label the identity of social members are reduced by applying ASSD. Because social spammers frequently change their behavior to deceive the spammer detection model, an incremental learning method is designed to update the spammer detection model adaptively, without retraining. We evaluate ASSD by comparing it with other supervised and semi-supervised machine learning methods using the Social Honeypot Dataset. Experimental results show that the proposed model outperforms the baseline methods in terms of recall and precision. Additionally, ASSD maintains a high detection accuracy by adaptively updating the model with newly generated social media data.

A variety of novel mobile applications are developed to attract the interests of potential users in the emerging 5G-enabled vehicular networks. Although computation offloading and task scheduling have been widely investigated, it is rather challenging to decide the optimal offloading ratio and perform adaptive task scheduling in high-dynamic networks. Furthermore, the scheduling policy made by the network operator may be violated, since vehicular users are rational and selfish to maximize their own profits. By considering the incentive compatibility and individual rationality of vehicular users, we present POETS, an efficient partial computation offloading and adaptive task scheduling algorithm to maximize the overall system-wide profit. Specially, a two-sided matching algorithm is first proposed to derive the optimal transmission scheduling discipline. After that, the offloading ratio of vehicular users can be obtained through convex optimization, without any information of other users. Furthermore, a non-cooperative game is constructed to derive the payoff of vehicular users that can reach the equilibrium between users and the network operator. Theoretical analyses and performance evaluations based on real-world traces of taxies demonstrate the effectiveness of our proposed solution.

Intelligent Transportation System (ITS) is critical to cope with traffic events, e.g., traffic jams and accidents, and provide services for personal traveling. However, existing researches have not jointly considered the user data safety, utility and system latency comprehensively, to the best of our knowledge. Since both safe and efficient transmissions are significant for ITS, we construct a blockchain-enabled crowdsensing framework for distributed traffic management. First, we illustrate the system model and formulate a multi-objective optimization problem. Due to its complexity, we decompose it into two subproblems, and propose the corresponding schemes, i.e., a Deep Reinforcement Learning (DRL)-based algorithm and a DIstributed Alternating Direction mEthod of Multipliers (DIADEM) algorithm. Extensive experiments are carried out to evaluate the performance of our solutions, and experimental results demonstrate that the DRL-based algorithm can legitimately select active miners and transactions to make a satisfied trade-off between the blockchain safety and latency, and the DIADEM algorithm can effectively select task computation modes for vehicles in a distributed way to maximize their social welfare.

Vehicular edge computing (VEC) is a promising paradigm based on the Internet of vehicles to provide computing resources for end users and relieve heavy traffic burden for cellular networks. In this paper, we consider a VEC network with dynamic topologies, unstable connections and unpredictable movements. Vehicles inside can offload computation tasks to available neighboring VEC clusters formed by onboard resources, with the purpose of both minimizing system energy consumption and satisfying task latency constraints. For online task scheduling, existing researches either design heuristic algorithms or leverage machine learning, e.g., deep reinforcement learning (DRL). However, these algorithms are not efficient enough because of their low searching efficiency and slow convergence speeds for large-scale networks. Instead, we propose an imitation learning enabled online task scheduling algorithm with near-optimal performance from the initial stage. Specially, an expert can obtain the optimal scheduling policy by solving the formulated optimization problem with a few samples offline. For online learning, we train agent policies by following the expert’s demonstration with an acceptable performance gap in theory. Performance results show that our solution has a significant advantage with more than 50 percent improvement compared with the benchmark.

Age of Information (AoI) has become an important metric to evaluate the freshness of information, and studies of minimizing AoI in wireless networks have drawn extensive attention. In mobile edge networks, changes in critical levels for distinct information is important for users’ decision making, especially when merely partial observations are available. However, existing research has not yet addressed this issue, which is the subject of this paper. To address this issue, we first establish a system model, in which the information freshness is quantified by changes in its critical levels. We formulate Age-of-Critical-Information (AoCI) minimization as an optimization problem, with the purpose of minimizing the average relative AoCI of mobile clients to help them make timely decisions. Then, we propose an information-aware heuristic algorithm that can reach optimal performance with full obsevations in an offline manner. For online scheduling, an imitation learning-based scheduling approach is designed to choose update preferences for mobile clients under partial observations, where policies obtained by the above heuristic algorithm are utilized for expert policies. Finally, we demonstrate the superiority of our designed algorithm from both theoretical and experimental perspectives.

Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithm, network planning, and anomaly and intrusion detection. This article studies the problem of end-to-end network traffic prediction in IoV backbone networks, and proposes a deep learning-based method. The constructed system considers the spatio-temporal feature of network traffic, and can capture the long-range dependence of network traffic. Furthermore, a threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic dataset.

A novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC) emerges recently, which integrates Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies. It enables mobile clients to both extend their computing capacities by task offloading, and charge from edge servers via energy transmission. Existing studies generally focus on the centralized design of task scheduling and energy charging in WP-MEC networks. To meet the decentralization requirement of the near-coming 6G network, we propose an online learning algorithm for computation offloading in WP-MEC networks with a distributed execution manner. Specifically, we first define the delay minimization problem by considering task deadline and energy constraints. Then, we transform it into a primal-dual optimization problem based on the Bellman equation. After that, we design a novel neural model that learns both offloading and time division decisions in each time slot to solve the formulated optimization problem. To train and execute the designed algorithm distributivity, we form multiple learning models decentralized on edge servers and they work coordinately to achieve parameter synchronization. At last, both theoretical and performance analyses show that the designed algorithm has significant advantages in comparison with other representative schemes.

This paper investigates the performance of cooperative device-to-device (C-D2D) communications in a cellular network, where the simultaneous wireless information and power transfer (SWIPT) technology is adopted by D2D transmitters (DTs). In this network, DTs can act as relays that consume a portion of energy harvested by a time switching (TS) strategy to satisfy the quality of service (QoS) requirements of cellular users (CUs) with poor channel conditions, in exchange for spectrum resources of CUs for D2D communications. To achieve the sum-throughput maximization of the network while guaranteeing the QoS requirements of both D2D and cellular links, we formulate a novel optimization problem that jointly determines user pairing between DTs and CUs, time allocation for energy harvesting and information transmission, and power allocation at DTs for relaying information and performing D2D communications. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) problem which is computationally prohibitive. To overcome this issue, a two-step policy-based algorithm is proposed to solve the problem in polynomial time. Simulation results validate the convergence of the proposed algorithm and the effectiveness of the joint user pairing and resource allocation scheme for improving network throughput.

The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

The signal processing of industrial big data (IBD) is a challenging task, owing to the complex working scenarios and the lack of annotations. Defect detection, which is an important subject of IBD research works, has shown its effectiveness in digital signal processing of industrial inspection applications in many previous studies. This article proposes a novel defect detection method based on deep learning for digital signal processing of industrial inspection applications. In our method, a module named feature collection and compression network is applied to merge multiscale feature information. Then, a new pooling method named Gaussian weighted pooling, which provides more precise location information, is used to replace region of interest (ROI) pooling. Experiment results show that our method gets improvements in both accuracy and efficiency, with mAP/AP50 of 41.8/80.2 at 33 fps on NEUDET, which satisfies the requirement of real-time systems.

Energy efficiency plays a vital role in device-to-device communications, which has been recognized as a key challenge. In this article, we study the implications of the peak power constraints and processing cost on the energy scheduling policy, and find that the peak power should be used around the time slots of each energy arrival, and the only time slot in which the intermittent communication may occur is the last time slot. A new optimal energy scheduling algorithm is devised based on these observations. Also, we propose a near-optimal energy scheduling algorithm that simultaneously takes into account the peak power constraints and the processing cost. Our simulation results demonstrate the effectiveness of the proposed energy scheduling algorithm and the validity of the mathematical analyses.

Intelligent Internet of Things (IIoT) is comprised of various wireless and wired networks for industrial applications, which makes it complex and heterogeneous.The openness of IIoT has led to the intractable problems of network security and management. Many network security and management functions rely on network traffic prediction techniques, such as anomaly detection and predictive network planning. Predicting IIoT network traffic is significantly difficult because its frequently updated topology and diversified services lead to irregular network traffic fluctuations. Motivated by these observations, we proposed a reinforcement learning-based mechanism in this article. We modeled the network traffic prediction problem as a Markov decision process, and then, predicted network traffic by Monte Carlo Q-learning. Furthermore, we addressed the real-time requirement of the proposed mechanism and we proposed a residual-based dictionary learning algorithm to improve the complexity of Monte Carlo Q-learning. Finally, the effectiveness of our mechanism was evaluated using the real network traffic.

Industrial Internet of Things (IIoT), as a common industrial application of Internet of Things, has been widely deployed in recent years. End-to-end network traffic is an essential information for many network security and management functions. This article investigates the issues of IIoT-oriented backbone network traffic prediction. Predicting the traffic of IIoT backbone networks is intractable because of the large number of prior network traffic information, which needs to consume expensive network resources for sampling. Motivated by that, we propose an effective prediction mechanism using multitask learning (MTL), which is a special paradigm of transfer learning. A deep learning architecture constructed by MTL and long short-term memory is designed. This deep architecture takes advantage of link loads as additional information to improve prediction accuracy. We provide a theoretical analysis for the MTL mechanism. The effectiveness is evaluated by implementing our mechanism on real network.

The papers in this special section focus on pervasive edge computing (PEC)for industrial Internet of Things. With the development of 5G technology and intelligent terminals, computation, communication, and storage capacities of devices are largely improved. Based on that, pervasive edge computing (PEC) becomes possible, where data can be processed on the network edge with the assistant of those intelligent terminals enhanced by 5G technology except the management of any centralized servers, including clouds and remote servers. The papers in this section solicits original research and practical contributions which advance PEC in industrial IoTs (IIoTs), regarding the architecture, technologies, and applications.

Recently, Internet of Vehicles (IoV) has become one of the most active research fields in both academic and industry, which exploits resources of vehicles and Road Side Units (RSUs) to execute various vehicular applications. Due to the increasing number of vehicles and the asymmetrical distribution of traffic flows, it is essential for the network operator to design intelligent offloading strategies to improve network performance and provide high-quality services for users. However, the lack of global information and the time-variety of IoVs make it challenging to perform effective offloading and caching decisions under long-term energy constraints of RSUs. Since Artificial Intelligence (AI) and machine learning can greatly enhance the intelligence and the performance of IoVs, we push AI inspired computing, caching and communication resources to the proximity of smart vehicles, which jointly enable RSU peer offloading, vehicle-to-RSU offloading and content caching in the IoV framework. A Mix Integer Non-Linear Programming (MINLP) problem is formulated to minimize total network delay, consisting of communication delay, computation delay, network congestion delay and content downloading delay of all users. Then, we develop an online multi-decision making scheme (named OMEN) by leveraging Lyapunov optimization method to solve the formulated problem, and prove that OMEN achieves near-optimal performance. Leveraging strong cognition of AI, we put forward an imitation learning enabled branch-and-bound solution in edge intelligent IoVs to speed up the problem solving process with few training samples. Experimental results based on real-world traffic data demonstrate that our proposed method outperforms other methods from various aspects.

Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.

In this paper, a novel system architecture including a massive multi-input multi-output (MIMO) or a reconfigurable intelligent surface (RIS) and multiple autonomous vehicles is considered in vehicle location systems. The location parameters of autonomous vehicles can be estimated based on the deep unfolding technique, which is a recent advance of deep learning. Traditional vehicle location methods such as the global position system (GPS) can only locate the target vehicles with relatively low accuracy. The super resolution cannot be achieved when two vehicles are too close, which means that the safety incidents exist when autonomous vehicles are deployed in future intelligent transportation systems (ITS). Different from the existing massive MIMO or RIS equipped with a regular array such as uniform rectangular array (URA) and uniform circular array (UCA), we exploit a massive MIMO or a RIS equipped with a conformal array extended from traditional regular array. First, the rotation from the global coordinate system to the local coordinate system is achieved based on geometric algebra. Second, 2D-DOA estimation of autonomous vehicles is modeled as a novel block sparse recovery problem. Third, the deep network architecture SBLNet is implemented to learn the nonlinear characteristic from the DOAs of autonomous vehicles and the data received by massive MIMOs or RISs. The 2D-DOA and polarization parameters can be estimated based on SBLNet with relatively low computational complexity. Simulation results demonstrate that SBLNet performs better than the state-of-the-art methods in terms of estimation accuracy and successful probability. The SBLNet is also suitable for the practical scenario considering fast moving autonomous vehicles, while, the traditional block sparse recovery methods fail in this complex scenario.

The explosive growth of mobile devices promotes the prosperity of novel mobile applications, which can be realized by service offloading with the assistance of edge computing servers. However, due to limited computation and storage capabilities of a single server, long service latency hinders the continuous development of service offloading in mobile networks. By supporting multi-server cooperation, Pervasive Edge Computing (PEC) is promising to enable service migration in highly dynamic mobile networks. With the objective of maximizing the system utility, we formulate the optimization problem by jointly considering the constraints of server storage capability and service execution latency. To enable dynamic service placement, we first utilize Lyapunov optimization method to decompose the long-term optimization problem into a series of instant optimization problems. Then, a sample average approximation-based stochastic algorithm is proposed to approximate the future expected system utility. Afterwards, a distributed Markov approximation algorithm is utilized to determine the service placement configurations. Through theoretical analysis, the time complexity of our proposed algorithm is linear to the number of users, and the backlog queue of PEC servers is stable. Performance evaluations are conducted based on both synthetic and real trace-driven scenarios, with numerical results demonstrating the effectiveness of our proposed algorithm from various aspects.

Pervasive edge computing refers to one kind of edge computing that merely relies on edge devices with sensing, storage and communication abilities to realize peer-to-peer offloading without centralized management. Due to lack of unified coordination, users always pursue profits by maximizing their own utilities. However, on one hand, users may not make appropriate scheduling decisions based on their local observations. On the other hand, how to guarantee the fairness among different edge devices in the fully decentralized environment is rather challenging. To solve the above issues, we propose a decentrailized computation offloading algorithm with the purpose of minimizing average task completion time in the pervasive edge computing networks. We first derive a Nash equilibrium among devices by stochastic game theories based on the full observations of system states. After that, we design a traffic offloading algorithm based on partial observations by integrating general adversarial imitation learning. Multiple experts can provide demonstrations, so that devices can mimic the behaviors of corresponding experts by minimizing the gaps between the distributions of their observation-action pairs. At last, theoretical and performance results show that our solution has a significant advantage compared with other representative algorithms.

In the big data era, thousands of hundreds of devices play the role of data producer as well as data consumer. However, wireless devices bear the power exhausted problem in various situations. How to balance the trade-off between data processing speed and power efficiency is meaningful to be researched. In this study, we propose a Sleep Data Fusion Networks module (SFDN) which has a star topology Bluetooth network to fuse data of sleep-aware applications basing on our designed application protocol. In the network, a center Bluetooth device fuses data generated from target node Bluetooth devices. Due to the low power consumption of Bluetooth as well as connection safety, our method is a better choice for a long lifetime and non-real time sleep data processing and fusion tasks then the Wi-Fi-based approach used in EAST and Smart-Alarm sleep-aware applications.

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance for anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

The end-to-end network traffic information is the basis of network management in large-scale IP backbone networks. To obtain exact network traffic data, a prevalent idea is to employ NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, it also affects the network load. Motivated by this issue, we propose an optimized traffic measurement method based on reinforcement learning in this paper, which can collect most of the network traffic data by activating NetFlow on a subset of interfaces of routers in a network. We use the Q- learning-based approach to deal with the problem of the interface-selection, and propose an approach to compute the reward. Furthermore, a modified Q- learning approach is proposed to handle the problem of interface-selection. The method is evaluated by the real data from the Abilene and GEANT backbone networks. Simulation results show that the proposed method can improve the efficiency of traffic measurement distinctly.

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

With the incoming era of 5G communication, the number of mobile devices is anticipated to increase dramatically. Flexible network resource allocation is required urgently to meet the mobility needs of a large number of users, accelerating the rise of network virtualization. However, the existing researches on virtual network embedding (VNE) consider less the virtual node migration caused by user mobility. In this paper, we attempt to address the problem of VNE supporting user mobility. The concepts of interruption penalty and blocking penalty are proposed to quantify the impact of virtual node mobility on infrastructure providers (InPs) and refine the revenue model of InPs. Then we propose a location-constrained 5G VNE algorithm, where a virtual node and virtual link pair embedding method is designed to increase the probability of successful VNE. Based on the proposed VNE algorithm, we further propose a virtual network re-embedding algorithm that can dynamically migrate the embedding of virtual nodes following user mobility. The virtual node migration is triggered by predicting the locations of virtual nodes and selecting the target physical nodes with the minimum number of re-embedding. Simulation results show that the proposed algorithm outperforms the existing VNE algorithms with higher InP revenue.

Optical switching has been investigated for a long time, as a possible effective solution to overcome the limitations of power consumption, footprint and scalability in data center networks (DCNs). However, with the increasing DC traffic and narrow channel spacing between wavelengths for optical interconnects, traditional single-mode and multi-waveguide optical switching solutions have encountered bandwidth bottlenecks. To this end, we propose a 2×2 silicon-based on-chip optical switching architecture compatible with hybrid wavelength-andmode division multiplexing (WDM-MDM), and it is found experimentally that the proposed design increases the bandwidth 8× times with low crosstalk.

With the evolutionary development of latency sensitive applications, delay restriction is becoming an obstacle to run sophisticated applications on mobile devices. Partial computation offloading is promising to enable these applications to execute on mobile user equipments with low latency. However, most of the existing researches focus on either cloud computing or mobile edge computing (MEC) to offload tasks. In this paper, we comprehensively consider both of them and it is an early effort to study the cooperation of cloud computing and MEC in Internet of Things. We start from the single user computation offloading problem, where the MEC resources are not constrained. It can be solved by the branch and bound algorithm. Later on, the multiuser computation offloading problem is formulated as a mixed integer linear programming problem by considering resource competition among mobile users, which is NP-hard. Due to the computation complexity of the formulated problem, we design an iterative heuristic MEC resource allocation algorithm to make the offloading decision dynamically. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of execution latency and offloading efficiency.

Mobile edge computing (MEC) has risen as a promising paradigm to provide high quality of experience via relocating the cloud server in close proximity to smart mobile devices (SMDs). In MEC networks, the MEC server with computation capability and storage resource can jointly execute the latency-sensitive offloading tasks and cache the contents requested by SMDs. In order to minimize the total latency consumption of the computation tasks, we jointly consider computation offloading, content caching, and resource allocation as an integrated model, which is formulated as a mixed integer nonlinear programming (MINLP) problem. We design an asymmetric search tree and improve the branch and bound method to obtain a set of accurate decisions and resource allocation strategies. Furthermore, we introduce the auxiliary variables to reformulate the proposed model and apply the modified generalized benders decomposition method to solve the MINLP problem in polynomial computation complexity time. Simulation results demonstrate the superiority of the proposed schemes.

The automated diagnosis helps us better understand the complex landscape of diseases, leading to more effective, early and reliable medical diagnosis and therapy. The human-machine interactions in healthcare delivery relying on automated cyber-physical systems (ACPSs) play an important role in the automated diagnosis. Currently, the multicore accelerator used for ACPS has utilized the network-on-chip (NoC) for personalized healthcare. However, the discrete cores based on NoC are affected by limited computation speed, since the data have to pass through an electrical interconnect. In this paper, we propose a novel optical NoC (ONoC) solution of designing discrete cores to quickly understand biomarkers for early detecting abnormal pathophysiology, such as the deviation from the protein's native state. We analyze the performance of our ONoC-based ACPS accelerator for personalized healthcare by virtue of the tested proteins widely adopted in the lattice protein model. Our mathematical analysis and simulation results demonstrate that: 1) the chip area becomes smaller than a traditional design, which makes the personalized healthcare product more convenient; 2) the computation speed is promoted, resulting in the rapid understanding of biomarkers; and 3) we improve the data transmission reliability through accurately capturing the photonic effect so that desirable human-machine interactions can be guaranteed. Note to Practitioners-We design an on-chip hardware accelerator for automated diagnosis and personalized healthcare by predicting biological protein folding. The simulation results based on the lattice protein model can well guide the practitioners to design a more convenient and reliable product quickly detecting the biomarker, such as the deviation from the protein's native state.

The peer-to-peer energy trading has been achieved among nodes in industrial Internet of Things. To establish a secure private market, some meaningful works propose the concept of the energy chain, where one block is added in a linear and chronological order once the trading pair of nodes (buyer and seller) has a valid transaction verified by data audit (e.g., a hash value). Since the buyer applies virtual coins from the credit bank to buy others' surplus energy, a considerable credit utility is obtained if all nodes are encouraged to meet local power loads out of self-interest. However, such frequent transactions have huge operational overhead, including a long chain maintaining many blocks and an expensive energy transportation cost between trading pairs. To solve these challenging issues, our method enables nodes to satisfy their power loads through local stored energy (self-sufficiency), before participating as sellers if they still have considerable surplus electricity. Without transactions made by some self-sufficient nodes, the operational overhead can be mitigated in a more secure environment. Taking the classic Internet of energy as a case study, we demonstrate the effectiveness of our solutions, and it can achieve a good tradeoff between credit utility and operational overhead.

Currently, vehicles have the abilities to communicate with each other autonomously. For Internet of Vehicles (IoV), it is urgent to reduce the latency and improve the throughput for data transmission among vehicles. This article proposes a deep learning based transmission strategy by exploring trirelationships among vehicles. Specifically, we consider both the social and physical attributes of vehicles at the edge of IoV, i.e., edge of vehicles. The social features of vehicles are extracted to establish the network model by constructing triangle motif structures to obtain primary neighbors with close relationships. Additionally, the connection probabilities of nodes based on the characteristics of vehicles and devices can be estimated, by which a content sharing partner discovery algorithm is proposed based on convolutional neural network. Finally, the experiment results demonstrate the efficiency of our method with respect to various aspects, such as message delivery ratio, average latency, and percentage of connected devices.

Due to the ever-increasing requirements of delay-sensitive and mission-critical applications in 5G, mobile edge computing is promising to react and support real-time interactive systems. However, it is still challenging to construct a 5G-enabled traffic management system, owing to the qualification of ultra-low latency and ubiquitous connectivity. Furthermore, the computing resources and storage capacities of edge nodes are limited, thus computation offloading is a fundamental issue for real-time traffic management. This paper puts forward a hybrid computation offloading framework for real-time traffic management in 5G networks. Specially, we consider both nonorthogonal-multiple-access-enabled and vehicle-to-vehicle-based traffic offloading. The investigated problem is formulated as a joint task distribution, subchannel assignment, and power allocation problem, with the objective of maximizing the sum offloading rate. After that, we prove its NP-hardness and decompose it into three subproblems, which can be solved iteratively. Performance evaluations illustrate the effectiveness of our framework.

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users’ traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users’ Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.

Performing virtualization can further improve the infrastructure utilization of Inter-Data-Center Optical Networks (IDCONs). In virtualized IDCONs, a service requestor subscribes one lightpath for transferring the virtual machine to the specific server in the destination DC. However, the infrastructure overload leads to the denial of services. Thus, we design a novel service degradability (well-intentioned service degradation) framework, in order to minimize the number of service requirements declined by overloads, resulting in the maximal utility of the IDCON provider. We mathematically formulate the problem and determine the most appropriate degradation degree based on microeconomic theory. For heuristic algorithms, the availability-aware bandwidth degradability policy is invoked once there is the optical backbone overload; while for the server overload in DCs, the application-specific virtual machine degradability policy is considered. Finally, the simulation results demonstrate the effectiveness of our heuristic algorithms, and the optimal combination of degradation coefficients is achieved by microeconomic theory and LINGO.

Nowadays, owing to the advantages of high bandwidth and low power consumption, the wired optical network-on-chip (W-ONoC) has emerged as a high-performance on-chip communication solution. However, the W-ONoC suffers from increased latency and limited scalability since the multi-hop data transmission is frequently performed in wired structures. In addition, several problems such as photoelectric conversion and thermal sensitivity pose challenges to the design of WONoCs. In this paper, we develop a novel on-chip communication architecture based on free-space optics (FSO), and leverage a suite of emerging devices. The proposed architecture eliminates the power loss caused by the waveguide and microring resonator previously deployed in W-ONoCs, and the transmission latency is also reduced through simplifying the packet switching. Moreover, compared with the traditional single-layer FSO NoC, our 3D FSO NoC further decreases the number of consumed lasers and power. Extensive simulation results demonstrate the aforementioned advantages of our solution.

In many people's daily life, alarm sounds play an important role, which reflects the fast paced life in the modern society. On most occasions, people uses alarm sounds to wake them up in the morning. Improper alarm sounds could make people feel terrible. In this paper, we mainly propose a smart alarm sound recommendation system and construct an application to study how alarm sounds can impact human emotions. The recommendation system is deployed on the cloud, working with smartphones to deliver smart alarm sounds by considering not only sleep patterns, but also context information such as weather. The designed system can recommend smart alarm sounds to users, orchestrate sensing data collected by multiple sensors on smartphones, and collaborate with cloud computing to recommend preferable alarm sounds. An application is developed to demonstrate system effectiveness, which consists of the fore-end on Android OS and the back-end on the cloud. Experiments demonstrate that our system can recommend smart alarm sounds to wake participants up in the morning and the participants give feedback about their emotional states. The results show the system can improve people's emotion states by about 14.57%, compared to traditional alarm sound delivery.

Because of the enormous potential to guarantee road safety and improve driving experience, social Internet of Vehicle (SIoV) is becoming a hot research topic in both academic and industrial circles. As the ever-increasing variety, quantity, and intelligence of on-board equipment, along with the evergrowing demand for service quality of automobiles, the way to provide users with a range of security-related and user-oriented vehicular applications has become significant. This paper concentrates on the design of a service access system in SIoVs, which focuses on a reliability assurance strategy and quality optimization method. First, in lieu of the instability of vehicular devices, a dynamic access service evaluation scheme is investigated, which explores the potential relevance of vehicles by constructing their social relationships. Next, this work studies a trajectory-based interaction time prediction algorithm to cope with an unstable network topology and high rate of disconnection in SIoVs. At last, a cooperative quality-aware system model is proposed for service access in SIoVs. Simulation results demonstrate the effectiveness of the proposed scheme.

Due to the heterogeneous and resource-constrained characters of Internet of Things (IoT), how to guarantee ubiquitous network connectivity is challenging. Although LTE cellular technology is the most promising solution to provide network connectivity in IoTs, information diffusion by cellular network not only occupies its saturating bandwidth, but also costs additional fees. Recently, NarrowBand-IoT (NB-IoT), introduced by 3GPP, is designed for low-power massive devices, which intends to refarm wireless spectrum and increase network coverage. For the sake of providing high link connectivity and capacity, we stimulate effective cooperations among user equipments (UEs), and propose a social-aware group formation framework to allocate resource blocks (RBs) effectively following an in-band NB-IoT solution. Specifically, we first introduce a social-aware multihop device-to-device (D2D) communication scheme to upload information toward the eNodeB within an LTE, so that a logical cooperative D2D topology can be established. Then, we formulate the D2D group formation as a scheduling optimization problem for RB allocation, which selects the feasible partition for the UEs by jointly considering relay method selection and spectrum reuse for NB-IoTs. Since the formulated optimization problem has a high computational complexity, we design a novel heuristic with a comprehensive consideration of power control and relay selection. Performance evaluations based on synthetic and real trace simulations manifest that the presented method can significantly increase link connectivity, link capacity, network throughput, and energy efficiency comparing with the existing solutions.

As an emerging communication platform, opportunistic Cloud of Things (CoT) is promising for clients to exchange messages through opportunistic contacts in cloud computing-enabled Internet of Things. Recently, numerous socially aware schemes have been put forward, leveraging users’ social attributes and contact history to predict future contacts with the purpose of improving message forwarding efficiency and network throughput. However, individual privacy is generally overlooked in the prediction process and transmission stage of opportunistic CoT. In this paper, we construct a privacy-preserving message forwarding framework for opportunistic CoT to guarantee individual privacy and improve transmission efficiency. We first set up a two-layer architecture of a cloud server to improve communication efficiency for terminal clients. By integrating a security-based mobility prediction algorithm with a routing decision process, our scheme can effectively protect individual privacy. We integrate an attribute-based cryptographic algorithm with a message delivery process to enable our scheme to resist attacks, such as Sybil attack, drop for profit, and data tampered attack. Compared with some existing solutions, our scheme improves network security significantly at the cost of slightly increased communication overhead.

Mobile edge computing (MEC) brings computation capacity to the edge of mobile networks in close proximity to smart mobile devices (SMDs) and contributes to energy saving compared with local computing, but resulting in increased network load and transmission latency. To investigate the tradeoff between energy consumption and latency, we present an energy-aware offloading scheme, which jointly optimizes communication and computation resource allocation under the limited energy and sensitive latency. In this paper, single and multicell MEC network scenarios are considered at the same time. The residual energy of smart devices' battery is introduced into the definition of the weighting factor of energy consumption and latency. In terms of the mixed integer nonlinear problem for computation offloading and resource allocation, we propose an iterative search algorithm combining interior penalty function with D.C. (the difference of two convex functions/sets) programming to find the optimal solution. Numerical results show that the proposed algorithm can obtain lower total cost (i.e., the weighted sum of energy consumption and execution latency) comparing with the baseline algorithms, and the energy-aware weighting factor is of great significance to maintain the lifetime of SMDs.

In sharing economy, people offer idle social resources to others in a sharing manner. Through community-based online platforms, the people offering services can earn commission while others can enjoy a better life via renting social resources. Consequently, the value-in-use of services is expectedly strengthened within the unit time, although the total amount of social resources remains constant. Influenced by sharing economy, some famous companies have developed intelligent systems to analyze the most appropriate coincidence between citizens' idle supply and renting demand from numerous data sets. However, the big data analysis of the optimal service-demand matching usually runs on the traditional multiprocessors equipped in intelligent systems, so-called “system-on-chip.” In this paper, we design a novel computer architecture - the accelerator based on optical network-on-chip (ONoC) - to further speed up the matching between citizens' offer and demand in sharing economy. Our ONoC-based accelerator is able to quickly calculate the optimal service-demand matching by processing computation tasks on parallel cores, i.e., task-core mapping. In addition, to improve the accelerator reliability, the assorted task-core mapping algorithm is also designed. The extensive simulation results based on real trace file demonstrate the effectiveness of our system and algorithm. Note to Practitioners - Sharing economy is of great importance for realizing green consumption and sustainable development in our human society. Sharing economy enterprise calls for intelligent system design for service-demand matching in the current big data era. In this paper, we design the accelerator based on ONoC to further speed up the matching between citizens' offer and demand in sharing economy. By processing computation tasks on parallel cores using our algorithm, the task-core mapping can be performed with high speed and reliability. The simulation results - based on the trace file of Amazon Mechanical Turk - can well guide the practitioners to design a more clever and reliable product by quickly calculating the optimal service-demand matching.

As an integrated environment deployed with wired and wireless infrastructures, the smart city heavily relies on the wireless-optical broadband access network. The information flows captured by indoor devices are sent to optical network units through front-end wireless mesh sensor networks (WMNs) and, finally, reach the optical line terminal for industrial/commercial decision making via the passive optical network backhaul. To reduce the backhaul bandwidth saturated by this conventional approach, edge devices are deployed at the front-end WMN to preprocess information flows. Based on collaborative edge computing, home users or factory workers customize their computing services as virtual networks embedded onto the common WMN. In this paper, we propose the green survivable virtual network embedding for the collaborative edge computing in smart cities. We mathematically formulate the problem and derive the corresponding bound. Extensive simulations with real traces demonstrate the algorithm effectiveness.

Fog computing has been merged with Internet of Vehicle (IoV) systems to provide computational resources for end users, by which low latency can be guaranteed. In this paper, we put forward a feasible solution that enables offloading for real-time traffic management in fog-based IoV systems, aiming to minimize the average response time for events reported by vehicles. First, we construct a distributed city-wide traffic management system, in which vehicles close to road side units can be utilized as fog nodes. Then, we model parked and moving vehicle-based fog nodes according to a queueing theory, and draw the conclusion that moving vehicle-based fog nodes can be modeled as an $M/M/1$ queue. An approximate approach is developed to solve the offloading optimization problem by decomposing it into two subproblems and scheduling traffic flows among different fog nodes. Performance analyses based on a real-world taxi-trajectory datasets are conducted to illustrate the superiority of our method.

Opportunistic Network (OppNet) is an emerging communication paradigm, by which nodes inside forward messages through personal contact opportunities. Recently, numerous studies have focused on predicting nodes meeting to promote routing efficiency and reduce transmission delay. However, individual privacy would likely be revealed to strangers or attackers during the execution of prediction. In this paper, we construct a privacy-reserved network framework for message forwarding to guarantee both efficient communication and individual privacy in OppNets, including a security-based prediction method and an attribute-based cryptosystem. Simulation results demonstrate that, our algorithm outperforms TRSS on average delivery ratio, generally by 15% for dropping probability and data tempered probability.

Finding rising star in social networks becomes a popular research topic in recent years. Rising star means he or she may be not so charming at the initial time but turns out to be an outstanding star over time. In academic network, rising star means the scholar who just starts his research career with not so many papers published. While in the future, the sum of citations and papers will increase and the scholar will be more outstanding. There are some works of scholarly assessment, however, few works are about finding rising stars. Most of the algorithms of finding rising star are based on random walk on a heterogeneous network constructed by bibliography. These methods need entire information of networks and fit for a long time. In this paper, we propose a method based on “Social Genes”, which are defined as the inside factors of scholars' activities characteristics. We use factor analysis to find the inside factors, calculate the weights by neural network, and make assessments via AHP method. The experiment results on APS dataset show our method is able to find more authors with high rank.

People spend up to one-third of lives asleep, and healthy sleep habits can make a big difference in their quality of life. But in modern society, many people have unhealthy sleep diaries and suffer from various sleep disorders, which may result in irregular mood fluctuations or even mental health problems such as anxiety and depression. We propose the Emotion-Aware Smart Tips (EAST), a novel approach that could help to inform users about their irregular emotional states with smart tips to improve their sleep qualities. EAST aims at helping users keep healthy sleep schedules and emotional states by providing smart tips through a novel model that combines multivariate regression, random forest, and neural network to quantify the relations between sleep patterns and emotional states. Prototype implementation and initial experiments of EAST in mobile phones have demonstrated its desired functionality and practicality for real-world deployment.

Stable and reliable wireless communication is one of the critical demands for smart cities to connect people and devices. Although intelligent terminals can be leveraged to deliver and exchange data through Internet, poor network coverage and expensive network access challenge the deployment of network infrastructure. In this paper, we propose a social-oriented smartphone-based adaptive transmission mechanism to improve the network connectivity and throughput in Internet of Things (IoTs) for smart cities. First, a social-oriented double-auction-based relay selection scheme is investigated to stimulate the relay smartphones to forward packets for others so that the network connectivity can be strengthened. Furthermore, for the sake of achieving high throughput in smartphone-based IoTs, the relay method selection is determined by integrating various kinds of transmission schemes in an optimal fashion to make full use of wireless spectrum resource. Due to its high computational complexity, a firefly-algorithm-based scheme is investigated, by which the formulated NP-complete problem can be solved effectively. Simulation results demonstrate the superiority of our proposed method.

Cooperation among nodes plays an important role in the commercial development of wireless networks. Efficient cooperation should include not only encouraging selfish nodes to forward packets for one another but also selecting optimal transmission methods. Therefore, in this paper, in order to improve network performance, we propose a social-oriented adaptive transmission scheme for wireless ad hoc networks. Firstly, next-hop node for each transmission is decided by a double auction-based social awareness mechanism. Then in the case of relay-aided transmissions, optimal relaying method is selected by jointly considering network coding and spectrum spatial reuse. Simulation results demonstrate that the proposed scheme has significant advantages in social welfare and throughput improvement.