Green computation has emerged as one of the key goals of cloud resource provisioning. The cloud resource clusters (CRCs) composed of heterogeneous performance servers are powered by uninterruptible power supplies (UPSs). However, due to the inherent nonlinear losses of UPSs, CRCs face challenges in computing resource provisioning with regard to sustainable energy consumption. Meanwhile, to alleviate the backlog in service queues, we propose a joint resource configuration and instance placement optimization problem that comprehensively models the energy consumption during instances processing in CRCs. Specifically, based on the length of the execution intervals of these two phases, this problem is decomposed into two subproblems in a dual-timescale framework. In a long timescale, we explore the temporal correlation of historical request data to pre-configure resources. Subsequently, the Lyapunov optimization method is employed to decompose the energy consumption problem of servers supported by each UPS into multiple subproblems across short timescale, while ensuring the stability of service queues. Furthermore, we use instance continuous relaxation to derive the optimal placement solution ideally, and design a double optimal gradient descent strategy for its practical implementation. Evaluation results demonstrate that the proposed strategy achieve measurable energy reduction in CRCs while maintaining flexibility during resource scaling.

While multi-device cooperative task-oriented semantic communication (TOSC) enhances task performance through comprehensive information representation, it inevitably introduces redundancy, thereby increasing communication overhead. Existing redundancy elimination methods suffer from limitations in interpretability and coarse granularity, hindering the optimal utilization of communication resources. To this end, we propose a disentangled information bottleneck guided TOSC framework (DisenIB-TOSC). The framework first employs the basic IB for initial feature compression, then formulates a novel DisenIB specifically designed for multi-device cooperation inference, which enhances task performance and achieves interpretable feature disentanglement by separating features into common and private components, thereby establishing a theoretical foundation for redundancy identification. Subsequently, we derive differentiable, computationally tractable forms for both IB objectives by combining variational approximation, consistency constraints, and density ratio trick. Leveraging the disentangled features, we further design a feature importance-aware selective transmission strategy, DisenIB-TOSC-ST, which quantifies feature importance via mutual information estimation to dynamically discriminate and control redundant feature transmission. Experimental results on several tasks demonstrate that our method outperforms baselines in task performance while reducing communication costs, verifying the effectiveness and interpretability of feature disentanglement.

Low earth orbit (LEO) satellite networks, as integrated service systems, are typically divided into domains based on application functions, such as observation and communication. However, independent domain resources and the high dynamics of satellites make cross-domain resource interactions difficult to capture, posing challenges to large-scale satellite network resource coordination and degrading the quality of service (QoS) for task flows. To address this, a multi-level hypergraph (MLH) is introduced to represent domain resources across temporal, resource type and spatial dimensions. MLH consolidates similar resource features via hyperedges, reducing redundant connections. The cross-domain resource coordination problem is then modeled as a mixed-integer linear programming (MILP) problem to maximize the sum of the minimum priorities of scheduled tasks. Furthermore, by leveraging MLHs topological nesting, a multi-dimensional resource dual-level scheduling algorithm (MRDSA) is proposed, decomposing the problem into two subproblems solved using the hyperpath scheduling algorithm (HSA) and internal path scheduling algorithm (IPSA). Simulations demonstrate that the proposed method enhances QoS, reduces computational complexity and improves resource utilization ratio in LEO satellite networks.

Satellite communications are widely utilized in the Internet of Remote Things (IoRT) to achieve large-scale coverage and efficient data collection. This paper investigates an efficient data acquisition scheme in which Low Earth Orbit (LEO) satellites collect data from terrestrial low-power sensor networks via gateway stations (GSs). Specifically, ground-based sensors employ short-packet transmissions to relay sensed data to GSs, which subsequently aggregate the received data and upload it to LEO satellites passing over the relevant area. To ensure efficient data collection, our objective is to minimize the energy consumption of terrestrial sensors while achieving the lowest possible data collection delay, thereby extending the sensor lifetime. To address this joint optimization problem, we propose a delay-energy-efficient data aggregation and scheduling algorithm based on proactive network configuration and a novel hypergraph-based link scheduling approach. Finally, extensive numerical simulations are conducted to evaluate the performance of the proposed algorithm. The experimental results demonstrate that, compared to existing benchmark algorithms, the proposed method significantly reduces both data collection delay and sensor energy consumption.

Integrated Energy Systems (IES) are critical infrastructures enabling multi-energy synergy and low-carbon transitions. However, their distributed and uncertain nature poses significant challenges for adaptive and efficient scheduling. To address these issues, this paper proposes a software, edge-intelligent, and cross-layer orchestration algorithm based on Multi-Agent Multi-Objective Proximal Policy Optimization (MAMOPPO). First, a cooperative multi-agent scheduling model is established, where distributed agents operate at the edge and coordinate through shared global information. Then, a multi-value network is employed to decouple and optimize operational costs, carbon emissions, and renewable energy utilization. Finally, a mirror learning strategy is introduced to enhance policy stability under uncertainty and facilitate cross-layer coordination between energy control and communication layers. Simulation results show that the proposed approach reduces system cost by 42.8%, lowers emissions by 44.6%, and maintains high renewable energy utilization, demonstrating its effectiveness in orchestrating intelligent edge-based services in next-generation smart grid scenarios.

Telemedicine significantly reduces patients' medical treatment time and costs. Medical images play an important role in telemedicine services, which contain patients' private information and face the risk of illegal access when transmitted over the network, leading to patient privacy leakage. This paper proposes a dual-adversarial medical image encryption network (DAEN) for protecting patient privacy. We formulate the image encryption problem as an image conversion task and design two paired adversarial networks for image transformation. Furthermore, we analyze the characteristics of an ideal cipher image and construct pseudo-ciphertext to guide network training. The networks generate keys with strong randomness and sensitivity through adversarial training and dynamically encrypt the medical images. Experiments are conducted on the Chest X-ray dataset, and the results show that the DAEN can encrypt medical images into meaningless images, achieving high information entropy, NPCR, and UCAI compared to advanced encryption algorithms, which can effectively secure medical image transmission.

Intelligent Visitor Management System (IVMS) is crucial for enhancing security and operational efficiency in smart factories and intelligent office buildings. Leveraging AIoT-driven image analysis will facilitate real-time visitor authentication and access control. However, the growing volume of interactions and the limited processing power of local terminals complicate the delivery of timely and accurate image analysis. To address these challenges, we propose an edge-terminal collaborative AIoT framework for real-time visitor management. The framework solves the limitations of traditional approaches, where local terminals are unable to handle the computational load and edge solutions experience high latency due to transmission delays. Specifically, it integrates three key components to improve system performance : a local analysis module for initial processing, an image communication module for efficient data transmission, and an edge analysis module for advanced processing. Moreover, the framework jointly optimizes image task offloading , wireless channel allocation, and image compression , all formulated as an optimization problem to ensure fast and accurate analysis. Additionally, a novel multi-level Deep Reinforcement Learning (DRL) method is further designed to dynamically refine the selection of compression and offloading strategies. By learning in real-time, the DRL model adapts to network variations, addressing the scalability and adaptability limitations of existing methods. Simulation results show that our proposed edge-terminal collaborative AIoT framework significantly outperforms both edge-only and terminal-only methods in terms of latency and accuracy.

This review delves into the applications and prospects of nonterrestrial networks (NTNs) in the field of information and communication. NTNs utilize aerial or space platforms as critical components of the communication network, including high-altitude unmanned systems, low-altitude unmanned systems, and satellites. Compared to traditional terrestrial cellular networks, NTNs offer advantages, such as wider coverage, flexible deployment, and resistance to ground-based disasters. Therefore, NTNs have broad application prospects in industries, such as transportation, public safety, media entertainment, healthcare, energy, agriculture, and finance. This review focuses on the network architecture and key technologies that support NTNs, including the system’s composition architecture, key technologies, application case analysis, challenges, potential solutions, and future outlook, aiming to provide beneficial reference and guidance for the promotion and application of NTNs. The review also examines the support of international organizations for NTNs’ standardization, as well as related research progress and future challenges.

Intelligent search enables users to access information from the Internet quickly, but existing schemes fail to achieve accurate semantic awareness and reliable information transmission, especially in constrained communication conditions, which degrade search accuracy and personalized user experience. To address these challenges, we propose a multiuser semantic communication system to perform personalized search (PS) tasks, named MU-SemCom-PS. In particular, the system introduces a novel semantic encoder at the transmitter to deeply extract user-specific search semantics by analyzing search history from multiple perspectives, and designs a semantic decoder at the receiver to recover and enhance search semantics by leveraging implicit correlations among users, thus the PS tasks are performed based on the recovered search semantics. To optimize the PS tasks for all users, the federated learning (FL) framework is leveraged to jointly train the MU-SemCom-PS system through knowledge collaboration and sharing. Experimental results show that the proposed scheme significantly improves search accuracy and robustness under constrained communication conditions.

The integration of vehicular networks with Machine Learning (ML) is driving the advancement and intelligence of future vehicular systems. As a core technology in the IoVs, Vehicular Edge Computing (VEC) leverages the computational and communication resources of both vehicles and edge servers, enabling model training closer to the data source. Federal Learning (FL) has shown great promise in training large-scale ML models without exposing raw data. However, many vehicles are reluctant to participate in FL training due to high resource demands and the inherent mobility challenges of vehicular networks. To address this issue, this paper proposes an FL auction framework that incentivizes vehicle participation by maximizing the utility of the FL platform. Specifically, our approach factors in the basic utility, average reward and tolerance delay of dynamic vehicles to determine their bidding intent. Additionally, an online auction-based client selection algorithm is proposed that ensures individual rationality for vehicles, coupled with a reward function based on model accuracy to further encourage participation in FL training. Simulation results demonstrate the effectiveness of the proposed algorithm, showing a 38.9% improvement in platform utility and a 31.3% reduction in average payments compared to the Online Auction (OA) algorithm.

Non-terrestrial networks (NTNs) are integrated with terrestrial networks to form space-air-ground integrated networks (SAGINs), providing seamless global coverage and supporting the development of the digital economy. However, when it comes to the actual design and deployment of SAGINs, the heterogeneity, self-organization, and flexibility of SAGIN pose challenges for precise modeling and quantitative analysis. In this regard, this paper proposes a multi-dimensional analysis model based on stochastic geometry for SAGIN, which considers the randomness of ground users’ (GUs) distribution and the multi-dimensional coverage characteristics of NTN nodes. The model determines the policies for GUs to access NTNs by adopting the maximum received average signal-to-interference-plus-noise ratio (SINR) association policy (AP) and the balanced satellite load AP. Specifically, we analyze the interference distribution of different links in the terahertz (THz) band and their Laplace transforms, then derive the uplink connectivity expressions of ground-to-space links with/without aerial relays under the two APs. Numerical results validate the accuracy of the theoretical model and explore the impact of APs, SINR thresholds, THz channel propagation coefficients, and aerial relay numbers on SAGIN connectivity, providing theoretical guidance for the deployment of THz SAGINs.

As the most vulnerable part of the traffic scenario, it is vital to ensure the safety of pedestrians. Accurately predicting the future trajectory of pedestrians not only ensures the safety of pedestrians but also improves the efficiency of traffic operations. In light of this, this paper presents a novel target-driven method for pedestrian trajectory prediction. The method uses bidirectional long short-term memory (Bi-LSTM) to predict the approximate position of the pedestrian at different time intervals and uses the short-term location as a short-term target for pedestrians. Meanwhile, our method takes into account the influence of surrounding vehicles on the future trajectory of pedestrians. We predict the speed of vehicles around the pedestrian and apply it to the pedestrian trajectory prediction to make the prediction more accurate. We evaluated our method on two public datasets, PIE and JAAD, and demonstrated its superiority over the benchmark methods. The ablation experiments further reveal that the target estimation and speed estimation modules reduced prediction errors by approximately 11% and 30% respectively.

Pedestrian trajectory prediction in autonomous driving is a critical and complex issue with important implications for road traffic safety. The primary challenges of this task stem from: 1) difficulty in simultaneous learning of individual and group motion behaviors within dense crowd scenarios and 2) limited interpretability of the model. In this work, we implement the social force model to the Conditional Variational Auto Encoder (CVAE) trajectory prediction framework and propose a trajectory prediction method in dense crowd scenarios, called GroupGCN. Specifically, firstly, pedestrians are grouped using the grouping module. Secondly, Graph Convolutional Network (GCN) is utilized to analyze pedestrian movement from both intra-group and inter-group perspectives. Intra-group involves understanding interactions among individuals while considering their preferences. Inter-group entails studying interactions between pedestrian groups at the group level, then the trajectory collision problem between groups is addressed by the collision prediction network. The performance of GroupGCN is evaluated compared with several public benchmarks. Experimental results show that GroupGCN improves performance by 61.8% on the ETH and UCY datasets compared to the prediction methods that consider pedestrian groups, which shows that GroupGCN will have better performance in dense crowd scenarios.

Internet of Medical Things (IoMT) is increasingly gaining attentions in fall detection because of its ability to sense, monitor and analyze, which can provide proper assistance to the elderly with fragile health conditions. As fall events are infrequent, its important to timely detect its occurrence in order to alleviate the harmless. This paper presents a Lightweight Attention Network Fall Detection (LA-FD) framework, which detects falls by analyzing gait acceleration signal (GAS). Then, we can take appropriate measures to mitigate the impact. LA-FD introduces depth-separated convolution to the Lightweight Attention Network module to reduce computational costs and model parameters. Additionally, relative positional offsets are incorporated into each self-attention module to enhance attention mechanisms' expressiveness. The result shows that LA-FD significantly reduces model size by 99.3% compared to the Transformer and 95.1% compared to CNN-LSTM, while only a 5% accuracy drop compared to the Transformer and 2% compared to CNN-LSTM.

Artificial Intelligence of Things (AIoT) represents a collaborative fusion of artificial intelligence (AI) and the Internet of Things (IoT). AIoT systems enable real-time data acquisition through IoT sensors and conduct intelligent data analysis tasks across the entire spectrum from terminal to edge to cloud, creating a dynamic and empowering ecosystem. However, the evolving landscape of AIoT faces a perplexing challenge: how to effectively sense the geographically diverse and highly variable environment, accurately gather massive and diverse IoT data with varying value density, and intelligently integrate multisource data to deliver real-time, intelligent, and high-quality applications.

Crowdsensed data trading (CDT) solves the problem of data resource scarcity and diversity, faced in conventional data trading by dispatching workers to perform data collection tasks and sharing data through trading. In CDT, both worker and data requesters need to provide geographic location or task location information for spatiotemporal data collection tasks. Existing research has insufficiently addressed the simultaneous consideration of both location privacy information and overlooked the variability in data quality resulting from variations in worker task accessibility and location. To address this problem, we propose a privacy-preserving task allocation scheme with regional coverage based on homomorphic encryption, which allows workers to perform tasks within the qualified region, the degree of regional coverage is associated with data quality to provide diversified data. To solve the sensing data trading and allocation problem for many-to-many users, we further introduce double auction. And thus propose a privacy-preserving data trading scheme to protect bidding information privacy, this scheme ensures the truthfulness of the auction process and mitigates participant manipulation. Besides, we employ a secure multiparty computing strategy to implement truth discovery in CDT, which enables third-party platforms to perform accurate task allocation and winner decisions based on encrypted location and bidding information. Extensive theoretical and simulation analyses show that the proposed scheme satisfies the expected economic properties (truthfulness, individual rationality, etc.), privacy, and effectiveness.

The metaverse is a human-centric beyond-reality virtual world, in which people use virtual identities to live, work, and socialize. Due to the openness and sharing of metaverse applications, the virtual-real identity link (VRIL) may cause uncertainties and unpredictable risks. At present, the research on VRIL risks is still in its infancy and VRIL risk predictions lack a comprehensive theoretical system and methodological tool. In this paper, we first construct a VRIL attack model, according to which an attacker can link a user’s real and virtual identities together using the information observed in the real and virtual worlds. Then we propose the tuple frequency-based VRIL prediction (TupPre) model and discover the population distribution, recursive hypergeometric (RH) distribution, and approximate binomial distribution of the tuple frequency (i.e., the occurrence times of attribute value combinations) given incomplete information. Focusing on the tuple frequency estimation error in biased samples, we introduce attribute value correlation knowledge to improve the prediction performance. The experimental results on generated and real-world datasets show that the TupPre model has excellent performance, with a mean area under the curves (AUCs) of 0.86 to 0.98 on these datasets, and it performs even more superior with certain background knowledge (mean AUC 0.95~0.98). The discovered basic distribution rules of the tuple frequency and the proposed quantitative analysis method for metaverse VRIL risk predictions construct the foundation of the identity privacy framework for the metaverse.

Image matching is a fundamental and critical task of multisource remote sensing image (RSI) applications. However, RSIs are susceptible to various noises. Accordingly, how to effectively achieve accurate matching in noise images is a challenging problem. To solve this issue, we propose a robust multisource RSI matching method utilizing attention and feature enhancement against noise interference. In the first stage, we combine deep convolution with the attention mechanism of the transformer to perform dense feature extraction, constructing feature descriptors with higher discriminability and robustness. Subsequently, we employ a coarse-to-fine matching strategy to achieve dense matches. In the second stage, we introduce an outlier removal network based on a binary classification mechanism, which can establish effective and geometrically consistent correspondences between images; through weighting for each correspondence, inliers versus outliers classification are performed, as well as removing outliers from dense matches. Ultimately, we can accomplish more efficient and accurate matches. To validate the performance of the proposed method, we conduct experiments using multisource RSI datasets for comparison with other state-of-the-art methods under different scenarios, including noise-free, additive random noise, and periodic stripe noise. Comparative results indicate that the proposed method has a more well-balanced performance and robustness. The proposed method contributes a valuable reference for solving the difficult problem of noise image matching. The code is available athttps://github.com/liyuan-repo/RMmodel.

Psychophysiological computing can be utilized to analyze heterogeneous physiological signals with psychological behaviors in the Internet of Medical Things (IoMT). Since IoMT devices are generally limited by power, storage, and computing resources, it's very challenging to process the physiological signal securely and efficiently. In this work, we design a novel scheme named Heterogeneous Compression and Encryption Neural Network (HCEN), which aims to protect signal security and reduce the required resources in processing heterogeneous physiological signals. The proposed HCEN is designed as an integrated structure that introduces the adversarial properties of Generative Adversarial Networks (GAN) and the feature extraction functionality of Autoencoder (AE). Moreover, we conduct simulations to validate the performance of HCEN using the MIMIC-III waveform dataset. Electrocardiogram (ECG) and Photoplethysmography (PPG) signals are extracted in the simulation. The results reveal that the proposed HCEN can effectively encrypt floating-point signals. Meanwhile, the compression performance outperforms baseline compression methods.

To support diverse Internet of vehicles (IoV) services with different quality of service (QoS) requirements, network slicing is applied in vehicular networks to establish multiple logically isolated networks on common physical network infrastructure. However, dynamic and efficient radio access network (RAN) slicing adapting to the dynamics of vehicular networks remains challenging. The diverse applications make multi-dimensional resource requirements, which will result in the resource allocation more complicated. In addition, the system needs to frequently adjust the resources of slices, which will cause additional slicing overhead. Thus, to solve the above problems, we propose a resource allocation strategy by using multi-agent reinforcement learning to allocate resources in vehicular networks. Firstly, the cost composition of RAN slicing is analyzed, and the optimization problem is formulated to minimize the long-term system cost. Then, we transform the resource allocation problem into a partially observable Markov decision process. Finally, we propose a multi-agent deep deterministic policy gradient based resource allocation algorithm to solve it. All base stations are treated as independent agents, and they cooperatively allocate spectrum and computing resources. Simulation results show that the proposed strategy reduces the system cost effectively compared to the benchmarks, and the average QoS satisfaction rate achieves 96.5%.

Mobile Crowd Sensing (MCS) is a data collection technology in which workers finish tasks and get payment. In MCS, some tasks are not preferred workers due to their remote locations or cheap prices, which leads to a huge proportion of unpopular tasks. Although increasing tasks payment is an effective to increase task popularity, however, it may decrease platform utility. In this work, we introduce bundling into MCS to solve this problem. Specially, a Task Bundling Reorganization Mechanism (TBRM) is proposed. In TBRM, unpopular tasks are properly bundled with popular tasks to maximize the minimum of both the number of task completions and expected profit. The TBRM is separated into two phases: the area selection phase and the rule selection phase. First, the randomly generated solution is input into the area selection phase, which selects the portion of the bundle that needs to be reorganized; then, the results of the area selection phase is regarded as input of the rule selection phase, which selects the appropriate task to reorganize; finally, the TBRM repeats this process until convergence. Experimental results demonstrate the effectiveness of the TBRM mechanism.

To improve the compression performance of screen content coding, extension coding standards (HEVC-SCC, VVC-SCC) have been developed. However, considering the compression ratio alone may lead to packet losses in bitstreams which may cause plenty of images decoded incorrectly, degrading the video quality at the receiver side. Thus, it urgently needs to study source-channel jointly coding scheme of screen content video. The most significant challenge lies in the complex spatial-temporal characteristics of screen content video, which complicate the creation of an accurate end-to-end distortion model. In this article, we delve into the traits of screen content video and construct an end-to-end distortion model. Building upon this, we introduce an error resilient coding scheme specifically for screen content video. More specifically, we first consider the characteristic of non-stationary temporal domain variation and classify the screen content images into three types of frames using a fast block-searching method. We then propose an adaptive error concealment method, taking into account the spatial-temporal prediction characteristics. Following this, we derive a pixel-level end-to-end distortion model and incorporate it into the rate distortion optimization process. Our experimental results reveal that, compared to state-of-the-art methods, our proposed method significantly enhances both objective and subjective quality across a variety of channel conditions.

Federated Learning (FL) serves as a machine learning paradigm where distributed devices collaboratively train on local data, with their models subsequently aggregated on a central server. However, challenges arise due to unreliable communication channels, potential sign errors in model parameters, data heterogeneity, and resource limitations that can hinder full client participation. In this paper, firstly, we address these issues by proposing an optimization objective that minimizes FL loss while taking into account constraints on delay and energy consumption. Secondly, to counteract the model drift caused by data heterogeneity and packet errors, we introduce a proximal term in the local training process and incorporate packet errors into the global aggregation phase. Finally, we establish a theoretical convergence upper bound for our FL algorithm in complex non-convex situations, providing insights to guide the formulation of client sampling strategies and ensure FL algorithm convergence. We validate our algorithm’s superior accuracy on the MNIST and CIFAR-10 datasets.

As an essential part of the future wireless networks, low earth orbit satellite networks (LEO-SN) is expected to achieve ubiquitous global networks access, in which the multibeam transmission is widely used to meet the increasing rate demand. However, in multibeam LEO-SN systems, the multiuser access and inter-group interference are crucial issues that need to be addressed urgently. To this end, we investigate the system weighted sum rate (WSR) maximization problem under the constraints of the user terminals (UTs) grouping, satellite total power budget, and minimum transmission rate requirements. For solving the problem, we propose a multiuser space-ground multicast group control (MU-SGMGC) scheme. Specifically, we first group all UTs into multiple multicast groups based on the channel correlation coefficient. Then, the group centers determination algorithm based on user distribution is proposed to ensure that each beam can cover all user groups. Finally, the beamformers designing problem is transformed into a difference-of-convex (DC) programming problem by utilizing auxiliary variables, and an iterative algorithm based on convex-concave procedure (CCP) is presented to solve the problem. Simulation results show that our proposed MU-SGMGC scheme has significant superiority in system WSR compared with the benchmark algorithms.

This paper investigates the Virtual Reality (VR) service delay performance for an edge-terminal cooperative system. A convolutional neural network (CNN)-based user interest analysis method is first proposed to characterize the content requesting behavior. Based on this, a service delay minimization problem is formulated with consideration of performance fairness among users. Thereafter, the caching and computing scheme is derived for each user with given communication resources, while a bisection-based communication resource allocation scheme is derived with given content caching and offloading information. With alternative optimization, a joint caching, computing and communication scheme is proposed. The effectiveness of the proposed scheme is finally validated by simulation results.

The proliferation of satellite launch technology has prompted the rise of Low Earth Orbit (LEO) satellite constellations (LSC) as an effective complement to improve network coverage. However, few LSC are designed to guarantee quality of service (QoS) within link budget constraints, leading to wasteful resource utilization. In this paper, we consider QoS and link budget as constraints and formulate the LSC design problem as a multi-objective optimization problem (MoP). We focus on optimizing the configuration of the LSC to achieve maximum seamless multi-coverage and link capacity while minimizing its cost. To improve the population diversity and approach the optimal solution, we propose an Improved Non-Dominated Sorting Genetic Algorithm-II (INSGA-II) to solve this MoP, then the optimal LSC is designed considering different elevation angle constraints. Furthermore, a performance comparison with existing state-of-the-art constellations is presented. The results show that the designed LSC exhibits comparable and even superior performance to Telesat and Kepler, while the constellation scale is only 0.64 times that of the Kepler system.

Within smart home systems, the heating, ventilation and air conditioning (HVAC) system plays a critical role in regulating the indoor thermal environment, however, HVAC, which consume 40% of total building energy, are very energy-intensive. It is essential to design HVAC control strategy that reduce energy consumption while maintaining a satisfactory thermal environment. Considering that the human ability to control the thermal environment is underutilized in the existing research, this paper proposes a framework that involves human in the loop (HITL) and reinforcement learning (RL) to improve HVAC strategy under human guidance. We formulate the optimization problem as a problem of minimizing energy cost and thermal comfort cost. A human behavior model is then created to simulate the human behavior in a variety of thermal environments. We propose a HITL- deep deterministic policy gradient (DDPG) algorithm for thermal comfort and HVAC energy optimization based on human guidance and DDPG. We built a simulation environment based on proposed framework for strategy train and performance evaluation. The results show that HITL-DDPG can reduce HVAC energy consumption by 38.7% while improving occupant thermal comfort by 31.1%.

As the Medical Internet of Things (MIoT) is rapidly evolving, increasing wearable devices are applied to collect various physiological signals for the purpose of medical applications. Currently, it's challenging to process multi-modal physiological signals on wearable devices because of their limitations of energy and computing resources, as well as the threat of data privacy leakage. To address aheadmentioned problems, this paper designs a novel scheme to process multi-modal physiological signals, namely Multi-modal Compression and Encryption Network (M-CEN) with GAN and AE. The proposed MCEN accomplishes the encryption and compression of data by the adversarial property of GAN and the feature extraction property of AE. The proposed scheme aims to protect user privacy and reduce the consumption of energy and computing resources. In addition, the proposed MCEN is simulated using the Pulse Transit Time PPG dataset. The results show that the proposed scheme can well encrypt the data, and the compression performance is improved by 36.3% and 61.3% compared to the CAE and WT Algorithm respectively.

Alzheimer's disease (AD) is a common neurodegenerative disease that damages the health of the aged. To precisely diagnose Alzheimer's disease, a widely-accepted approach is to extract the features of resting-state functional Magnetic Resonance Imaging (rs-fMRI). Existing work fails to effectively explore the spatial dependency among brain regions and the temporal dynamics of brain activity. Furthermore, these methods are also limited by the scale of the dataset. This paper proposes a novel transformer-based method, namely, spatiotemporal graph transformer network (STGTN), which can effectively extract spatiotemporal features of rs-fMRI for accurate diagnosis. STGT-N unites temporal transformer and spatial transformer, which incorporates functional connectivity (FC) of rs-fMRI as edge features in the constructed brain graph. To break the limitation of sample size, we use adversarial training to generate adversarial examples (AEs) by the proposed STGTN. Experiments are conducted based on the ADNI dataset. The results show that the proposed model achieves accuracy of 92.58% for the task of normal control (NC) vs. AD classification, and 85.27 % for the task of early mild cognitive impairment (eMCI) vs. late mild cognitive impairment (lMCI) classification, respectively.

Vehicular networks require massive communication connections between vehicles and infrastructure to support the high data rate vehicular service applications. However, due to the obstruction of buildings in urban areas, the channel capacity of vehicle-to-infrastructure (V2I) links will be deteriorated. Thus, intelligent reflecting surface (IRS) is introduced to aid vehicular communications to increase the channel capacity of V2I links. In this paper, we aim to maximize the sum V2I capacity by jointly optimizing the transmit beamforming matrix at the base station (BS) and the phase shifts at the IRS. Most of the existing works adopt alternating optimization-based iterative algorithms to tackle the joint beamforming and phase shift optimization problem, which suffer from high computational complexity. Therefore, we propose an unsupervised learning (UL)-based algorithm with a two-stage network architecture to address the joint optimization problem. The network architecture consists of a two-stage transformer network, which can implicitly learn the spatial and temporal features of historical channels to further improve the learning performance. Simulation results show that the proposed UL-based algorithm can obtain the comparable performance with much lower computational complexity compared with the conventional alternating optimization-based iterative algorithm.

The growth of the Internet of Things has given rise to various innovative services such as self-driving vehicles, remote operations, immersive technologies, tactile Internet and automated factories. Machine Type Communication (MTC) data, primarily transmitted through short packets, is the backbone of these applications. However, short packets have limited error correction capabilities, thus it is necessitating the use of retransmission techniques to increase the reliability of Short Packet Communication (SPC). While retransmissions exploit time diversity to lower the bit error rate, excessive retransmissions can degrade system performance. Consequently, refining retransmission policies is a crucial research area for SPC systems. In this paper, an SPC system model with Incremental Redundancy Hybrid Automatic Repeat reQuest (IR-HARQ) is studied to optimize the number of retransmissions under various conditions by determining an ideal block-length selection policy. A Probabilistic Q-learning (PQL) algorithm based on the two-factor theory is proposed to determine the optimal block length selection by striving to optimize the retransmission. Simulation results show that the proposed algorithm can effectively improve the performance of retransmission system.

In the Healthcare Internet of Things (H-IoT), Federated Learning (FL) is a promising solution for processing huge amounts of medical data. At present, FL applied in H-IoT still faces many challenges such as low training efficiency and high data privacy risk. In this work, we develop a three-layer FL architecture, which introduces split learning to both prevent the leakage of medical data and improve training efficiency according to the inherent properties of Neural Networks (NN). Moreover, we formulate a long-term optimization problem with the goal of accelerating training speed of models in H-IoT. Then, an online model partitioning algorithm namely Privacyaware Model Partitioning Algorithm (PMPA) is derived based on Lyapunov optimization theory that enables mobile devices of the FL architecture to efficiently train local models and protect the data privacy. Furthermore, the simulation results show that compared with traditional FL, the local training delay of the proposed algorithm can be reduced by 28.94% and 39.89%, respectively.

Mobile crowdsensing (MCS) has become an attractive issue in recent years. Most existing researches either select opportunistic sensing or participatory sensing for task execution, which will lead to the problem of restricted task locations or high cost. In this work, we propose a complementary hybrid worker selection method for MCS, where workers complete tasks in different sensing modes, namely opportunistic and participatory sensing. The proposed worker selection method contains two phases. In the opportunistic worker selection phase, an updated iterative algorithm is designed to select a low-cost and high-coverage opportunistic worker set. Specifically, when an opportunistic worker is selected, the algorithm will update the coverage of the remaining candidate opportunistic workers on the sensing task. In the participatory worker selection phase, we design an algorithm that combines group and match to solve the problem of restricted task locations. Specifically, we group the sensing tasks that opportunistic workers have failed to cover and recruit participatory workers to complete the sensing tasks in the groups. Experiments on a real dataset prove that the proposed method outperforms other benchmark methods.

In this paper, we investigate the reliability of intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications in the case of limited UAV energy. Under constraints of the UAV energy and the channel decoding error rate, we formulate a reliability maximization problem by jointly optimizing the IRS’s scheduling, the UAV’s trajectory, the IRS’s phase shift, and the UAV’s transmit power. Since the partial constraints of the problem are strictly nonconvex and its variables are coupling, the problem is difficult to convert to a nonconvex problem. Therefore, we propose a chaotic adaptation hybrid whale optimization algorithm (CAHWOA) to solve the problem. CAHWOA is implemented by using alternately the chaotic adaptation whale optimization algorithm (CAWOA) and the binary optimization algorithm (BWOA). Simulation results demonstrate that the joint optimization of IRS and UAV can improve the system communication reliability by almost 32% compared with the two baseline schemes. CAHWOA can improve the convergence rate by nearly 20% and enhance the optimization-seeking accuracy by about 0.04 compared with the three baseline algorithms.

Low earth orbit (LEO) satellite networks have become one of the hot research areas as an essential part of satellite communication networks. The dynamic topology and unbalanced traffic demand may lead to intersatellite link (ISL) congestion; thus, improving network load balancing performance is one of the key issues to be addressed in LEO satellite networks. We propose a load-balanced collaborative offloading (LBCO) strategy to achieve a balanced traffic distribution in LEO satellite networks. LBCO strategy consists of two algorithms, namely, channel-aware gradient fair association (CAGFA) algorithm and ISLs collaborative offloading (ISLCO) algorithm. The CAGFA algorithm aims to maximize the aggregate weighted utility, and the ISLCO algorithm aims to achieve the traffic offloading and download observation data from the LEO satellite network. Specifically, we first determine the actual downloading satellite set and neighboring satellite set by constructing an earth station (ES) time-share graph and a space–time topology graph. Then, the LBCO strategy uses the CAGFA algorithm to obtain the optimal satellite terminal association indicator and the load of downloading satellites. Finally, the ISLCO algorithm is proposed to achieve proportional offloading of traffic among the neighboring satellites and download massive observation data. Simulations show that the proposed CAGFA algorithm improves the weighted utility by 3.3% and the convergence by 47.6% compared with the benchmark stochastic gradient descent-based association (SGDA) algorithm. We also validate the performance of the LBCO strategy by data download throughput, which performs better than the other benchmark algorithms under three different load scenarios.

Demand-oriented data service can provide the physical entity information for Internet of Things (IoT) users conveniently and quickly. The traditional cloud-oriented data service architecture has been inapplicable to the state time-varying and privacy-sensitive entity data in IoT due to long response delay and the risk of privacy leakage for the entities and users, respectively. The edge-based architecture lacks global service function although it can alleviate problems with cloud services. Moreover, existing demand-oriented data service ignores the characteristics of “thousands of people have thousands of faces” and the implicit intents of users, which results in limited service quality and weak user experience. To solve the above problems, a personalized secure demand-oriented data service scheme is proposed. Specifically, an edge-cloud collaborative architecture is designed to realize privacy-preserving, timely response, and personalized search combining the advantages of edge and cloud. To achieve the personalized service for IoT users, a time span fused personalized ranking method (TSFPR) is proposed to deeply perceive individual demands via mining user preferences with temporal evolution characteristics. Finally, an edge-cloud collaborative personalized secure data service approach (ECPSS) oriented different search modes is presented to achieve encryption data matching and personalized reranking, thereby improving the service quality of the IoT system synthetically. Security analysis and simulation demonstrate the effectiveness of the proposed method in terms of privacy preserving, data service time, and personalized performance.

The metaverse aims to build an immersive virtual reality world to support the daily life, work, and recreation of people. In this survey, the status quo of the metaverse is investigated, and the technical framework of the metaverse is introduced from three aspects: 1) the generation of virtual worlds; 2) the connection of virtual and real objects; and 3) the transmission of data. Specifically, this survey first discusses the development and challenges of the related technologies for virtual world generation methods from three aspects: 1) the 3-D world generation; 2) immersive human–computer interaction experience; and 3) ecosystem. Second, we investigate the status quo of extended reality (XR), motion capture, and brain–computer interface technologies and evaluate the potential and research directions of these entrance technologies for the metaverse. Finally, network and data transmission technologies for the metaverse are reviewed from the Internet of Things (IoT), 5G/6G wireless, and edge computing aspects, the demand side of the metaverse in virtual-reality interpromotion, big data processing, and low-latency networking is discussed, and promising research hotspots are identified.

The high-speed movement of nodes and the burstiness of interactions in the Internet of Vehicles pose huge challenges to the trusted vehicle collaboration and data sharing. Aiming at the disadvantages of existing authentication mechanisms and trust management models for connected vehicles, this article proposes a trust management model enabled by blockchain to ensure the traceability, nontampering, unforgeability, and transparency of vehicular interactions. The proposed trust management model leverages Dirichlet distribution, reputation regression, and revocation punishment to objectively and accurately reflect the trust status of vehicles. Simulation results on real-world data sets show that the proposed trust management model advantageously improves the accuracy of malicious vehicle detection and the attack resistance of connected vehicles.

Federated learning (FL) is a distributed machine learning paradigm that ensures data do not leave local devices. Data sharing problems can be addressed by FL in untrusted environments, e.g., the Internet of Vehicles (IoV). However, FL needs to frequently exchange massive parameters to achieve preset model goals. In addition, the change in bandwidths and the delay of data communications due to user mobility challenge the synchronization of model parameters. In this article, an efficient hierarchical asynchronous FL (EHAFL) algorithm is proposed to adjust the encoding length dynamically according to the bandwidth and reduce the communication cost substantially. A dynamic hierarchical asynchronous aggregation mechanism is proposed leveraging gradient sparsification and asynchronous aggregation techniques to further reduce the communication costs and improve the aggregation efficiency of the global model. Simulation results on MNIST and real-world data sets show that our proposed solution can reduce the communication costs by 98% while only compromising the model accuracy by 1%.

Limited by the storage and computing capacity of Internet of Things (IoT) devices, outsourcing encrypted entity data has become a prevalent trend. The existing IoT entity search methods lack the integration and utilization of both edge and cloud resources and the protection of user privacy. Besides, the traditional searchable encryption mode is inapplicable to state-time-varying entities in IoT. Therefore, in this article, an edge–cloud collaborative entity search method with privacy protection in IoT is proposed, fusing the advantages of edge and cloud resources to fulfill users’ needs for real-time search and privacy protection. Specifically, a secure search architecture and search method for edge–cloud collaboration is designed to support various needs of users, such as real-time search and global search. Then, an adaptive discrimination method for similar interested entities through attribute analysis and feature extraction is proposed to construct attribute-distinguished encrypted index and query vector groups, enabling efficient entity search meanwhile ensuring fast index update. Simulation results demonstrate that the proposed method with privacy protection can effectively improve the efficiency of entity search in IoT while safeguarding user privacy.

With the massive and rapid deployment of Internet of Things (IoT) devices, the number of IoT devices in the cloud radio access network (C-RAN) has increased dramatically. It is intractable to provide deterministic Quality-of-Service (QoS) guarantee for task processing due to the task burstiness and the time-varying channels. In order to guarantee latency requirements of energy-limited IoT devices in C-RAN, this article proposes a statistical latency guarantee scheme for task computation. With the assistance of densely distributed remote radio heads (RRHs) providing wireless power transfer to the IoT devices, a wireless-powered edge computing C-RAN model is constructed. Then, the problem of minimizing task latency violation probability is formulated. With the help of effective capacity theory, the problem is decoupled into multiple subproblems, which are QoS parameter optimization, task offloading optimization, wireless power transfer, and energy allocation. Thereafter, low-complexity schemes are designed to jointly optimize the wireless power transfer, energy allocation, and task offloading. The effectiveness of the proposed statistical latency guarantee scheme is finally validated by extensive simulations.

This article focuses on the service freshness guarantee for cooperative sensing in the Internet of Everything. Specifically, the peak Age of Information (AoI) is first introduced to evaluate the information and service freshness. An analytical model is then constructed to decouple the components of peak AoI into the interarrival time and transmission time. With the knowledge of identical increments of update arrival and transmission process, a close bound of peak AoI violation probability is derived based on martingale theory. Furthermore, the impact of source node parameter configuration on the peak AoI violation probability are analyzed and a task allocation scheme is proposed to guide cooperative sensing. Numerical analysis validates the tightness of the peak AoI violation probability bound and the effectiveness of the proposed scheme in service freshness guarantee.

Federated learning (FL) is receiving much attention in the Healthcare Internet of Things (H-IoT) to support various instantaneous E-health services. Today, the deployment of FL suffers from several challenges, such as high training latency and data privacy leakage risks, especially for resource-constrained medical devices. In this article, we develop a three-layer FL architecture to decrease training latency by introducing split learning into FL. We formulate a long-term optimization problem to minimize the local model training latency while preserving the privacy of the original medical data in H-IoT. Specially, a Privacy-ware Model Partitioning Algorithm (PMPA) is proposed to solve the formulated problem based on the Lyapunov optimization theory. In PMPA, the local model is partitioned properly between a resource-constrained medical end device and an edge server, which meets privacy requirements and energy consumption constraints. The proposed PMPA is separated into two phases. In the first phase, a partition point set is obtained using Kullback-Leibler (KL) divergence to meet the privacy requirement. In the second phase, we employ the model partitioning function, derived through Lyapunov optimization, to select the partition point from the partition point set that that satisfies the energy consumption constraints. Simulation results show that compared with traditional FL, the proposed algorithm can significantly reduce the local training latency. Moreover, the proposed algorithm improves the efficiency of medical image classification while ensuring medical data security.

Wearing masks can effectively inhibit the spread and damage of COVID-19. A device-edge-cloud collaborative recognition architecture is designed in this paper, and our proposed device-edge-cloud collaborative recognition acceleration method can make full use of the geographically widespread computing resources of devices, edge servers, and cloud clusters. First, we establish a hierarchical collaborative occluded face recognition model, including a lightweight occluded face detection module and a feature-enhanced elastic margin face recognition module, to achieve the accurate localization and precise recognition of occluded faces. Second, considering the responsiveness of occluded face detection services, a context-aware acceleration method is devised for collaborative occluded face recognition to minimize the service delay. Experimental results show that compared with state-of-the-art recognition models, the proposed acceleration method leveraging device-edge-cloud collaborations can effectively reduce the recognition delay by 16% while retaining the equivalent recognition accuracy.

This paper investigates user grouping and beam-forming design in multi-beam low earth orbit (LEO) satellite communication (SATCOM) systems. To serve a great many user terminals (UTs) with a limited number of beams and improve the system performance, we formulate the weighted sum rate (WSR) maximization problem subject to the constraints of the UTs grouping, the satellite total power, and the minimum rate requirements of UTs. For solving this problem, we propose a multi-group multi-beamforming (MGMBF) scheme. In this scheme, all UTs are firstly adaptively grouped based on the channel correlation coefficients. Further, the beam centers are determined to ensure that all UTs are covered. After UTs grouping, slack variables are introduced to convert the beamforming design into a difference-of-convex (DC) programming problem. Moreover, an iterative algorithm is presented to solve the problem based on the convex-concave procedure (CCP), in which the beamforming vectors and slack variables are updated jointly by solving the convex sub-problem. Simulation results demonstrate that the MGMBF scheme improves the WSR by 25.1% compared with the MBIM algorithm, verifying the significant advantages of the proposed scheme.

Epilepsy is one of the most common neurological diseases in the world. Feature extraction of electroencephalogram (EEG) is very important for predicting epileptic seizures. Conventional technologies of EEG signals analysis mostly utilized supervised learning methods with a mass of labeled data. However, annotating data is a time-consuming and expensive process. In this paper, we propose a novel unsupervised feature learning method based on variational autoencoder, namely, residual convolution variational autoencoder (RCVAE), which aims to improve the accuracy of epileptic seizure prediction. RCVAE automatically extracts important features and reconstructs the spatiotemporal EEG signal, reducing the learning difficulty with residual network structure. In addition, this work also utilizes the Pearson correlation coefficient and the reconstructed loss function, which are used to evaluate the quality of the reconstructed signal. Finally, the performance of the proposed model is verified on the CHBMIT dataset, the accuracy rate is up to 96.17%, and the false alarm rate is only 0.015.

To address the impact caused by the large increase of data traffic in internet of vehicles (IoV), vehicular edge caching, as an effective technology to alleviate the above issue, attracts extensive attention. However, most existing studies in vehicular edge caching only considered the temporal feature of content popularity, which will impact its accuracy. Thus, we propose a spatial-temporal correlation multi-agent caching policy (STC-MACP) to dynamically determine where to cache and what to cache. Firstly, we predict the content popularity based on the spatial-temporal correlation of the historical content requests. Secondly, multi-agent reinforcement learning (MARL) is applied to solve the caching decision optimization problem to obtain the optimal caching policy with maximizing delay reduction. Finally, we conduct the IoV simulation environment, and simulation results show that the STC-MACP can effectively reduce the content access delay. Compared with the most popular caching (MPC), the content access delay of STC-MACP is decreased by 28% when the Zipf parameter is 0.8.

Low earth orbit (LEO) satellites have a pivotal role in global data monitoring. However, one of the most challenges is load balancing between LEO satellite networks due to the frequent topology changes and uneven distribution of global ground users. Thus, we propose a channel-aware gradient fair association (CAGFA) strategy to maximize the aggregate weighted utility in a dynamic satellite environment. Specifically, we first determine the downloading and neighboring satellites by constructing an earth station (ES) time-share graph and a space-time topology graph, respectively. Then, the CAGFA strategy is designed to obtain the optimal satellite terminal association indicator, and intersatellite links (ISLs) collaborative offloading is used to achieve the traffic balance for the LEO satellite networks. Simulation results indicate that the proposed CAGFA strategy increases the weighted utility by 6.5% and the convergence by 34.7% compared with the root mean square propagation-based association (RMSPA) strategy.

In 6G Internet of Vehicles (IoV) system, Federated Learning (FL) is usually used to structure the joint training model between vehicles and RSU. However, due to the mobility of the vehicles, the link between vehicles is unstable and the parameters trained by FL are exchanged frequently, which may increase the communication overheads. Therefore, we propose a communication-efficient Federated Double Distillation (FedDD) framework in this paper. In particular, the cluster-heads are dynamically selected as the distributed learning clients combined with three-dimensional attributes to improve the collaborative transmission efficiency. Then, the knowledge distillation is further integrated into the federated learning framework to reduce communication overheads caused by the frequent parameters exchange in the instable link. The experimental results show that, compared with the benchmark FedAvg algorithm, the FedDD reduces the communication overheads by three orders of magnitude. Moreover, the FedDD improves the communication efficiency of FL while sacrificing only a small amount of accuracy.

The mobile crowdsensing (MCS) is an emerging sensing paradigm based on the mobile device. For location-dependent sensing tasks (LDSTs), when tasks are farther with low payment from workers, they can be difficult to complete. The completion rate of this unpopular task has always been an issue. Most existing researches mainly focus on how to increase payment for unpopular tasks, but the platform may suffer from it, because an incorrect increase results in an inability to raise the number of completed tasks. In this paper, we present a task bundling reorganized mechanism (TBRM) to improve the platform utility of MCS system. In the proposed mechanism, the unpopular and popular tasks are properly bundled to improve the platform utility. To decrease searching time for suitable bundles, two sub-policies are respectively utilized to design TBRM based on reinforcement learning: the area selection policy and the rule selection policy. Experimental results demonstrate that TBRM outperforms the three benchmark mechanisms, which reveals that TBRM can effectively bundle unpopular tasks and improve platform utility.

In the field of mobile crowd sensing (MCS), worker selection is a key research issue and has progressively gained considerable interests in the academic community in recent years. The goal of worker selection is to choose the superior workers for tasks that require high-performance characteristics. To solve the problems of long delay and poor perceived quality, we present a worker selection architecture for a recommendation system applied to the MCS system. A worker selection algorithm with high quality of service (QoS) is designed within the architecture, which considers the worker’s reputation and willingness attributes to address the challenge of efficiently selecting excellent workers. Based on these two attributes, we then compute worker QoS and develop a three-dimensional tensor to optimize the worker’s service. Finally, we get a continuously updated list of workers. Extensive experiments on real-world datasets show that the proposed algorithm performs better than the benchmarks, including random, greedy, and matrix-based algorithm. The results indicate that the proposed algorithm’s efficiency has risen by 31% compared to the matrix-based algorithm.

The computation-intensive applications pose unprecedented demands on the Internet of Vehicles (IoVs). How to address the delay constraint to execute the computation tasks effectively becomes a significant issue for this scenario. Compared with remote cloud, edge servers reduce the delay by being deployed close to vehicles. However, most edge servers are connected to fixed access points, which leads to the inflexible edge computing architecture. Considering the dynamics of vehicles’ location and service request, it is a promising paradigm that multi-vehicle compute the task collaboratively by utilizing the vehicles’ available computing resources. In this paper, by jointly considering the local execution, V2V offloading, and multi-vehicle collaboration, we determine the optimal task partition ratio after the cooperative vehicles are selected. Then, double deep Q-network (DDQN) is used to take the optimal dual actions. Finally, we develop a multi-vehicle intelligent collaborative computing strategy (MV-ICCS) to minimize the total system delay. Simulation results show the advantage of the proposed strategy and evaluate the system performance.

The ever-increasing user requests for video services have posed a great challenge to the cellular networks, and the emergence of various new services also puts forward higher requirements to the mobile networks. By caching video contents in cache-enabled heterogeneous networks, the delivery delay of content and the stress of backhaul links can be improved conspicuously. However, how to store diverse contents has got much attentions in the past decade. In this paper, considering the time-varying user requests, a hierarchical cooperative caching strategy with user preference is proposed. Firstly, the caching of content is modeled as a delay optimization problem. Secondly, the historical request data is used to predict the user preference, and the singular value decomposition (SVD) model is further used to predict the missing rating data. Thirdly, both the user preference and rating matrix are used to optimize the caching strategy. Finally, the proposed caching strategy is validated using the MovieLens dataset, the results reveal that the proposed strategy improves the delay performance by at least 35.3% compared with the benchmark strategies.

To support the diversified Quality of Service (QoS) requirements of application scenarios, network slicing has been introduced in the mobile cellular network. It allows mobile cellular network operators to accomplish the creation of multiple logically isolated networks on common network infrastructure flexibly depending on specified demands. Meanwhile, in Internet of Vehicles (IoV), it is very intractable to supply a stable QoS for the vehicles, especially for the dynamic vehicular environments. Thus, we investigate the IoV slicing problem in this article, and propose a QoS guaranteed network slicing orchestration, namely, the long short-term memory-based deep deterministic policy gradient algorithm (LSTM-DDPG), to ensure the stable performance for the slices. Specifically, we first decouple the resource allocation problem into two subproblems. After that, the deep learning and reinforcement learning (RL) are used to allocate resources collaboratively to solve these two questions. We use deep learning LSTM to track the characteristic of the long-term vehicular environment changing, and the RL algorithm DDPG is utilized for online resource tuning. Extensive simulations have proved the effectiveness of the LSTM-DDPG, which can offer stable QoS to the vehicles with a probability greater than 92%. We also demonstrated the adaptiveness of the proposed orchestration with different slicing environments, and the performance is always optimal compared to that of other algorithms.

The virtualized fiber wireless broadband access networks (V-FiWi) paradigm, effectively embedding heterogeneous virtual networks (VNs) originated from the service provider (SP) into shared substrate network (SN) provided by infrastructure provider (InP), plays a tremendous role in meeting the differentiated requirements between wireless frontend subnetwork and fiber backhaul subnetwork to achieve the interoperability of heterogeneous resource allocation. However, most of the existing V-FiWi integration systems mainly focused on the virtual network request (VNR) acceptance ratio, InP revenue, and substrate resource utilization, and they ignored the crucial issue called higher energy consumption cost, which was resulted from the imbalanced consumption of the substrate resource between the arrival and departure of VNR. In this article, we devote to exploring the energy-aware virtual network migration (EA-VNM) problem over the FiWi access technology, aiming to reoptimize the energy consumption while maintaining the high InP revenue and the large substrate resource utilization. In response to this issue, we first represent the service-oriented V-FiWi broadband access network architecture from the perspective of computing, storage, and network resource constraints, in which a migration model consisting of migration node and migration time is explained in detail. Then, we propose an enhanced KM-based energy-aware node migration (EKM-ENM) algorithm to economize on more bandwidth resource. More specially, the EA-VNM technology consists of network topology attributes and global network resources-based node-ranking measurement (NRM) phase, maximum weight matching-based node migration phase, and energy-aware link migration phase via Dijkstra shortest path algorithm. Finally, a rather large number of simulations are analyzed and evaluated numerically. Simulation results suggest that the proposed EKM-ENM algorithm outperforms the traditional embedding algorithms in terms of saving energy cost, decreasing time complexity, and improving VNR acceptance ratio.

In order to make full use of the network data and guarantee user privacy simultaneously, federated learning (FL) is proposed to enable distributed intelligence for local nodes without sharing data with each other. However, in practice, due to resource limitations, traditional FL suffers from node scheduling and parameter transmission failure, which not only affects the final performance but also further reduces the fairness of the participating nodes. This article addresses the challenge and proposes an FL method to enhance the performance of FL on the basis of guaranteeing the fairness of the local nodes in a resource-constrained Internet of Things (IoT) network. Specifically, an analytical model is first constructed to characterize the performance of FL with joint considerations of node fairness, unreliable parameter transmissions as well as resource limitations. Thereafter, a statistically reweighted aggregation (SRA) scheme is proposed for parameter aggregation and the corresponding model is proved to be unbiased to that based on ideal parameter transmissions. With the knowledge of time dependency of the global model, we further extend SRA and propose a reliable SRA (RSRA) scheme. Additionally, we prove RSRA is able to achieve higher stability performance than SRA in model training. Furthermore, the convergence bound of the proposed RSRA is derived analytically, based on which an adaptive local training scheme is proposed under a given resource budget. Finally, extensive experiments are carried out with a public data set to validate the effectiveness of the proposed scheme with comparisons of other baseline schemes.

The Ever-Increasing evolution of technologies in communication, artificial intelligence (AI), manufacturing, etc., is promoting a new wave of industrial revolution. Industrial Internet of Things (IIoT) has been considered as a critical stimulator for both science and economics by amounts of countries.

Collaborative perception enables autonomous vehicles to exchange sensor data among each other to achieve cooperative object classification, which is considered an effective means to improve the perception accuracy of connected autonomous vehicles (CAVs). To protect information privacy in cooperative perception, we propose a lightweight, privacy-preserving cooperative object classification framework that allows CAVs to exchange raw sensor data (e.g., images captured by HD camera), without leaking private information. Leveraging chaotic encryption and additive secret sharing technique, image data are first encrypted into two ciphertexts and processed, in the encrypted format, by two separate edge servers. The use of chaotic mapping can avoid information leakage during data uploading. The encrypted images are then processed by the proposed privacy-preserving convolutional neural network (P-CNN) model embedded in the designed secure computing protocols. Finally, the processed results are combined/decrypted on the receiving vehicles to realize cooperative object classification. We formally prove the correctness and security of the proposed framework and carry out intensive experiments to evaluate its performance. The experimental results indicate that P-CNN offers exactly almost the same object classification results as the original CNN model, while offering great privacy protection of shared data and lightweight execution efficiency.

The value of trajectory data lies mainly in the spatio-temporal correlation. However, the existing privacy protection methods ignore the spatio-temporal correlation of trajectory data, resulting in a large error in trajectory proportion estimation and Top-K classification. For the privacy of truck trajectory in intelligent logistics, the location and trajectory data perturbation method based on quadtree indexing is proposed, which leverages location generalization and local differential privacy techniques. Our proposed algorithms are suitable for datasets with a large sample space and can protect the trajectory privacy of truck drivers while preserving the strong correlation between adjacent spatio-temporal nodes in the trajectory. The results of simulation on a real trajectory dataset show that the proposed methods not only meet the trajectory privacy requirements of users but also have a good performance in trajectory proportion estimation and Top-K classification.

Collaborative caching in edge-user architecture has been regarded as one of the most promising technologies to release the pressure of core networks and reduce the content download delay. However, the caching resources at edge servers and devices are limited, so how to utilize their cache space efficiently has become a significant issue. This paper introduces a three-tier caching framework consisting of a macro-cell base station (MBS), multiple small-cell base stations (SBSs), and user equipments (UEs). In this framework, by combining physical and social attributes, we propose a directed graph-based edge-user collaborative caching (DG-EUCC) strategy to minimize the content download delay. Specifically, the wireless communication networks between the different types of nodes at the SBSs tier and the UEs tier are simplified to a one-tier directed graph (DG) with social attributes, to realize the simplification of the system model. Further, we design a DG-based collaborative caching strategy to minimize the content download delay, where each node caches the most popular contents according to the weighted content popularity set. Simulation results show that, compared with the benchmark strategies, the proposed DG-EUCC strategy can effectively reduce average download delay.

To guarantee heterogeneous performance requirements of diverse vehicular services, it is necessary to design a full cooperative policy for both vehicle to infrastructure (V2I) links and vehicle to vehicle (V2V) links. This paper investigates how to improve the quality of experience (QoE) of the V2I users for video services while satisfying the delay requirements of both V2I and V2V links. In specific, a QoE maximization problem is formulated with consideration of vehicular collaboration where task offloading decision, channel reuse decision and power allocation of V2V users are all included. A multi-agent reinforcement learning (MARL) framework is then designed, where a new reward function is proposed to evaluate the utility of the considered network. Thereafter, a proximal policy optimization approach is proposed to enable each V2V user to learn policy individually with the shared global network reward. The effectiveness of the proposed approach is finally validated with comparison of other baseline approaches through extensive simulation experiments.

Mobile edge computing (MEC) enables resource-constrained mobile devices (MDs) to offload their tasks onto nearby edge servers. However, there exists a profit allocation problem between users and edge nodes (ENs) due to the limi-tations of ENs computing capacity and spectrum resources. In this paper, we propose an auction pricing-based MEC offloading strategy to maximize the profit of ENs. Firstly, we design an overall auction process using the binary offloading model by considering MDs battery capacity, basic profit, and tasks tolerable delay. Secondly, the bidding willingness of MDs in each round of auction are given on the premise of effectively ensuring users rationality. Finally, an auction pricing-based task offloading strat-egy is proposed, in which the winner of a single-round auction can offload its computation task to the ES. Simulation results verify the performance of the proposed strategy. Compared with the VA algorithm, the profit obtained by ENs has increased by 23.8%.

Targeted sentiment analysis aims to predict the sentiment polarity of the target in a sentence. Most traditional Graph Neural Network-based methods have focused only on the syntactic dependency information of sentences. However, they ignore the position information of words in the linear form of sentences, which leads the model to paying attention to the irrelevant syntactic dependency information to the target. To attenuate the irrelevant information, a novel model called Position-aware Dual Relational Graph Attention Network (PDRGAT) is proposed. Firstly, introducing the position-aware weight window of the syntactic dependency information to make the model pay more attention to the local syntactic information of words that neighbor the target. Secondly, a dual relational attention mechanism combining syntactic and position information is proposed. Experiments show that our model can effectively attenuate the irrelevant syntactic information and outperform state-of-the-art baselines on Accuracy and Macro-F1.

How to improve the content caching efficiency and user coverage rate at the same time is a fundamental challenge in edge caching networks. This paper studies an edge caching scheme based on user interest to address this issue. Specifically, a group interest aware caching framework is first developed. An individual interest prediction scheme is then proposed by merging factorization machine (FM) model and multi-layer perceptron (MLP) model, where both low-order and high-order features can be well learned simultaneously. Thereafter, the group interest is represented by a weighted average approach, based on which a caching scheme is further proposed. Moreover, the effectiveness of the proposed method is validated by extensive experiments with a real-world dataset.

Network slicing can support the diverse use cases with heterogeneous requirements, and has been considered as one of the key roles in future networks. However, as the dynamic traffic demands and the mobility in vehicular networks, how to perform RAN slicing efficiently to provide stable quality of service (QoS) for connected vehicles is still a challenge. In order to meet the diversified service request of vehicles in such a dynamic vehicular environment, in this paper, we propose a two-timescale radio resource allocation scheme, namely, LSTM-DDPG, to provide stable service for vehicles. Specifically, for the long-term dynamic characteristics of service request from vehicles, we use long short-term memory (LSTM) to follow the tracks, such that the dedicated resource allocation is executed in a long timescale by using historical data. On the other hand, for the impacts of channel changes caused by high-speed movement in a short period, a deep reinforcement learning (DRL) algorithm, i.e., deep deterministic policy gradient (DDPG), is leveraged to adjust the allocated resources. We prove the effectiveness of the proposed LSTM-DDPG with simulation results, the cumulative probability that the slice supplies a stable performance to the served vehicle within the resource scheduling interval can reach more than 90%. Compared with the conventional deep Q-networks (DQN), the average cumulative probability has increased by 27.8%.

Mobile CrowdSensing (MCS) aims to accomplish task requesters sensing tasks by recruiting quantities of workers. Thus, it is crucial to match and distribute sensing tasks to workers efficiently. We consider the influence of transmission outage probability to obtain the best matches and maximize overall social welfare. The workers cannot receive sensing tasks if the outage probability is large, in this case, the social welfare will be smaller. Thus, we maximize overall social welfare via four steps: winner selecting, matching, transmitting and pricing, which is developed as maximize social welfare (MSW) mechanism. Experiment results show that compared with benchmark algorithms, our mechanism transmit sensing tasks to far workers with a lower outage probability and achieve larger social welfare. Furthermore, outage probability is decreased by 19% and overall social welfare created by accomplishing sensing tasks is improved by 15%.

In order to mitigate the impact of explosively increasing data traffic on content request services in the Internet of Vehicles (IoV), edge caching technology is implemented in IoV to accelerate the response process of content requests and release the backhaul burden of the base station. However, the content popularity obtained by the traditional content popularity method cannot capture the requests of vehicles accurately due to the time-varying characteristics of the content popularity, which results in a relatively low cache hit ratio. Thus, this article proposes a cooperative caching strategy with content request prediction (CCCRP) in IoV, which precaches the contents requested by vehicles with greater probability in other vehicles or the roadside unit (RSU) to reduce the content acquisition delay. Specifically, vehicles are first clustered using the K-means method to simplify the process of vehicle requesting and content transmission. Then, content requests from vehicles are predicted using the long short-term memory (LSTM) networks according to the historical content request information. Finally, reinforcement learning method is adopted to solve the objective function to obtain the optimal caching decision, which improves the Quality of Service (QoS) of vehicle requests. Simulation results demonstrate that CCCRP can improve the cache hit ratio and reduce content acquisition delay effectively. For example, the cache hit ratio of CCCRP can be increased by 5% and 7% compared to the traditional LFU and LRU caching strategies when the Zipf parameter equals 0.7, respectively.

The flexible combination of pervasive portable smart devices and omnipresent high-speed access infrastructures has revolutionized the data sensing and knowledge acquisition in mobile crowdsensing (MCS), underpinning fine-grained city management and highly customizable Internet service applications. However, MCS applications are still confronted with unsolved challenges, such as task assignment, privacy risks, and misbehavior detection. In light of this, this article proposes PETA, a privacy-preserving edge task assignment framework for MCS, leveraging the powerful edge servers deployed between users and the platform to cluster and manage users according to user attributes. Furthermore, group signature is employed by PETA to anonymize and verify user identities for privacy-preserving task assignments. The theoretical analysis and simulation results validate the performance of PETA on identity anonymity, malicious user detection, and task completion rate.

Emerging virtual reality/augmented reality games and self-driving cars necessitate accurate/responsive/private emotion recognition. Usually, traditional emotion recognition models are deployed at central servers, which results in the lack of abilities in generalization and covering the individual variation of clients. This paper proposes a responsive, localized, and private transfer learning based emotion recognition framework under the cloud-edge-client collaborations. Additionally, a 3-dimensional channel mapping method is designed to aggregate features extracted from electroencephalogram (EEG) signals for the generic emotion recognition model, which is further localized and personalized using transfer learning. Simulation results validate the performance of the proposed TLER framework in reducing model training time and improving emotion recognition accuracy.

In mobile crowd sensing (MCS), the cloud as a single sensing platform undertakes a large number of communication tasks, leading to the reduction of sensing task execution efficiency and the risk of loss and leakage of users’ private data. In this paper, we propose a spatial ciphertext aggregation scheme with collaborative verification of fog nodes. Firstly, the cloud and fog collaboration architecture is constructed. Fog nodes are introduced for data validation and slices transmission, reducing computing cost on the sensing platform. Secondly, a multipath transmission method of slice data is proposed, in which the user identity and data are transmitted anonymously by the secret sharing method, and the data integrity is guaranteed by hash chain authentication. Finally, a spatial data aggregation method based on privacy protection is presented. The ciphertext aggregation calculation of the sensing platform is realized through Paillier homomorphic encryption, and the problem of insufficient data coverage in the sensing region is solved by the position-based weight interpolation method. The security analysis demonstrates that the scheme can achieve the expected security goal. The simulation results show the feasibility and effectiveness of the proposed scheme.

Caching state data of real-world entities just in the cloud without any distinction will cause search performance degrading, due to the characteristics of uncountable number of entities and time-varying state of entities in cyber-physical systems (CPSs). Considering the diverse time-varying features of CPS entities, an edge-cloud collaborative entity state data caching strategy toward networking search application in CPSs is proposed in this article. Specifically, an entity state feature extraction method is presented to mine underlying changing rules of CPS entities via raw entity state observation sequence. Then, an edge and cloud collaborative entity state data caching strategy is devised to improve the search accuracy of CPSs search service and reduce the search delay and energy consumption, in which entities are clustered first according to the time-varying degree of their state and then these state information are discriminately cached based on their belonging clusters. Simulation results validate the effectiveness of the proposed strategy in terms of real-time and accuracy performances.

In this paper, a video service enhancement strategy is investigated under an edge-cloud collaboration framework, where video caching and delivery decisions are made at the cloud and edge respectively. We aim to guarantee the user fairness in terms of video coding rate under statistical delay constraint and edge caching capacity constraint. A hybrid human-artificial intelligence approach is developed to improve the user hit rate for video caching. Specifically, individual user interest is first characterized by merging factorization machine (FM) model and multi-layer perceptron (MLP) model, where both low-order and high-order features can be well learned simultaneously. Thereafter, a social aware similarity model is constructed to transfer individual user interest to group interest, based on which, videos can be selected to cache at the network edge. Furthermore, a dual bisection exploration scheme is proposed to optimize wireless resource allocation and video coding rate. The effectiveness of the proposed video caching and delivery scheme is finally validated by extensive experiments with a real-world dataset.

To fulfill the diversified requirements of vehicular network services, we design an intelligent slice coordinator in this paper, which consists of two parts, service clustering and slice scheduling. In the first part, service clustering captures the Service Level Agreement (SLA) of services and clusters them based on K-means++ clustering algorithm according to the similarity of service requirement. Meanwhile, the services will be mapped into different slices. In slice scheduling module, we design the shared proportional fairness scheme (SPFS) to deal with the imbalance of radio resource utilization, and then further design the resource allocation algorithm based on linear programming obstacle method to solve the optimal slice weight distribution and maximize the slice load variation tolerance. Simulation results show that the SPFS has smaller average bit transmission delay (BTD) than the static slicing scheme, and the optimal slice weight distribution can be obtained under different user load distribution scenarios. The BTD gain achieves 1.5632 in the uniform user load scenario with 20 users per slice.

To reconcile the conflict between ceaselessly growing mobile data demands and the network capacity bottleneck, we exploit the terminal-edge-cloud collaboration to design a streaming distribution framework, SD-TEC, with the major objective to avoid streaming interruptions caused by inter-cluster handovers and corresponding user defections. First, the merge-and-split rule in the coalition game is employed for virtualized passive optical network clustering to structurally reduce the inter-cluster handover frequency. Second, the terminal-edge collaboration leverages device-to-device communications to sustain streaming services when inter-cluster handovers inevitably occur, reducing the time of possible streaming interruptions and improving the quality of experience of multimedia services. Lastly, the edge-cloud collaboration proactively caches streaming contents to alleviate the traffic congestion of peak hours and considers user priorities and buffer queue underflow/overflow to manage both fronthaul and backhaul resources. Simulation results validate the efficiency of our proposed SD-TEC in reducing the traffic congestion and streaming interruptions caused by inter-cluster handovers.

Vehicular networks have become a visible reality enabling information sharing between vehicles to enhance driving safety and provide value-added services to drivers and passengers. However, false information might be injected into the network because of defective sensors, malicious vehicles, and so on. Therefore, an efficient mechanism to guarantee the reliability of information used by vehicles is of great importance in vehicular networks. To solve this problem, this article proposes a context-awareness trust management model to evaluate the trustworthiness of messages received by vehicles to ensure bogus information will not influence the driving decision-making process. In the proposed scheme, the trust evaluation result of an evaluation request is determined by available related information and the evaluation strategy in the current situation, which is unaffected by the presence of conflicting evidence and the trust level of entities in the network. Moreover, we design a reinforcement learning model that allows vehicles to adjust the evaluation strategy so as to maintain an accurate evaluation result in different driving scenarios. Extensive experiments were conducted in different driving scenarios to verify the effectiveness of the proposed model. The results show that our model is adaptive to different driving scenarios with negligible time overhead, regardless of the proportion of malicious nodes in the network. Furthermore, compared with three types of state-of-the-art trust models in different scenarios, our scheme can achieve a higher evaluation precision rate with no more computational and communication overhead in nonrandom road conditions.

The emerging mobile edge computing (MEC) evolutionarily extends the cloud services to the network edge. In order to efficiently coordinate distributed edge resources, software defined networking (SDN) at the network edge has been explored to realize the integrated management of communication, computation, and cache (3C) resources. However, many research efforts, in software-defined edge networks, are mainly devoted to 1C or 2C resource sharing. Motivated by high service performance and user demands, we propose a user-centric edge resource sharing model for software-defined ultra-dense network (SD-UDN) where multiple MEC servers around small base stations (SBSs) can share their 3C resources through OpenFlow-enabled switches. In particular, the service models of MEC servers and users are formulated to optimize the service process by minimizing the service delay, which is NP-hard. To address this NP-hard issue, a service association model is constructed based on design structure matrix (DSM), and a simulated annealing algorithm is employed to further optimize the service association model for reducing time complexity and offering a near-optimal solution. Compared with traditional 1C or 2C resource sharing, the proposed edge resource sharing model can guarantee lower service delay for users.

It is enormously challenging to achieve a satisfactory balance between quality of service (QoS) and users’ privacy protection along with measuring privacy disclosure in social Internet of Things (IoT). We propose a privacy-preserving personalized service framework (Persian) based on static Bayesian game to provide privacy protection according to users’ individual security requirements in social IoT. Our approach quantifies users’ individual privacy preferences and uses fuzzy uncertainty reasoning to classify users. These classification results facilitate trustworthy cloud service providers (CSPs) in providing users with corresponding levels of services. Furthermore, the CSP makes a strategic choice with the goal of maximizing reputation through playing a decision-making game with potential adversaries. Our approach uses Shannon information entropy to measure the degree of privacy disclosure according to the probability of game mixed strategy equilibrium. Experimental results show that Persian guarantees QoS and effectively protects user privacy despite the existence of adversaries.

The diversified service requirements in vehicular networks have stimulated the investigation to develop suitable technologies to satisfy the demands of vehicles. In this context, network slicing has been considered as one of the most promising architectural techniques to cater to the various strict service requirements. However, the unpredictability of the service traffic of each slice caused by the complex communication environments leads to a weak utilization of the allocated slicing resources. Thus, in this paper, we use Long Short-Term Memory- (LSTM-) based resource allocation to reduce the total system delay. Specially, we first formulated the radio resource allocation problem as a convex optimization problem to minimize system delay. Secondly, to further reduce delay, we design a Convolutional LSTM- (ConvLSTM-) based traffic prediction to predict traffic of complex slice services in vehicular networks, which is used in the resource allocation processing. And three types of traffic are considered, that is, SMS, phone, and web traffic. Finally, based on the predicted results, i.e., the traffic of each slice and user load distribution, we exploit the primal-dual interior-point method to explore the optimal slice weight of resources. Numerical results show that the average error rates of predicted SMS, phone, and web traffic are 25.0%, 12.4%, and 12.2%, respectively, and the total delay is significantly reduced, which verifies the accuracy of the traffic prediction and the effectiveness of the proposed strategy.

Calcium signaling plays an important role in both physiological activities and engineered applications of molecular communication. Recent experimental studies in biology reveal that calcium signaling is closely related to mobility of biological cells. In this paper, we address communication-related issues of calcium signaling among a group of mobile cells. First, a mobility model of biological cells is established based on experimental studies in biology. Then, the mobility model is integrated with a widely accepted model of calcium signaling. Further, computer simulations are performed using the integrated model to examine the communication-related performance of calcium signaling among a group of mobile cells. A major finding from computer simulations is that there exists an optimal moving velocity of cells to maximize the range of signal propagation in a group of mobile cells.

In this paper, a video collaborative distribution mechanism is studied in Cloud Radio Access Networks (C-RANs) with object to guarantee the quality of experience (QoE) for different users. Specifically, a framework which enables the remote radio head (RRH)-to-device (R2D) technology to cooperate with the device-to-device (D2D) technology is constructed to transmit video traffic efficiently. Besides, a new QoE evaluation model is built in terms of the transition characteristics of video quality version and the interruption characteristics of video transmissions. Then, the optimal choice of video quality version is studied to achieve a good tradeoff among the video quality, interruption and smoothness for a target user. Moreover, a resource allocation policy is proposed to reduce the mean latency caused by video interruption of the whole network. Simulation results verify that the proposed mechanism performs better than other existing ones when the latency jointly caused by the quality version transition and the transmission interruption is sensitive to the users.

Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IOMT) are resource constrained in power and computational capabilities. The IoMT devices are continuously pushing their readings to the remote cloud servers for real-time data analytics, that causes faster drainage of the device battery. Moreover, other demerits of continuous centralizing of data include exposed privacy and high latency. This paper presents a novel Federated Filtering Framework for IoMT devices which is based on the prediction of data at the central fog server using shared models provided by the local IoMT devices. The fog server performs model averaging to predict the aggregated data matrix and also computes filter parameters for local IoMT devices. Two significant theoretical contributions of this paper are the global tolerable perturbation error (ToiF) and the local filtering parameter (δ); where the former controls the decision-making accuracy due to eigenvalue perturbation and the later balances the tradeoff between the communication overhead and perturbation error of the aggregated data matrix (predicted matrix) at the fog server. Experimental evaluation based on real healthcare data demonstrates that the proposed scheme saves upto 95% of the communication cost while maintaining reasonable data privacy and low latency.

In many applications of Internet of Things (IoT), the huge amount of data are generated by sensor nodes and processing them are complex. Offloading data classification and anomaly event detection tasks to sink nodes in sensor networks can reduce the computing complexity, lower remote communication loads, and improve the response time for the delay-sensitive IoT applications. Many existing classification and anomaly detection methods cannot be directly applied to these IoT applications, because the computing and energy resources of sensors are limited. In this paper, a new feature-based learning system for IoT applications is proposed to effectively classify data and detect anomaly event. Especially, based on the theory of distributed compression, the sparsity and relativity of the data are exploited to obtain the classification features, which can reduce the computation overhead and energy consumption. Further, an RBF-BP hybrid neural network is employed to detect the anomaly event based on the classification results, by which the training time of neural network can be significantly reduced and the accuracy can be improved for users' decisions.

The fifth generation (5G) mobile communication system is regard as a key enabler in promoting the deployment of Internet of Things (IoT), which is accompanied by the increasing service demands such like high data rate, enormous connection, and low latency. To meet these demands, network slicing has been envisioned as an efficient technology to customize infrastructures and allocate resources for 5G IoT services. However, due to various application backgrounds and ubiquitous social interactions of IoT services, the heterogeneous and social-driven resource requirement of users should be carefully assessed in resource allocation for the sliced 5G wireless network. In this paper, a novel nature-inspired wireless resource allocation scheme with slice characteristic perception is proposed, which comprehensively analyzes the properties of slices and converts them into a network profit model of resource utilization. Specifically, personalized service preferences and evolutionary interest relationships of users are exploited to model the complex and dynamic network environment with cellular automaton, and a biologically inspired allocation strategy of virtual wireless resource is proposed on the requirements of continuously updated user groups. Simulation results show that the proposed scheme achieves favorable resource utilization and low computational complexity, which favors the dynamic IoT slicing architecture and improves the efficiency and flexibility of resource allocation.

The ever-growing demand for electrical energy of sensing devices in the Internet of Things (IoT) has led to generating large amounts of electricity consumption data. Electricity service providers often use wireless sensor networks to collect sensing devices' electricity consumption data for statistical analysis, so as to provide sensing devices with improved electrical services. As an important data mining technique, while data clustering excels in dealing with such massive data, it imposes the risk of privacy disclosure in the process of data clustering. In an effort of solving this problem, Blum et al. proposed a differential privacy k-means algorithm, effectively preventing privacy disclosure. However, the availability of data clustering results is reduced due to the data distortion in Blum's algorithm. In this paper, we propose a privacy and availability data clustering (PADC) scheme based on k -means algorithm and differential privacy, which enhances the selection of the initial center points and the distance calculation method from other points to center point. Moreover, PADC attempts to reduce the outlier effect through detecting outliers during the clustering process. Security analysis indicates that our scheme satisfies the goal of differential privacy and prevents privacy information disclosure. Meanwhile, performance evaluation shows that our scheme, at the same privacy level, improves the availability of clustering results compared to the existing differential privacy k-means algorithms, suggesting that our proposed PADC scheme outperforms others for intelligent electrical service in IoT.

Equipped with the emerging cloud computing, clients prefer to outsource the increasing number of Industrial Internet of things (IIoT) data to cloud to reduce the high storage and computation burden. However, existing searchable encryption (SE) schemes just apply to IIoT records containing textual keyword fields rather than both digital and textual keyword ones. Besides, the key management issue still impedes the practicality and availability of SE schemes due to high key storage overhead. To this end, we present an outsourced Hybrid Keyword-Field Search over encrypted data with efficient Keys Management (HKFS-KM) scheme by utilizing the relevance score function and keyed hash tree. Formal security analysis proves that the HKFS-KM scheme can achieve keyword privacy and trapdoor unlinkability in both known ciphertexts attack model and known background attack model. Experimental results using real-world dataset show its efficiency and practicality in practice.

The ever-increasing demand for videos on mobile devices poses a significant challenge to existing cellular network infrastructures. To cope with the challenge, we propose a user-centric video transmission mechanism based on device-to-device communications that allows mobile users to cache and share videos between each other, in a cooperative manner. The proposed solution jointly considers users' similarity in accessing videos, users' sharing willingness, users' location distribution, and users' quality of experience (QoE) requirements, in order to achieve a QoE-guaranteed video streaming service in a cellular network. Specifically, a service set consisting of several service providers and mobile users, is dynamically configured to provide timely service according to the probability of successful service. Numerical results show that when the number of providers and demanded videos is 40 and 2, respectively, the improved users experience rate in the proposed solution is approximately 85%, and the data offload rate on base station(s) is about 78%.

The continuous advances in the storage and transmission capabilities of smart devices have made them possible to share multimedia services with each other through device-to-device (D2D) communications. However, when D2D users transmit multimedia services in manner of cooperative communications, the relay users with non-cooperative behavior may lead to a sharp decline in the quality of services for the receivers. In this paper, a socially aware trust framework for multimedia delivery is proposed to choose the trustworthy D2D cooperative users from D2D relay users. By considering the different D2D cooperative scenarios, the trust relationships of entities in the network are divided into two cases: capability trust mainly from base station (BS) to relay users and social trust from sender to relay users. In particular, capability trust is quantified based on the service capability of a user, such as caching capability, processing capability, and transmission capability, and social trust is obtained based on the historical interactive behaviors among users, such as cooperative behavior, altruistic behavior, and reciprocal behavior. By analyzing such two types of trust, the trustworthy D2D cooperative users are selected from relay users to support different cooperative scenarios. The numerical results verify that the proposed trust framework can effectively identify the selfish users and notably enhance the delivery efficiency of multimedia services, such as delay and delivery success ratio.

With the development of medical informatization, more and more patients actively obtain health information from online medical communities. The traditional methods based on statistical analysis are inefficient in dealing with growing mass of medical texts. Based on the Latent Dirichlet Allocation (LDA), we propose the Medical of Sentence LDA (MS-LDA) for short online medical texts with distribution features of medical words in online medical communities. Disease-related hot topics are assumed to be generated by sentences, the Gaussian function is employed to fit word distribution, and the correlation weight is exploited to modify word frequency for the information extension in sentences. Furthermore, Unified Medical Language System (UMLS) is introduced to cluster the topic recognition results from disease-related hot topics. Experiments on three representative disease boards from www.MedHelp.org show that the perplexity value and word relevance in topics are significantly improved by MS- LDA. Besides, hot topics concerned by members are automatically mined and texts in online medical community are automatically classified.

Analysis of statistical characteristics in electroencephalogram (EEG) data can accurately predict epileptic seizures. However, traditional on-off line prediction methods ignore the specificity of different epileptic subjects (e.g., age and seizures region in brain) and the diversity of epileptic seizure modalities (e.g., seizures during stress and awake state). which leads to low prediction accuracy and poor flexibility. In this paper, sparse group penalty algorithm and incremental learning mechanism are proposed to improve seizure risk prediction. In particular, sparse group penalty algorithm is proposed based on data correlation to incorporate the dependence structure among the features into solving approaches. Then, a relative spectral feature extraction approach is applied to construct a pattern library incrementally. Further, the prediction model parameters are dynamically updated and adjusted based on the updated subject-specific pattern library and incremental learning mechanism. The experimental results show that the proposed epileptic seizure prediction mechanism can sparse the parameters of the model and reduce the retraining time of the parameters. At the same time, it has high prediction accuracy and robustness.

Malicious network nodes often incur problems to network and data privacy by distributing forged public keys. To address this issue, this paper proposes a dynamic trust relationships aware data privacy protection (DTRPP) mechanism for mobile crowd-sensing. In this mechanism, combining key distribution with trust management, the trust value of a public key is evaluated according to both the number of supporter and the trust degree of the public key. The trust value is estimated from the accuracy of the public key provided by the encountering nodes. DTRPP achieves the dynamic management of nodes and estimates the trust degree of the public key. In addition, by classifying traffic data into different types and selecting a proper relay node to forward the data according to data types, it is more effective to use the network resource with the trust degree and centrality of the relays. With extensive evaluations, results show that the proposed mechanism protects the data privacy effectively and has better performance on the average delay, the delivery rate and the loading rate when compared to traditional mechanisms.

To relieve the current overload of cellular networks caused by the continuously growing multimedia service, mobile edge collaboration, which exploits edge users to distribute videos for base station (BS), provides an effective way to share the heavy BS load. With the emergence of mobile edge technologies for Internet-of-Things applications, such as device to device and machine to machine, how to exploit users' social characteristics and mobility to minimize the number of transmissions of BS and how to improve the quality of experience of users have become the key challenges. In this paper, we study two aspects that are critical to these issues. One is the two-step detection mechanism, namely the establishment of virtual communities and collaborative clusters. Specifically, we take into consideration user preference for content and location. First of all, a virtual community is established, which exploits the coalition game based on the user's preference list to dynamically divide users into multiple communities. Then, to take full advantage of the temporary link established between users, a grid-based clustering method is proposed to manage the video requesting users. On the other hand, we propose a scalable video coding sharing scheme based on user's social attributes. This approach makes video distribution more flexible at the edge of mobile network through collaboration among users, and effectively reduces transmission energy consumption of transmitters. Numerical results show that the proposed mechanism can not only effectively alleviate the BS load, but also dramatically improve the reliability and adaptability of video distribution.

To offload and alleviate the heavy base station (BS) traffic load caused by the rapidly growing video services, device-to-device (D2D) communication, as one of the most indispensable technologies of the future cellular networks, can be potentially exploited by mobile users to distribute videos for a BS. In this paper, an effective pricing-based multicast video distribution system and a grid-based clustering method are proposed to support the distribution. Moreover, with the consideration of users' mobility and social characteristics, we classify them into multicast and core types by studying the user stay probability and familiarity. In particular, core users can cooperate with the BS to distribute videos to the multicast users through intracluster D2D multicast. However, core users cannot selflessly help the BS to distribute videos; instead, they will evaluate their personal benefits before distributing the videos to the multicast users. Further, a Stackelberg game-based pricing mechanism is proposed to inspire the core users to distribute videos. Simulation results demonstrate that the proposed mechanism can not only effectively alleviate the BS traffic load, but also significantly improve the effectiveness and reliability of video transmission.

To preserve the privacy of multimedia big data and achieve the efficient data aggregation in wireless multimedia sensor networks (WMSNs), a distributed compressed sensing--based privacy-preserving data aggregation (DCSPDA) approach is proposed in this article. First, in this approach, the original multimedia sensor data are compressed and measured by distributed compressed sensing (DCS) and the compressed data measurements are uploaded to the sink, by which the inherent characteristics between sensor data can be obtained. Second, the original multimedia data are jointly recovered and the common and innovation sparse components are obtained through solving the optimization problem and linear equations at the sink. Third, through least squares support vector machine (LSSVM) learning of the sparse components, the sparse position configuration can be determined and disseminated for each node to conduct the privacy-preserving data configuration. After receiving the configuration message, original multimedia sensor data are accordingly customized, compressed, and measured by the common measurement matrix, aggregated at the cluster heads, and transmitted to the sink. Finally, the aggregated multimedia sensor data are recovered by the sink according to the data configuration to achieve the privacy-preserving data aggregation and transmission. Our comparative simulation results validate the efficiency and scalability of DCSPDA and demonstrate that the proposed approach can effectively reduce the communication overheads and provide reliable privacy-preserving with low computational complexity for WMSNs.

Intermittently connected wireless networks (ICWNs) have been studied in recent years to solve the disruption problem in mobile ad hoc networks and improve the utilization of temporary links raised by node movements. In ICWNs, the packet storing-carrying-forwarding principle is adopted through the cooperation between multiple nodes. Therefore, it is critical to include the connection status of nodes in designing efficient packet forwarding mechanism. In this paper, a node service ability aware packet forwarding mechanism is proposed based on the connection status. First, the connection model is established to analyze the transition of connection status; moreover, the service ability can be evaluated according to the connection strength and connection availability. Second, packet forwarding levels are determined based on their transmitting status to exploit the limited buffer resources. Consequently, the efficient packet forwarding mechanism can guarantee the flexibility of packet transmission in both complex and dynamic network scenarios. Numerical results show that about 20% delivery ratio increase can be achieved by the proposed mechanism, while the overheads and latency are reduced.

A novel channel estimation method for asymmetrically clipped optical orthogonal frequency division multiplexing-based optical wireless communications systems is proposed. Different from the superimposed sequence used in traditional methods, the local matrix and superimposed periodic training are designed rationally. Furthermore, the channel impulse response coefficient of indoor optical wireless diffuse channel can be estimated exactly. The proposed method is not only accurate and simple, but can also allocate time and power flexibly, and improves the bandwidth efficiency.

This letter investigates the extrapolation problem of discrete (a,b,c,d)-bandlimited signals. Based on operator notations, we first show that the extrapolation problem amounts to finding an admissible solution of an under-determined system. Then the minimum norm least squares (MNLS) solution is shown to be an admissible extrapolation estimate. Besides, singular value decomposition method is used to set up an extrapolation formula which guarantees that the extrapolation result achieves the MNLS estimate. An iterative extrapolation algorithm is also proposed to achieve the MNLS estimate. Finally, numerical results are given to demonstrate the efficiency of the proposed extrapolation algorithms.

This letter investigates the extrapolation problem of (a, b, c, d)-bandlimited signals. First, an iterative (a, b, c, d)-bandlimited signal extrapolation algorithm is proposed. Then by use of the interesting properties of generalized prolate spheroidal wave functions, the convergence of the proposed algorithm is proved. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed extrapolation algorithm.

The IEEE 802.11e standard is specified to support quality-of-service in wireless local area networks, and different contention parameters are designated to each type of service. This developed model is presented to analyse the scheduling and the contention between packets with different priorities, where the new features of the enhanced distributed channel access such as virtual collision, backoff, minimum contention window and different arbitration inter-frame spaces are taken into account. Based on the model, the delay performance of differentiated service traffic is analysed and a recursive method is proposed, which is capable of calculating the mean access delay. Simulations show that the model and the analysis provide an insight into the protocol and the effects of different parameters on the performance.