The interaction between negative emotion contagion and the spread of extreme opinions on social media can lead to serious consequences, highlighting the need to understand the co-evolution of opinion and emotion. However, most existing studies treat opinion and emotion dynamics as independent processes. The few models that explore their interplay often do not fully capture their distinct dynamic mechanisms and tend to overlook the mutual reinforcement between opinion convergence and emotion contagion. To address these limitations, we propose the dynamics of opinion and emotion on social media (DOES) model, which employs two differential equations to represent the separate dynamics of opinion and emotion, and introduces coupling functions that describe their mutual reinforcement. We analyze the model’s steady-state conditions and validate the results through simulations. Prediction experiments on real-world datasets demonstrate the model’s ability to reproduce key patterns. DOES provides a novel framework for modeling the coevolution of opinion and emotion, offering insights into emotional regulation and discourse moderation on social media platforms.

With the prevalence of emerging artificial intelligence services in next-generation wireless edge networks, Split Federated Learning (SFL), which divides a learning model into server-side and client-side models, has emerged as an appealing technology to deal with the heavy computational burden for network edge clients. However, existing SFL frameworks would frequently upload smashed data and download gradients between the server and each client, leading to severe communication overheads. To address this issue, this work proposes a novel communication-and-computation efficient SFL framework, which allows dynamic model splitting (server- and client-side model cutting point selection) and broadcasting of aggregated smashed data gradients. We theoretically analyze the impact of the cutting point selection on the convergence rate, revealing that model splitting with a smaller client-side model size leads to a better convergence performance and vise versa. Based on the above insights, we formulate an optimization problem to minimize the model convergence rate and latency under the consideration of data privacy via a joint Cutting point selection, Communication and Computation resource allocation (CCC) strategy. To deal with the proposed mixed integer nonlinear programming optimization problem, we develop an algorithm by integrating the Double Deep Q-learning Network (DDQN) with convex optimization methods. Extensive experiments validate our theoretical analyses across various datasets, and the numerical results demonstrate the effectiveness and superiority of the proposed communication-efficient SFL compared with existing schemes, including parallel split learning and traditional SFL mechanisms.

Predicting roll call votes through modeling political actors has emerged as a focus in quantitative political science and computer science. Widely used embedding-based methods generate vectors for legislators from diverse data sets to predict legislative behaviors. However, these methods often contend with challenges such as the need for manually predefined features, reliance on extensive training data, and a lack of interpretability. Achieving more interpretable predictions under flexible conditions remains an unresolved issue. This paper introduces the Political Actor Agent (PAA), a novel agent-based framework that utilizes Large Language Models to overcome these limitations. By employing role-playing architectures and simulating legislative system, PAA provides a scalable and interpretable paradigm for predicting roll-call votes. Our approach not only enhances the accuracy of predictions but also offers multi-view, human-understandable decision reasoning, providing new insights into political actor behaviors. We conducted comprehensive experiments using voting records from the 117-118th U.S. House of Representatives, validating the superior performance and interpretability of PAA. This study not only demonstrates PAA's effectiveness but also its potential in political science research.

Dynamic graphs evolve continuously, presenting challenges for traditional graph learning due to their changing structures and temporal dependencies. Recent advancements have shown potential in addressing these challenges by developing suitable meta-learning-based dynamic graph neural network models. However, most meta-learning approaches for dynamic graphs rely on fixed weight update parameters, neglecting the essential intrinsic complex high-order topological information of dynamically evolving graphs. We have designed Dowker Zigzag Persistence (DZP), an efficient and stable dynamic graph persistent homology representation method based on Dowker complex and zigzag persistence, to capture the high-order features of dynamic graphs. Armed with the DZP ideas, we propose TMetaNet, a new meta-learning parameter update model based on dynamic topological features. By utilizing the distances between high-order topological features, TMetaNet enables more effective adaptation across snapshots. Experiments on real-world datasets demonstrate TMetaNet’s state-of-the-art performance and resilience to graph noise, illustrating its high potential for meta-learning and dynamic graph analysis. Our code is available at https://github.com/Lihaogx/TMetaNet.

Emerging as a promising technology to bridge the trust gap among multiple participants, blockchain has been envisioned to enable dynamic spectrum sharing in a decentralized manner. However, satellites with limited resources may struggle to support the frequent interactions required by blockchain networks. Additionally, due to the large coverage area of satellites, the differentiated spectrum sharing needs in various regions can make traditional blockchain approaches inadequate. In this paper, a two-tier multi-region blockchain-based dynamic spectrum sharing approach (TMB-DSS) is proposed. This approach enables regions to manage spectrum autonomously while jointly maintaining a unified blockchain ledger. Moreover, a theoretical framework using stochastic geometry is derived to evaluate the stability performance of TMB-DSS. Finally, numerical results are presented to validate the proposed approach.

Polarization and fragmentation in social media amplify user biases, making it increasingly important to understand the evolution of opinions. Opinion dynamics provide interpretability for studying opinion evolution, yet incorporating these insights into predictive models remains challenging. This challenge arises due to the inherent complexity of the diversity of opinion fusion rules and the difficulty in capturing equilibrium states while avoiding over-smoothing. This paper constructs a unified opinion dynamics model to integrate different opinion fusion rules and generates corresponding synthetic datasets. To fully leverage the advantages of unified opinion dynamics, we introduces UniGO, a framework for modeling opinion evolution on graphs. Using a coarsen-refine mechanism, UniGO efficiently models opinion dynamics through a graph neural network, mitigating over-smoothing while preserving equilibrium phenomena. UniGO leverages pretraining on synthetic datasets, which enhances its ability to generalize to real-world scenarios, providing a viable paradigm for applications of opinion dynamics. Experimental results on both synthetic and real-world datasets demonstrate UniGO's effectiveness in capturing complex opinion formation processes and predicting future evolution. The pretrained model also shows strong generalization capability, validating the benefits of using synthetic data to boost real-world performance.

Federated edge learning (FEL) is regarded as a promising distributed machine learning paradigm to reduce transmission latency and resources as well as preserve raw data privacy by collaboratively training local deep learning models across multiple edge devices. However, with the development of artificial intelligence (AI) technologies, the size of neural network models grows exponentially with their parameters to meet variable application requirements, which poses significant challenges to the computation, communication, and memory abilities of edge devices. Existing designs typically focus on either communication or computation efficiency without caring each device’s memory ability. To deal with the above issues, we first introduce a novel model sparse update enabled tiny FEL (TinyFEL) architecture, which terminates the backpropagation early in local model training processes. Therefore, the proposed TinyFEL can reduce local memory occupation and lessen the communication-and-computation burden. Furthermore, we propose a parameter splitting mechanism instead of transmitting the full model, only a part of updated layers of parameters is transmitted for aggregation, which significantly reduced the communication overheads. Thereafter, we develop a communication and computation latency minimization problem to accelerate the training of TinyFEL. To this end, we theoretically analyze the convergence performance of TinyFEL, which unveils the mathematical relationship among sparse update ratio assignment, device selection, and learning performance. Then, a joint sparse update ratio assignment, device selection, and resource allocation strategy is introduced based on the alternating direction method of multipliers (ADMMs) and block coordinate descent (BCD) algorithms. Numerical results indicate that our proposed TinyFEL can reduce training memory occupation by over 40% than the traditional FEL at the cost of negligible accuracy loss.

Low-latency and high-precision vehicle localization plays a significant role in enhancing traffic safety and improving traffic management for intelligent transportation. However, in complex road environments, the low latency and high precision requirements could not always be fulfilled due to the high complexity of localization computation. To tackle this issue, we propose a road-aware localization mechanism in heterogeneous networks (HetNet) of the mobile communication system, which enables real-time acquisition of vehicular position information, including the vehicular current road, segment within the road, and coordinates. By employing this multi-scale localization approach, the computational complexity can be greatly reduced while ensuring accurate positioning. Specifically, to reduce positioning search complexity and ensure positioning precision, roads are partitioned into low-dimensional segments with unequal lengths by the proposed singular point (SP) segmentation method. To reduce feature-matching complexity, distinctive salient features (SFs) are extracted sparsely representing roads and segments, which can eliminate redundant features while maximizing the feature information gain. The Cramér-Rao Lower Bound (CRLB) of vehicle positioning errors is derived to verify the positioning accuracy improvement brought from the segment partition and SF extraction. Additionally, through SF matching by integrating the inclusion and adjacency position relationships, a multi-scale vehicle localization (MSVL) algorithm is proposed to identify vehicular road signal patterns and determine the real-time segment and coordinates. Simulation results show that the proposed multi-scale localization mechanism can achieve lower latency and high precision compared to the benchmark schemes.

Edge artificial intelligence (AI) is an emerging solution for pervasive intelligence service in future 6G networks, by learning machine learning (ML) models at network edge. Edge AI typically consists of three processes: sensing, communication, and computation (SC²). Edge devices first collect data samples through the sensing process, then train local models individually through the computation process, and finally update the local models periodically through the communication process to obtain the global model. Federated edge learning (FEEL) is particularly attractive for Edge AI due to its collaborative ML framework and privacy-enhancing feature. However, the research FEEL with SC2 integration remains an open question. On the one hand, there is still a lack of theoretical insight into the learning performance that is jointly influenced by the processes of SC2. On the other hand, the performance evaluation is another challenge for the proposed SC2-FEEL, which further poses the difficulties in design of efficient resource allocation. To address these issues, an SC2 integrated FEEL (SC2-FEEL) is investigated in this article, where the processes of SC2 are jointly considered and the over-the-air computation (AirComp) technique is employed for a communication-efficient model aggregation. First, theoretical analyses are conducted, which reveals both the sample sensing strategy and the AirComp-induced communication error significant affect the learning performance of SC2-FEEL. Then, we further formulate a latency and energy consumption minimization problem with learning performance guaranteed based on the theoretical results, which is mixed integer nonlinear programming (MINLP) and dynamic programming. To deal with this problem, we propose a joint SC2 resource optimization strategy with low complexity based on the block coordinate update and Lyapunov optimization framework. Extensive simulation results are provided to validate our theoretical analysis, and demonstrate the effectiveness of developed algorithm.

The massive-antenna wideband millimeter wave (mmWave)/terahertz (THz) systems inevitably suffer from a severe beam split effect due to the non-negligible signal propagation delays, which dramatically reduces communication efficiency. Nevertheless, if the wideband split effect is properly utilized, it can also bring benefits via sensing split directions for channel training. Hence, this paper proposes a novel wideband beam alignment framework with true-time-delayer (TTD) modules, which can fully exploit the controllable split beams for efficient angle-of-arrivals (AoAs) estimation. Moreover, we develop a hierarchical posterior matching (PM) enabled wideband beam alignment approach, which proactively configures the split beams to accelerate the estimation of AoAs posterior probability distributions. To deal with the computational complexity of the predesigned codebook and the insensitivity of the Gaussian distribution assumption in PM, we further introduce a low-complex and high-flexible wideband beam alignment approach based on a deep unfolding mechanism. Numerical results verify that: 1) The proposed framework can significantly improve the AoAs estimation accuracy at the cost of the same pilot overheads. 2) The proposed low-complexity deep unfolding approach outperforms the conventional PM mechanism even in low signal-to-noise-ratio (SNR) scenarios.

Federated multi-task learning (FMTL) is a promising technology to tackle one of the most severe non-independent and identically distributed (non-IID) data challenge in federated learning (FL), which treats each client as a single task and learns personalized models by exploiting task correlations. However, the transmission of individual task models generally results in a significant amount of communication overhead compared with global model broadcasting. Furthermore, related works mainly focus on FMTLs with default and static relationships among tasks, which obliterates the non-IID data characteristic. To address these issues, we propose a novel Clustered FMTL mechanism via Sparse Sharing (FedSS). Specifically, we introduce an iterative model pruning approach that trains customized client models to deal with the non-IID issue. Thereafter, we divide clients into different tasks according to their model similarities to promote communication efficiency. Based on clustered tasks, we introduce a sparse sharing mechanism that allows clients to share model parameters dynamically among different tasks to further boost the training performance. On the other aspect, the infertile communication resources would degrade the FMTL performance by restricting the personalized model transmissions. Hence, we first theoretically analyze the convergence performance of the proposed FedSS, which quantitatively unveils the relationship between the local model training performance and communication resources. Thereafter, we formulate a communication-and-computation efficient optimization problem via a joint sparsity ratio assignment and bandwidth allocation strategy. Closed-form expressions for the optimal sparsity ratio and bandwidth allocation are derived based on Lyapunov optimization and block coordinate update (BCU) algorithms. Numerical results illustrate that the proposed FedSS outperforms the benchmarks, and achieves an efficient communication and computation performance.

Communication and energy efficiencies are two crucial objectives in the pursuit of edge intelligence in 6G networks, and become increasingly important given the prevalence of large model training. Existing designs typically focus on either communication efficiency or energy efficiency due to the fact that improving one objective generally comes at the expense of the other. Over-the-air federated learning (OTA-FL) has recently emerged as a promising approach to enhance both efficiencies through an integrated communication and computation design. Nevertheless, most previous studies on OTA-FL only consider scenarios where the dataset for the entire FL procedure is collected and available prior to training. In real-world applications, devices continuously collect new data in an online manner. This underscores the significance of sample collection through sensing in a practical FL pipeline. We propose to integrate sensing with communication and computation into a joint design to further boost the communication-and-energy efficiencies of OTA-FL. Specifically, we consider a training latency and energy consumption minimization problem with performance guarantees. To this end, we first derive an average training error (ATE) metric to quantify convergence performance. Then, a joint sensing, communication and computation resource allocation strategy is developed based on a deep reinforcement learning (DRL) algorithm that nests convex optimization with a deep Q-network. Extensive experiments are conducted to validate our theoretical analysis, and demonstrate the effectiveness of the proposed design for communication-and-energy efficient FL.

Integrated sensing and communication (ISAC) within the unlicensed millimeter-wave (mmWave) frequency bands has been emerged as a pivotal technology in the next generation wireless communication era. However, the interference management issue between sensing and communication becomes much severe due to the absence of centralized scheduling function of the widely existed WiGig networks with the IEEE 802.11ay protocol in the unlicensed mmWave bands. In this way, we aim to investigate an efficient ISAC scheme for the promising WiGig networks via embedding radar pulses into the IEEE 802.11ay beamforming training (BFT) period. Particularly, we transmit both radar pulses and communication signals by exploiting the sector-level sweep of the WiGig network. Since there is a trade-off between radar detection/sensing and mmWave communication under diverse resource assignments, we propose a joint BFT and energy allocation strategy to find an achievable balance. Numerical results validate the effectiveness of the proposed scheme.

Persistent homology, a fundamental technique within Topological Data Analysis (TDA), captures structural and shape characteristics of graphs, yet encounters computational difficulties when applied to dynamic directed graphs. This paper introduces the Dynamic Neural Dowker Network (DNDN), a novel framework specifically designed to approximate the results of dynamic Dowker filtration, aiming to capture the high-order topological features of dynamic directed graphs. Our approach creatively uses line graph transformations to produce both source and sink line graphs, highlighting the shared neighbor structures that Dowker complexes focus on. The DNDN incorporates a Source-Sink Line Graph Neural Network (SSLGNN) layer to effectively capture the neighborhood relationships among dynamic edges. Additionally, we introduce an innovative duality edge fusion mechanism, ensuring that the results for both the sink and source line graphs adhere to the duality principle intrinsic to Dowker complexes. Our approach is validated through comprehensive experiments on real-world datasets, demonstrating DNDN's capability not only to effectively approximate dynamic Dowker filtration results but also to perform exceptionally in dynamic graph classification tasks.

The effectiveness of Millimeter Wave full-duplex (FD) Integrated Access and Backhaul (IAB) system relies on high-dimensional channel estimation with high computational complexity. To avoid high-overhead pilot training, we propose a novel low-complexity end-to-end (E2E) hybrid beamforming strategy for FD mmwave IAB systems using implicit channel state information (CSI). Particularly, the IAB node first dynamically senses spatial channels, where an sensing Transformer block is introduced to actively design the sensing vector. The active sensing strategy can effectively handle the sequential pilot observations with an arbitrary input length. Capitalizing on the implicit channel features extracted by the Transformer, a hybrid beamforming neural network (HBFnet) is further exploited to design the hybrid precoder/combiner of IAB node, thus efficiently mitigating SI while compensating channel fading. Simulation results demonstrate that the proposed scheme outperforms the benchmarks, especially with low pilot overheads.

Low Earth Orbit (LEO) satellite networks provide global connectivity but are vulnerable to security threats such as link flooding attacks. To defend against such attacks, stateof-the-art approaches employ SDN to acquire a global view of the network, enabling the detection and mitigation of malicious traffic. However, in LEO constellation networks, the distributed nature of satellites across a large spatial scale introduces significant latency in both satellite-to-ground and inter-satellite links, with latency reaching up to tens of milliseconds, while attack traffic dynamically adapts within sub-milliseconds. As a result, existing defense systems face challenges in countering these attacks effectively due to the increased reaction time caused by link latency. In this paper, we leverage programmable switches to build a real-time defense system against link flooding attacks (LFA) in LEO constellation networks. To achieve this, we analyze the practical constraints encountered in the deployment of LFA attacks against state-of-the-art LEO satellite systems. We observe that despite the ability of bots to initiate attack traffic from any location worldwide, an anomalous distribution of flow rate on the affected links can still be detected. We propose SatShield, an in-network defense system that filters out suspicious traffic (heavy flows) in the network and mitigates these threats by leveraging programmable packet scheduling. By using SatShield, we are able to achieve real-time identification and rate-limiting of attacks at line rate on a per-packet basis. We implement SatShield with P4 in a commercial programmable switch and evaluate it with real-world traffic traces. Our evaluation shows that SatShield autonomously identifies LFA attack flows and rapidly mitigates LFA attacks.

Metaverse has emerged as a revolutionary technique for transforming the way people interact with digital content, which relies on a distributed computing and communication infrastructure, encompassing terminal users, edge servers, and cloud servers. However, the rapid evolution of the Metaverse presents challenges that surpass the capabilities of existing communication and network infrastructures, particularly on network bandwidth and latency. Additionally, human experience becomes a critical factor in this domain. Therefore, we introduce a human-aware hierarchical software defined network (SDN) architecture consisting of a Metaverse cloud layer, a mobile edge computing (MEC) server empowered edge layer, and a distributed terminal layer. Each MEC server dynamically controls a multi-antenna base station (BS) and several reconfigurable intelligent surfaces (RISs) according to the terminal immersive experience requirements in real-time. To overcome the bandwidth limitation, we propose a novel smart reconfigurable spatial reuse new radio in unlicensed spectrum (NR-U) framework, which can realize customizable communications through flexibly and coordinately reconfiguring beams among the coordination between BSs and RISs. The objective function is formulated as a Lyapunov optimization based decentralized partially-observable Markov decision process (Dec-POMDP) problem to maximize the spectral efficiency while guaranteeing the latency and reliability requirements in Metaverse, via a joint user selection, phase-shift control, and beam coordination strategy. To solve the above non-convex, strongly coupled, and mixed integer nonlinear programming (MINLP), we propose a novel multi-agent hierarchical deep reinforcement learning (MAHDRL) algorithm that integrates deep Q-network (DQN) to solve discrete problems, deep deterministic policy gradient (DDPG) to solve continuous problems, and mixing network to capture complex interactions between multiple agents. Numerical results demonstrate the effectiveness of the proposed algorithm and verify the performance improvements compared to traditional multi-agent deep reinforcement learning (MADRL) algorithms.

Recently, adversarial attacks have shown to lead the state-of-the-art deep neural networks (DNNs) to misclassification. However, most adversarial attacks are generated according to whether they are perceptual to human visual system, measured by geometric metrics such as the$\ell _2$-norm, which ignores the common watermarks in cyber-physical systems. In this article, we propose a fast adversarial watermark attack (FAWA) method based on fast differential evolution technique, which optimally superimposes a watermark on an image to fool DNNs. We also attempt to explain the reason why the attack is successful and propose two hypotheses on the vulnerability of DNN classifiers and the influence of the watermark attack on higher-layer features extraction respectively. In addition, we propose two countermeasure methods against FAWA based on random rotation and median filtering respectively. Experimental results show that our method achieves 41.3 percent success rate in fooling VGG-16 and have good transferability. Our approach is also shown to be effective in deceiving deep learning as a service (DLaaS) systems as well as the physical world. The proposed FAWA, hypotheses, and the countermeasure methods, provide a timely help for DNN designers to gain some knowledge of model vulnerability while designing DNN classifiers and related DLaaS applications.

The unprecedented success of deep learning could not be achieved without the synergy of big data, computing power, and human knowledge, among which none is free. This calls for the copyright protection of deep neural networks (DNNs), which has been tackled via DNN watermarking. Due to the special structure of DNNs, backdoor watermarks have been one of the popular solutions. In this article, we first present a big picture of DNN watermarking scenarios with rigorous definitions unifying the black- and white-box concepts across watermark embedding, attack, and verification phases. Then, from the perspective of data diversity, especially adversarial and open set examples overlooked in the existing works, we rigorously reveal the vulnerability of backdoor watermarks against black-box ambiguity attacks. To solve this problem, we propose an unambiguous backdoor watermarking scheme via the design of deterministically dependent trigger samples and labels, showing that the cost of ambiguity attacks will increase from the existing linear complexity to exponential complexity. Furthermore, noting that the existing definition of backdoor fidelity is solely concerned with classification accuracy, we propose to more rigorously evaluate fidelity via examining training data feature distributions and decision boundaries before and after backdoor embedding. Incorporating the proposed prototype guided regularizer (PGR) and fine-tune all layers (FTAL) strategy, we show that backdoor fidelity can be substantially improved. Experimental results using two versions of the basic ResNet18, advanced wide residual network (WRN28_10) and EfficientNet-B0, on MNIST, CIFAR-10, CIFAR-100, and FOOD-101 classification tasks, respectively, illustrate the advantages of the proposed method.

Random Linear Network Coding holds great potential for enhancing the performance of mega-constellation networks. However, its implementation brings several challenges such as complexity in matrix operations, bandwidth limitations, and increased latency. Although some CPU- and GPU-based solutions have achieved sufficient coding throughput, the gateway stations of satellites require processing rates greater than 10 Gbps while maintaining sub-millisecond delays, which is a challenge with current solutions. In this study, we present and assess efficient RLNC encoding strategies for programmable hardware pipelines, such as the widely adopted Tofino chip with multiple programmable packet parsers and match-action stages. Our approach differs from existing RLNC implementations by offloading the demanding matrix operations from the CPU to the programmable network switch hardware pipeline. Additionally, we optimize logical table ID and other resource utilization by scheduling matrix multiplications across multiple stages. We performed a preliminary evaluation of our design on the Tofino switch and observed a significant improvement in RLNC encoding latency, with a reduction to sub-millisecond levels. Moreover, our design outperforms state-of-the-art solution in terms of throughput when the generation size is greater than 100.

This paper focuses on the mobility prediction problem of specific occupational groups that rely on mobile devices, predicting the mobilities of these groups using data shared across different platforms. While the mobility patterns of general populations have been studied extensively, predicting the movements of specific occupational groups like ride-hailing drivers, who frequently move over long distances, requires a more nuanced approach. This paper introduces FedOGM, a federated learning framework designed to predict the mobility of specific occupational groups while preserving user privacy. The framework utilizes a dynamic bidirectional graph attention network model DyBGAT for predicting the edge weights of an occupational dynamic Origin-Destination graph, representing the mobility behavior. In order to address key challenges in federated learning, FedOGM integrates By leveraging multi-source data from users’ mobile devices and extracting unique occupational features and occupational features into the framework, such as applications usage duration feature, location switching feature, and call duration feature. Assisted by these personalized occupational features, we have devised a client selection algorithm, generated occupational dynamic OD graphs to diminish communication overhead, and proposed a group-level FedAvg aggregation method. Experiments conducted on real-world datasets of ride-hailing drivers, ride-hailing truck drivers, and real estate salespersons, including scenarios with false data, confirm the efficacy of the proposed methods in both mobility prediction and privacy protection.

Federated multi-task learning (FMTL) is a promising technology to deal with the severe data heterogeneity issue in federated learning (FL), where each client learns individual models locally and the server extracts similar model parameters from the tasks to keep personalization for models of clients. Hence, it is essential to precisely extract the model parameters shared among tasks. On the other aspect, the limitation of communication resources would also restrict the model transmission, and thus influence the FMTL performance. To address the above issues, we propose a novel FMTL with Sparse Sharing (FedSS) mechanism that allows clients to share model parameters dynamically according to diversified model structures under limited communication resources. Particularly, we present an adaptive quantization approach for task relevance, which serves as a metric to evaluate the extent of model sharing across tasks. The objective function is formulated to minimize the model transmission latency while ensure the FMTL learning performance via a joint bandwidth allocation and client selection strategy. Closed-form expressions for the optimal client selection and bandwidth allocation are derived based on a alternating direction method of multipliers (ADMM) algorithm. Numerical results show that the proposed FedSS outperforms the benchmarks, and achieves efficient communication performance.

With the rapid growth of Internet of Things (IoT), smart home develops rapidly in these years, which could assist people who need family medical support. It could integrate health care with ambient assisted living (AAL) technologies and provide activities of daily life (ADLs) to the people who need care. This paper proposes a smart home and cross-cloud-and-edge computing based nursing system (NS). In general, a good NS requires low latency, high stability, and the real-time analysis and response, where the conventional centralized cloud computing based approaches cannot meet those requirements very well. To this end, we introduce a novel distributed joint edge-cloud structure to better satisfy these requirements. Moreover, to deal with the security and privacy issues, we introduce the blockchain to verify the identity of data exchanging and differential-privacy (DP) in the NS to protect the healthcare takers’ data privacy. In a word, we propose a privacy-preserving context-aware multi-armed bandit based online learning approach for edge-cloud-enabled NS via blockchain in IoTs. Additionally, our system with a novel top-down expanding tree based structure can support dynamically increasing health care datasets. Extensive experimental and numerical results demonstrate our solution can achieve accurate recommendation results with sublinear regret performance.

Deep Neural Network (DNN) models have offered powerful solutions for a wide range of tasks, but the cost to develop such models is nontrivial, which calls for effective model protection. Although black-box distribution can mitigate some threats, model functionality can still be stolen via black-box surrogate attacks. Recent studies have shown that surrogate attacks can be launched in several ways, while the existing defense methods commonly assume attackers with insufficient in-distribution (ID) data and restricted attacking strategies. In this paper, we relax these constraints and assume a practical threat model in which the adversary not only has sufficient ID data and query times but also can adjust the surrogate training data labeled by the victim model. Then, we propose a two-step categorical inference poisoning (CIP) framework, featuring both poisoning for performance degradation (PPD) and poisoning for backdooring (PBD). In the first poisoning step, incoming queries are classified into ID and (out-of-distribution) OOD ones using an energy score (ES) based OOD detector, and the latter are further classified into high ES and low ES ones, which are subsequently passed to a strong and a weak PPD process, respectively. In the second poisoning step, difficult ID queries are detected by a proposed reliability score (RS) measurement and are passed to PBD. In doing so, the first step OOD poisoning leads to substantial performance degradation in surrogate models, the second step ID poisoning further embeds backdoors in them, while both can preserve model fidelity. Extensive experiments confirm that CIP can not only achieve promising performance against state-of-the-art black-box surrogate attacks like KnockoffNets and data-free model extraction (DFME) but also work well against stronger attacks with sufficient ID and deceptive data, better than the existing dynamic adversarial watermarking (DAWN) and deceptive perturbation defense methods. PyTorch code is available athttps://github.com/Hatins/CIP_master.git.

With the development of intelligent transportation systems, timely and accurate travel forecasting task has witnessed growing interest. Unlike most previous research that only considers the demand prediction in origin regions, this task aims to predict the origin-destination demand between all-region pairs. Its main challenges come from effectively capturing the direction, weight, and temporal information of links in dynamic traffic networks. To confront these challenges, we treat the dynamic traffic networks as multiple weighted directed network snapshots and propose a graph-based deep learning framework, Temporal Graph Autoencoder (TGAE). Specifically, TGAE encodes the fundamentally asymmetric nature of a directed graph via directed neighborhood aggregation and learns a pair of vector representations for each node. Meanwhile, we use the graph attention mechanism to capture the weight information of links. Next, TGAE preserves the temporal dependencies by independently reconstructing the existence and weight of links over two consecutive time steps. Furthermore, we employ the Long Short-Term Memory network (LSTM) to capture the evolution patterns of traffic networks and predict both the direction and weight of links based on the historical data. Experimental results demonstrate that TGAE outperforms several baseline methods on the travel forecasting task, which can help traffic management, resource preallocation, and services optimization.

The advent of 5G opens up hitherto unimagined opportunities for delivering the much-anticipated Internet of Vehicles (IoV). There are two typical types of services in IoV, i.e., safety service and non-safety service. However, the existing IoV framework cannot efficiently support the differentiated IoV services. In addition, maximizing the number of accessed vehicles/users is another critical issue in IoV, especially in the dense urban area. To address these problems, in this paper, we utilize the emerging network slicing technology to support different services and employ Non-Orthogonal Multiple Access technology (NOMA) to help increase the connectivity. In particular, for the safety service, we take advantage of the finite blocklength capacity to correctly record the delay. Our goal is to maximize the connectivity of users by jointly considering the user association and their beamforming vectors, under the restrictions of limited physical resource. The problem is formulated as a Mixed-Integer Nonlinear Programming problem (MINLP). To tackle the intractable MINLP, we propose a two-stage scheme. Firstly, we exploit efficient approaches to solve the beamforming problem, i.e., successive convex approximation, semidefinite relaxation and second-order cone programming. Secondly, we propose a low-complexity Greedy User Association (GUA) algorithm to solve the user association problem. Finally, comprehensive simulations verify that our proposed GUA algorithm is close to global optimal solution and outperforms the benchmark schemes.

As a key infrastructural technology, Industrial Internet of Things (IIoT) and its related techniques have emerged in the age of Industrial Internet. Among them, an increasing popular and attractive federated edge learning (FEL) mechanism, which performs data analysis and inference at the edge devices distributedly, and aggregates local FEL units at a centralized controller, is introduced to meet the stringent data privacy and low-latency requirements for high-stake IIoT devices. Due to the bandwidth limitation, only parts of the IIoT devices can be selected to transmit their local FEL models to the centralized controller at each learning step. However, the centralized controller prefers to collect all the local FEL models to generate the global FL model since each IIoT device has a differential data set. Existing works mainly focus on selecting an appropriate subset of IIoT devices through advanced scheduling mechanisms without extending the resource bandwidth. However, the new radio in unlicensed spectrum (NR-U) technology in the 5G network opens up new possibilities for FEL since it is a privately owned network with fruitful bandwidth resources. We thus propose a novel communication-efficient FEL mechanism for NR-U-based IIoT networks, which aims to select data importance IIoT devices for local training under relatively sufficient unlicensed resources. The objective function is formulated as a tradeoff between total FEL data importance and the transmission latency via joint learning, device selection, and resource management scheduling, which is a mixed-integer nonlinear programming (MINLP). To deal with this problem, an alternating direction method of multipliers and block coordinate update (ADMM-BCU) algorithm with low computational complexity has been used. Closed-form expressions for both optimal device selection and resource management are derived, which highlighted significant insights. Numerical results demonstrate the algorithmic advantages and structural benefits of the proposed strategies.

The sixth-generation (6G) era is expected to provide even higher levels of massive connectivity/Internet of Things (IoT), tremendous data rate, and low latency than the 5G communication. Therefore, the future 6G wireless networks would confront with much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. Meanwhile, the high densely deployed massive IoT devices lead to fearful intercell and intracell interference issues. To address these challenges, a coordinated multipoint (CoMP) technology can be adopted. In this work, we introduce a CoMP-based NR-U network, particularly for 6G-enabled massive IoT scenarios. Under the proposed network, we introduce a spatial listen-before-talk (LBT) scheme to control mobile network operators (MNOs) to coexist with the incumbent WiFi devices orthogonally, which can significantly improve the spectrum efficiency. To suppress the strong co-channel interference among multiple MNOs, we investigate a joint beamforming coordination and user selection problem, which is NP-hard. To deal with this problem, we first adopt a fractional programming and an integer replacement method to transform the objective problem into a tractable one. Then, we introduce a semidistributed alternating direction method of multipliers and block coordinate update (ADMM-BCU) algorithm to find a suboptimal solution, which only requires limited information exchange via a CoMP server and can help reduce energy consumption. Theoretical analysis and numerical results demonstrate the effectiveness of the proposed algorithm for large-scale 6G IoT scenarios.

Vehicular ad hoc network (VANET) has become an accessible technology for improving road safety and driving experience, the problems of heterogeneity and lack of resources it faces have also attracted widespread attention. With the development of software-defined networking (SDN) and multiaccess edge computing (MEC), a variety of resource allocation strategies in MEC-enabled software-defined networking-based VANET (SDVN) have been proposed to solve these problems. However, we note that few of these work involves the situation where SDVN is under Distributed Denial of Service (DDoS) attacks. Actually, Internet of Things (IoT) devices are extremely easy to be compromised by malicious users, and compromised IoT devices may be used to launch edge DDoS attacks against the MEC servers in MEC-enabled SDVN at any time. In this article, we propose a graph neural network (GNN)-based collaborative deep reinforcement learning (GCDRL) model to generate the resource provisioning and mitigating strategy. The model evaluates the trust value of the vehicles, formulates mitigation of edge DDoS attacks and resource provisioning strategies to ensure that the MEC servers can work normally under edge DDoS attacks. In addition, GNN is adopted in the DRL model to extract the structure feature of the graph composed of MEC servers, and help transfer computing tasks between MEC servers to alleviate the problem of resources imbalance between them. Experimental results show that the method of estimating the vehicular trust value is effective, and our method can make the average throughput of edge nodes more stable and lower down the average delay and the average energy consumption under the edge DDoS attack. Also, a real-world case study is conducted to verify our conclusion.

With the accelerated development of immersive applications and the explosive increment of internet-of-things (IoT) terminals, 6G would introduce terahertz (THz) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) technologies to meet the ultra-high-speed data rate and massive connectivity requirements. Nevertheless, the unreliability of THz transmissions and the extreme heterogeneity of device requirements pose critical challenges for practical applications. To address these challenges, we propose a novel smart reconfigurable THz MIMO-NOMA framework, which can realize customizable and intelligent communications by flexibly and coordinately reconfiguring hybrid beams through the cooperation between access points (APs) and reconfigurable intelligent surfaces (RISs). The optimization problem is formulated as a decentralized partially-observable Markov decision process (Dec-POMDP) to maximize the network energy efficiency, while guaranteeing the diversified users’ performance, via a joint RIS element selection, coordinated discrete phase-shift control, and power allocation strategy. To solve the above non-convex, strongly coupled, and highly complex mixed integer nonlinear programming (MINLP) problem, we propose a novel multi-agent deep reinforcement learning (MADRL) algorithm, namelygraph-embedded value-decomposition actor-critic (GE-VDAC), that embeds the interaction information of agents, and learns a locally optimal solution through a distributed policy. Numerical results demonstrate that the proposed algorithm achieves highly customized communications and outperforms traditional MADRL algorithms.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled multi-user network with nonlinear wireless power transfer (WPT), where multiple user sensors are distributed on the ground. Acting as an energy source, the UAV operates at a fixed height and transfers energy to the multiple sensor nodes (SNs) via wireless signals. For more efficient energy harvesting (EH), an antenna array has been installed on UAV with a structure of three dimensional (3D) uniform linear array (ULA), which enables the UAV to perform analog beamforming for power concentration. Taking into account the UAV energy consumption and considering a practical nonlinear EH model, we characterize the UAV energy efficiency particularly for WPT task and subsequently formulate an efficiency maximization problem, in which the analog beamforming and UAV trajectory planning are jointly determined together with the transmit power control scheme. To deal with the nonconvex joint optimization problem, we first propose a cosine-based approximation for the complicated 3D ULA antenna pattern, in which a convex property is proved. Combining with the proved convexity in nonlinear EH model, through a series of mathematical analysis, we construct a convex subproblem based on any feasible point, solving which guarantees an improvement of the energy efficiency. Afterwards, an iterative algorithm is proposed for iteratively addressing the joint design until a convergence to a suboptimal solution. Via simulations, we verify the convergence and performance advantages of our proposed iterative solution. Among the solution, different beamforming preferences regarding the beam coverage enlargement and power concentration are also observed with respect to different antenna array scales.

Accurate crowd flow prediction has gained increasing importance for the development of social Internet-of-Things (IoT) systems. In this article, we provide an efficient crowd flow prediction for social IoT systems in urban space based on the mobile network big data. In particular, the usage detail records (UDRs) are used in the prediction. The feasibility of using UDRs in the prediction is first analyzed. Then, a graph data model is exploited to record and represent the mobile behavior of users. In particular, we propose to apply the heterogeneous information network (HIN) representing the UDR data and characterize the users’ behavior through the embedding methods of HIN. Moreover, an attention-based spatiotemporal graph convolution network with embedded vectors (EA-STGCN) is proposed for the final prediction. Through experimental evaluation, the advantages of the proposed model are shown in comparison to benchmarks.

With the development of data plane programmable Software-Defined Networking (SDN), Distributed Denial of Service (DDoS) attacks on the data plane increasingly become fatal. Currently, traditional attack detection methods are mainly used to detect whether a DDoS attack occurs and it is difficult to find the path that the attack flow traverses the network, which makes it difficult to accurately mitigate DDoS attacks. In this article, we propose a detection method based on Spatial-Temporal Graph Convolutional Network (ST-GCN) over the data plane programmable SDN, which maps the network into a graph. It senses the state of switches through In-band Network Telemetry (INT) with sampling, inputs the network state into the spatial-temporal graph convolutional network detection model, and finally finds out the switches through which DDoS attack flows pass. Based on this, we propose a defense method combined with an enhanced whitelist and a precise dropping strategy, which can effectively mitigate DDoS attacks and minimize the impact on legitimate network traffic. The evaluation results show that our detection method can accurately detect the path that the DDoS attack flows pass through, and can effectively mitigate the DDoS attack. Compared to classic methods, our method improves the detection accuracy by nearly 10%. At the same time, the southbound interface load and CPU overhead brought by our detection and defense process are much lower than the classic methods.

With the advent of big data and major technological achievements in information and communications technology, abundant human trajectory data are collected by location-aware devices such as mobile phones. This information has spurred the development of several methods for measuring spatial interaction, such as the gravity model and radiation model. However, prior studies have mainly measured the scale of spatial interaction at an aggregated level and have derived static or unidirectional interaction. As an improvement, an individual differentiated multidimensional (IDMD) Hawkes model, which can capture individual mobility differences as well as the dynamic mutual interaction between geographical units, is proposed herein. Internet protocol detail records (IPDRs) of Jinhua, Zhejiang, China, covering 23 days, are used as a case study to infer the spatial structure of urban interaction. Compared to the traditional spatial interaction model, the IDMD Hawkes model demonstrates unique advantages in capturing spatial interactions. The results reveal the level of interaction between regions and identify the closely related districts; this can be verified by the economic exchange and transportation convenience between these regions. Our approach provides a novel perspective for measuring spatial interaction considering individual mobility, which can assist policymakers to better understand the spatial structure of a city and plan an efficient city configuration.

Millimeter wave (mmWave) communication is a promising New Radio in Unlicensed (NR-U) technology to meet with the ever-increasing data rate and connectivity requirements in future wireless networks. However, the development of NR-U networks should consider the coexistence with the incumbent Wireless Gigabit (WiGig) networks. In this paper, we introduce a novel multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) based mmWave NR-U and WiGig coexistence network for uplink transmission. Our aim for the proposed coexistence network is to maximize the spectral efficiency while ensuring the strict NR-U delay requirement and the WiGig transmission performance in real time environments. A joint user grouping, hybrid beam coordination and power control strategy is proposed, which is formulated as a Lyapunov optimization based mixed-integer nonlinear programming (MINLP) with unit-modulus and nonconvex coupling constraints. Hence, we introduce a penalty dual decomposition (PDD) framework, which first transfers the formulated MINLP into a tractable augmented Lagrangian (AL) problem. Thereafter, we integrate both convex-concave procedure (CCCP) and inexact block coordinate update (BCU) methods to approximately decompose the AL problem into multiple nested convex subproblems, which can be iteratively solved under the PDD framework. Numerical results illustrate the performance improvement ability of the proposed strategy, as well as demonstrating the effectiveness to guarantee the NR-U traffic delay and WiGig network performance.

In this paper, we study a full-duplex (FD) relaying network operating with finite blocklength (FBL) codes. Based on Polyanskiy’s FBL model, we characterize the FBL reliability of the relaying network under both decode-and-forward (DF) and amplify-and-forward (AF) relaying schemes. Based on the characterisation, we provide reliability-optimal designs via optimal power allocation for both schemes in a single-carrier scenario. In particular, we prove that under the FD DF relaying scheme the (tightly approximated) overall error probability is convex in the transmit power at the relay. In addition, we show that minimizing the overall error probability of the FD AF relaying is equivalent to maximizing the overall signal to interference plus noise ratio (SINR), which is further proved to be pseudo-concave. Then, the designs for a single-carrier scenario are further extended to a multi-carrier scenario with a joint power and data allocation among carriers. In particular, for either the FD DF or FD AF relaying scheme, a joint optimization problem is reformulated to a single problem maximizing the reliability via finding and achieving the optimal SINRs, while auxiliary variables are introduced in FD AF relaying to facilitate the reformulation. Based on mathematical analysis, we respectively construct convex approximations and subsequently propose iterative algorithms, with which the error probability is reduced iteratively until an eventual convergence to an efficient suboptimal value. Hence, a corresponding suboptimal data and power allocation solution can be constructed for the multi-carrier scenario. Via numerical analysis, we validate our analytical model and the proposed allocation algorithms. The FD DF and FD AF relaying schemes are compared with direct transmission in both single-carrier and multi-carrier scenarios, and the benefits of applying FD relaying schemes and joint optimization among multiple carriers are observed.

In this work, we study a downlink multi-user network, where a single access point (AP) is supposed to accomplish data transmissions to all users under low latency constraints. To more effectively cope with the multiple access demand, we consider a hybrid strategy for the multi-user downlink service in finite blocklength (FBL) regime, which combines broadcasting with time-division multiple access (TDMA). In the hybrid strategy, the users are first clustered into different groups. Different groups are served in a TDMA manner with dedicated time slots, while users within each group are served together via a broadcasting signal from the AP. By taking into account the fairness of transmission reliability among all users, we formulate a problem minimizing the maximum error probability among users via jointly determining the user grouping and allocating blocklength among all groups. To address the complicated non-convex problem, we first characterize the optimal blocklength allocation under each given grouping decision, which leads to an optimal closed-form allocation solution via solving an equation system. Based on the characterized features, we are enabled to efficiently distill out the optimal grouping from all possible groupings, which forms the efficient optimal solution for the optimal joint design. Afterwards, aiming at a complexity reduction, we further propose a low-complexity iterative solution, in which the grouping is iteratively improved via the introduced operations until a convergence to a suboptimum. Finally, via simulations, we validate the proposed solutions and reveal the close optimality of the iterative solution. In addition, the hybrid strategy has shown a significant reliability advantage in comparison to pure broadcasting or TDMA, and this performance advantage becomes further enlarged in case of more users.

In this paper, we focus on a relaying network working with a decode-and-forward (DF) principle. A source reports latency-critical information updates to the destination with the help of the relay under periodic request, while this two-hop transmission is operating with finite blocklength (FBL) codes. To evaluate the data freshness at destination, we characterize the average age-of-information (AoI) of the two-hop relaying. Based on the characterization, we consider a problem minimizing the average AoI by jointly optimizing the blocklengths allocated to both hops. To address this non-convex problem, we construct a tight convex approximation for the average AoI at a feasible local point (values of the two blocklengths). Then, we propose an efficient algorithm which iteratively applies the convex approximation, solves the approximated convex problem and updates the local point until a convergence to a suboptimum. Via numerical results, we validate the convergence of the proposed iterative algorithm and confirm the high performance and high efficiency of the proposed solution. The performance advantage of relaying in improving the data freshness is also shown in comparison to direct transmission.

Adversarial examples (AEs) make Internet-of-Things (IoT) systems to face a great challenge. The tiny fluctuations in AEs mislead the learning model, let the model accept, and make wrong decisions. So, accurately identifying AEs is significant for improving the robustness in IoT systems. In this article, a method to identify AEs for time-series data is proposed. We analyze the characteristics of adversarial time-series examples and normal time-series samples and propose a time-series representation method. The tiny fluctuations in AEs are easily discovered by this representation. A metric is also designed to support this representation method to identify AEs with supervised learning and unsupervised learning methods. Our representation method and the metric is applied with supervised learning and unsupervised learning methods to identify time series in real data set and data sets in the official UCR repository. The experimental results show that our method has a great ability to identify AEs, compared to previous approaches.

Protecting enterprise information security is a main task of Internet of Things system. The interaction between employees in same enterprise is based on level structures. So it is important to discover levels of employees for urban developers to protect enterprise information security. In this article, we propose a level discovery method for employees (LDME) from the records of employees using mobile phones named LDME. The call behavior between employees are expressed as several weighted directed complex networks, LDME represent edges in these weighted directed complex networks as vectors to exact both direction and weight information of the edges. Combined with supervised learning method, LDME prune these weighted directed networks into directed acyclic networks, which accurately reflect levels information between employees. At the same time, LDME mines the maximal frequent directed acyclic substructure from the above directed acyclic networks with efficient way, which indicate the stable levels information. We use real data to verify the performance of our method. The experimental result shows that the level of employees mined with our method is accurate and stable.

Social network information is a measure of the number of infections. Understanding the effect of social network information on disease spread can help improve epidemic forecasting and uncover preventive measures. Many driving factors for the transmission mechanism of infectious diseases remain unclear. Some experts believe that redundant information on social media may increase people's panic to evade the restrictions or refuse to report their symptoms, which increases the actual infection rate. We analyze the engagement in the COVID-19 topics on the Internet and find that the infection rate is not only related to the total amount of information. In our research, information entropy is introduced into the quantification of the impact of social network information. We find that the amount of information with different distributions has different effects on disease transmission. Furthermore, we build a new dynamic susceptible-exposed-infected-recovered (SEIR) model with information entropy to simulate the epidemic situation in China. Simulation results show that our modified model is effective in predicting the COVID-19 epidemic peaks and sizes.

Driven by the rapid technological advances, industrial Internet of Things (IIoT) has recently been embraced to enhance autonomous industrial processes. Since a huge diverse traffic would be generated by IIoT, the industrial processes would meet the challenges of spectrum scarcity and on-demand service requirements. Millimeter wave (mmW) and pervasive edge computing (PEC) technologies in 5G communication are available to deal with these requirements. In this article, a novel dual-band framework that integrates both mmW and microwave (μW) networks in PEC environment has been proposed, which locally performs joint resource allocation and power assignment over mmW and μW to meet IIoT devices' specific requirements. To consider the new prominent figure of merit in IIoT scenario, the scheduling problem is formulated as an optimization problem to minimize the IIoT energy consumption in real-time environment. A Lyapunov optimization technique has been applied for the objective function with low complexity and rapid convergence. To solve the NP-hard Lyapunov algorithm, we introduce a block coordinate descent method that decompose the Lyapunov problem into two nested subproblems over the mmW and μW networks. An initialization-free semidistributed scheme is proposed in mmW PECs, which not only requires little information exchange via the μW network but also achieves the global optimal solution. Numerical results are shown to demonstrate the effectiveness of our proposed algorithms and confirm our theoretical analyses.

Ultra-dense HetNets (UDN)-based Millimeter-Wave (mmWave) massive MIMO is considered a promising technology for 5th generation (5G) wireless communications systems since it can offer massively available bandwidth and improve energy efficiency (EE) substantially. However, in UDN, the power consumption of the system increases sharply with the increase of network density. In this paper, we investigate the optimization of the EE in the mmWave massive MIMO systems with UDN. To develop the functions of massive MIMO, we first propose a system model where the massive MIMO harvests electromagnetic energy from the environment employing simultaneous wireless information and power transfer (SWIPT) technology, implemented at the base station (BS). Then, the EE optimization problem is formulated for 5G mmWave massive MIMO systems within the UDN. Considering the nonconcave feature of the objective function, an iterative EE algorithm is developed, based on Dinkelbach method. To utilize the role of coordinated multi-point transmission and reception (CoMP) for improving the EE, a coordinated user(UE)-BS association algorithm-based CoMP with maximum energy efficiency (MaxEE) is proposed. The simulation results demonstrate that the proposed algorithm has a substantially faster convergence rate and is very effective, compared with existing methods.

Based on the current situation of network resources in electric power communication SDH optical transmission networks, a backup network planning method is proposed for the random failures of service to guarantee normal transmission with the least possible backup network resources. The planned backup network can satisfy the needs of service transmission according to the pre-planned path when the main network fails. A method for random service failures protection, which uses service failures probability and network reliability to prevent over-allocation of capacity is introduced. In addition, a robust optimization algorithm is used to implement integer linear programming for the backup design and capacity allocation of communication networks. Finally, the simulation results are presented to verify the feasibility of the method.

The increasing Internet-of-Things (IoT) devices have produced large volumes of data. A deep learning technique is widely used to analyze the potential value of these data due to its unprecedented performance in both the academic and industrial communities. However, the data generated from the IoT devices are distributed among different users. Directly combining these data to a central server will cause privacy leakage, especially for personal sensitive data. Rather than centralized training by getting access to all these raw data, an alternative is to collaboratively learn a model in a distributed manner. However, there exist two main challenges in a distributed learning setting. The first one is how to preserve the privacy of users. The second one is to reduce the communication burden (e.g., mobile users have limited bandwidth) due to high-frequent data exchange. To address these two challenges, we design a communication efficient and privacy-preserving framework to enable different participants to distributively learn a model with a privacy protection guarantee. In particular, we develop a differentially private approximate mechanism for the distributed deep learning. In addition, we design a new gradient sparsification method to, at the first time, reduce both upload and download communication costs. The performance of the proposed framework is tested under different neural network structures for different data sets including, image classification and mobile sensor data. The experimental results demonstrate that we can reduce the communication up to only 2% compared to the full gradients exchange and achieve up to 16% accuracy increase compared to the previous works.

New radio in unlicensed spectrum (NR-U) is an exciting evolution of LTE-U/LAA from 4G LTE to 5G NR, which generates an opportunity to alleviate the spectrum crunch in future wireless networks by operating NR in unlicensed spectrum. Due to the openness of unlicensed spectrum, networks with heterogeneous radio access technologies (RATs) will coexist with NR-U, especially for the incumbent WiFi networks. In this paper, we first introduce a NR-U framework based on network slicing and spatial multiplexing, which can help on the management of networks with heterogeneous RATs. We then propose a synchronous RAT for the proposed NR-U network and derive a user group construction strategy to improve performance as well as provide flexibility for both cellular and WiFi users. Numerical results demonstrate the mutual benefits of the proposed RAT to both cellular and WiFi users.

We propose a novel context-aware task allocation framework for mobile crowdsensing in the scenario of edge computing to enable the crowdsensing platform effectively and real-timely handle large-scale crowdsensing tasks in smart city. The task allocation performs in both cloud computing layer and edge computing layer. It aims to combine the merits of cloud and edge computing, i.e., diminishing communication latency while guaranteeing overall scheduling. The cloud layer evaluates the participants' task-oriented reputation based on the participants' background information, task context, and historical feedbacks (i.e., rewards) and sends the edge layer the most promising subset of participants. Then the edge layer communicates with the participants for the real-time information and makes optimization based on the task requirement (e.g., maximizing the sensing coverage under the constraint of the task budget). In the cloud layer, we propose a privacy-preserving and contextual online learning algorithm to manage the participants' reputation. The algorithm can adapt the decision-making strategy based on previous performances of participants. In the edge layer, plenty of existing centralized task allocation strategies can be directly applied to optimize based on the participants' real-time information. Theoretical analysis shows that our proposal achieves sublinear regret and differential privacy for both requesters and participants. Experiments results validate that our proposed algorithm supports increasing big dataset while striking a balance between the privacy-preserving level and the prediction accuracy.

User portrait analysis is one of the key points in human behavior analysis. It is important to describe or guess user's characteristics through rational methods in business analysis. In this paper, we use the user details records data set from a mobile operator to analyze the preference of users with different brand phones for different APPs and propose the concept of mobile Internet life personas (MILP) and the latent MILP indexing (LMILPI) model for the analysis of users' MILP. At the same time, we build user portrait analysis framework based on the latent semantic indexing (LSI) theme model, LMILPI model, and association rule mining. On the one hand, we analyze users' preference for APP content when using different mobile brands. On the other hand, we analyze the relationship among mobile brands, user access time, and MILPs to describe users' Internet behavior. Our research shows that there is a difference between users who use different brands of mobile phones: 1) users who use different brand phones have different preferences for different APPs. However, if mobile brand marketing methods or target users are same or similar, the APP preference of these brands will be similar; 2) MILPs are different between users who use different brands of mobile phones, but the MILPs displayed on Android platform are similar even though brands are not same, while the MILP displayed on iPhone is quite different from MILPs on Android; and 3) MILPs' importance will be changed by mobile phone brands and time periods. The analytical framework which we propose can provide commercial solutions such as application recommendations, market strategy formulation, Internet access, and other fields.

In large-scale mobile networks, energy minimization problem pertaining to base station (BS) switching is known to require high computational complexity. In this paper, we propose a data-driven energy-saving (DDE) framework which partitions the networks into communities. Each community can separately make decisions with low computational cost. We introduce a novel metric to measure the collaborative relationship among BSs. Then, the mobile networks are partitioned via a multiscale community detection method. To improve the cell zooming performance, we estimate the aggregate traffic demands of the communities based on the historical traffic profile. A heuristic switch-off strategy is proposed to maximize the energy savings while guaranteeing minimal service requirements. Experiments with two real regional cellular network datasets show that the proposed DDE framework can conserve a significant amount of energy and leverage the tradeoff between energy savings and the blockage ratio.

Purchasing behavior analysis plays a crucial role in pricing, recommendation, and market strategy designing. One of the fundamental questions that arises in purchasing behavior analysis is to understand, characterize and estimate purchasing capability. In this article, we investigate the patterns of purchasing capability from the perspective of network usage of smartphone consumers based on a large-scale usage detail records (UDRs). First, the purchasing capability of smartphone consumers are divided into three (high/middle/low) levels according to corresponding device retail price around observation period. Then we pairwise integrating interest and temporality information, and conduct a clustering analysis for each purchasing capability level users. Finally, the profile of each community is extracted by a visualization process. The learned patterns can not only illustrate how users behave on interest, but also describe their network usage preference on temporality, providing fine-granularity to understand the dynamics behind each purchasing capability group. Moreover, inspired by learned patterns, we find network usage distribution on temporality, spatiality and interest can serve as good indicators for estimating users purchasing capability. Our work has broad applicability in fields such as pricing, recommendation, and market strategy designing.

Human mobility patterns have been widely investigated due to their application in a wide variety of fields, for example urban planning and epidemiology. Many studies have introduced spatial networks into human mobility analyses at the collective level. However, these studies merely analyzed spatial network structure, and the underlying collective mobility patterns were not further discussed. In this paper, we propose a collective mobility discovery method based on community differences (CMDCD). We constructed spatial networks where nodes represent geographical entities and edge weights denote collective mobility intensity between geographical entities. The differences between communities detected from the networks constructed in different periods were then identified. Since collective spatial movement has a large influence on network structure, we can discover groups with different mobility patterns based on community differences. By applying the method to data usage detail records collected from the cellular networks in a city of China, we analyzed different collective mobility patterns between the Spring Festival vacation and workdays. The experimental results show that our method can solve these two problems of identifying community differences and discovering users with different mobility patterns simultaneously. Moreover, the CMDCD method is an integrated approach to discover groups whose mobility patterns have changed in different periods at the large spatial scale and the small spatial scale. The discovered collective mobility patterns can be used to guide urban planning, traffic forecasting, urban resource allocation, providing new insights into human mobility patterns and spatial interaction analyses.

With the wide spread of user-friendly mobile devices and applications, the broadband cellular network experiences a pronounced growth of traffic volume, and consumes a large amount of energy. To cope with these two challenges, it is important to understand the resource usage of base stations, which is the first step to manage the radio spectrum resources and network infrastructure efficiently. In this paper, we first use the Pearson's correlation to measure the dependency between base stations on the time variance of traffic load, which is the most direct pattern of resource usage in the cellular data network. By concentrating the analysis on relatively higher linear dependency between base stations, we construct a network in which the vertex and edge represents the base station and significant correlation of traffic dynamic between base stations, respectively. We characterize it in a way of weighted network, and considering the spatial deployment of base stations as well. Our findings are interesting and valuable. Specifically, such a network shows a “long tail” degree distribution, “small world” phenomenon and low modularity. And the spatial deployment of base stations has significant impact to the edge weight, rather than the topology.

A integrated propagation model for VANET in densely built-up urban scenario, based on analyzing the special factors of the measurements, is proposed in the paper. The proposed propagation model can be used as the physical module in OMNet++ to evaluate the packet level performance for VANET in a densely built-up urban scenario. The effectiveness of the proposed method is demonstrated by using experiments under various radio environments in Wuhan, China. The simulation results of the statistical packet level performance are approximately consistent with that in measurements, and the error between measurements and simulation is acceptable.

Vehicular ad hoc network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). Understanding and modeling packet error in the VANET is especially relevant to the design and analysis of higher layer wireless communication protocols. In this paper, the analysis of the packet error characteristics based on measurements in live VANET in different scenarios is shown form which Traces-4 are selected for further analysis. Based on the statistical dependence between burstlengths and gaplengths, we present a packet-level Markov (PLM) model which is capable of describing the measured statistics properly. The parameters of the model are obtained from the packet error statistical characteristics. Finally, it is demonstrated that both the accuracy and the efficiency of the proposed PLM model outperform other popular Markov models.

Selective forwarding is one of the routing methods that if more than one downstream node are available at either the source or an intermediate node, the packet is forwarded along only one downstream link based on local conditions. In the paper, the selective forwarding probability based on the node degree and link loss is presented to increase the reliability of selective forwarding. The performance is analyzed through regular square network topology qualitatively and the simulation results show the effects of the parameters on our methods