This survey examines the current state of the Metaverse, encompassing its fundamental concepts, technological framework, practical applications, and user experience to evaluate its stage of development. This paper reviews the core concepts of the Metaverse and Extended Reality (XR) and evaluates the latest advancements in hardware and software technologies. Furthermore, it examines the Metaverse’s typical applications in four key domains: education, training, medicine, and mixed life, while summarizing user feedback to identify its advantages and challenges. The feedback indicates that the Metaverse offers notable benefits, including immersive experiences, enhanced training effectiveness, cost efficiency, and improved safety. However, significant challenges remain, such as hardware performance limitations, software inefficiencies, user discomfort, health risks, and social and ethical concerns. The analysis suggests that while the Metaverse has yet to reach full maturity, it holds great potential for future development. To further advance the field, this paper highlights key research priorities in artificial intelligence, quantum computing, and social governance, providing insights for future studies.

Insider threats are prevalent security issues for organisations. While attribute-based access control (ABAC) systems manage sensitive data, they are not fully effective against insider threats. We propose integrating moving target defence (MTD) into ABAC systems to mitigate these threats. Our approach enhances the ABAC system with three modules: 1) a correlated attribute generator to estimate correlations among attribute-value pairs; 2) a policy sensitivity estimator to determine sensitivity levels of policy rules; 3) a mutation engine to dynamically mutate sensitive policy rules using correlated attributes. We evaluated our framework using a real-world dataset from an educational system, assessing the efficiency of the attribute generator, efficiency of the sensitivity estimator, overhead from the MTD components, and the framework's overall performance. Our results show that with a dataset of 200,000 records and 13 policy rules, the framework identified five sensitive rules and achieved a 100% mitigation rate without excessive overhead.

Federated learning (FL) is promising for privacy-sensitive fault diagnosis in the Industrial Internet of Things (IIoT). However, real-world deployments must address the coexistence of class-incremental fault evolution and dynamic edge participation, which leads to fragmented class exposure, catastrophic forgetting, and aggregation bias. To address this issue, we propose FedDiDy, a unified framework for federated class-incremental fault diagnosis under dynamic participation. FedDiDy combines a multi-head classifier for task decoupling, a raw-data-free conditional generator for historical knowledge replay, and a perception-aware aggregation mechanism for bias mitigation. Experiments on four datasets under Bernoulli, Cyclic, and Markov participation patterns show that FedDiDy consistently outperforms the compared baselines, with up to 15.1% absolute improvement in average accuracy and 14.44% reduction in forgetting rate.

Collaborative edge computing enables multiple edge servers to cooperate by sharing information and computing resources, achieving resource complementarity and task collaborative processing. In areas with dense mobile users, edge servers are often deployed and managed by different business entities. Therefore, in a heterogeneous edge environment with multiple business entities, the collaborative relationship between edge servers is often constrained by their respective management strategies and profit considerations. Resource sharing is no longer unconditional, but instead involves a certain degree of competition and self-interested behavior. Existing works often assume unconditional collaboration, overlooking the rational and self-interested behavior of edge servers belonging to different business entities in practice. This paper proposes a coalition-based collaborative scheduling framework tailored for Directed Acyclic Graph (DAG) tasks with inter-task dependencies. It designs a coalition formation algorithm for edge servers and a multi-DAG task scheduling algorithm within the coalition to achieve optimal resource collaboration and edge server utility. Extensive experiments are conducted to evaluate the performance of the proposed algorithms. Experimental results show that the proposed algorithms consistently outperform all baselines across different network scales, achieving at least a 46.34% improvement in cooperative utility.

The extremely large reconfigurable intelligent surface (XL-RIS) is an architecture that shows potential in expanding the transmission region to meet high requirements of sixth-generation communication systems. In XL-RIS-assisted scenarios, users transmit signal through spherical-wavefront near-field channel, where the channel estimation presents great challenges. Additionally, XL-RIS elements are prone to failure due to accidental damages or blockages, which further complicates the channel state. To address these issues, we propose a three-stage signal-assisted sparse representation-based channel estimation (SA-SRCE) scheme to robustly recover near-field channel and diagnose RIS failure state with low pilot overhead. The first stage utilizes a double domain filter (DDF) to eliminate noise based on sparse representation in the angle and polar domains. Then, based on the filtered signal, a failure-aware orthogonal matching pursuit (FA-OMP) algorithm is proposed to iteratively reconstruct the channel, as well as eliminate the perturbation invoked by RIS element failures. Moreover, considering the limited availability of pilot, we further exploit the information of compressed signal and propose the signal-assisted failure-aware double-sparsity OMP (FADS-OMP) algorithm as the third stage to improve the estimation performance in FA-OMP. The effectiveness and robustness of our proposed scheme is validated through theoretical analysis and extensive simulations.

Camouflaged object detection (COD) relies on multi-granularity structural information and fine-grained details to distinguish objects from highly similar backgrounds. Whereas low-resolution data lacks high-frequency cues such as textures and sharp edges, retaining only coarse structures. These not only weaken discriminative features but also introduce resolution-induced camouflage beyond natural blending. Existing COD methods assume high-resolution data and fail to address this dual-source ambiguity, resulting in significant performance degradation and underscoring the need for approaches that explicitly explore essential spatial priors under low-resolution constraints. Therefore, we propose KRNet, the first framework explicitly designed for COD in low-resolution settings. KRNet presents a Leader-Follower framework where the Leader extracts dual gold-standard distributions: conditional and hybrid, from supporting data to drive the Follower in rectifying knowledge learned from low-resolution data. The framework further benefits from a cross-consistency strategy, and a stronger time-prompt conditional encoder that improve the rectification of these distributions. Extensive experiments on benchmark datasets demonstrate that KRNet outperforms state-of-the-art COD methods and SR-assisted COD approaches, highlighting its effectiveness in tackling the challenges of low-resolution data in COD. Code: https://github.com/whyandbecause/KRNet/tree/main.

In a collaborative large-small model (CLSM) system, application providers can deploy their small-scale intelligent services at the edge to serve the mobile users. Due to the personalized characteristics of edge small models, an application provider must properly accommodate its users for service and/or data provision. In general, a cost-effective mobile user allocation (MUA) solution must serve the maximum number of users with minimum edge resources by considering mobile users' preferences. However, existing studies of MUA assume that users' and edge servers' available resources are static and neglect the matching between mobile users' preferences and the edge small models' personalization. This is unrealistic in real-world CLSM systems where the mobility of users changes dynamically and the edge servers' available resources differ periodically. Therefore, users must be allocated on the fly to edge servers whose available resources also vary over time. Most existing MUA approaches are impractical in real-world CLSM systems because they are usually designed to address the MUA problem offline, and their performance is usually poor. In this paper, AirMUA, a novel online scheme, is designed, aiming to solve the dynamic MUA (DMUA) problem. AirMUA allocates users to edge servers as they join the system and utilizes system resources dynamically released by user departures. Comprehensive experiments and analyses are conducted to evaluate its performance systematically. Extensive experimental results show the high performance of AirMUA, which ensures its practicality in real-world CLSM systems.

Federated Learning (FL) enables devices to collaboratively train a global model without centralizing raw data. However, heterogeneous data distributions often hinder a single global model from performing optimally across diverse clients. In this paper, we introduce two personalized federated learning methods to address the aforementioned challenge. First, we introduce a gradient-fusion approach that merges global gradients aggregated via a learnable collaboration matrix with local gradients tailored to each client's data. This fusion harmonizes collective intelligence with device-specific knowledge, thereby improving personalization under data heterogeneity. Second, we propose a gradient-decoupling method that mitigates the limitations of relying solely on a collaboration matrix for balancing knowledge sharing and personalization. By decoupling the tasks and regularizing local updates through the incorporation of extracted collective intelligence, we introduce an inductive bias that significantly improves generalization. Extensive evaluations on four computed tomography and pathology imaging datasets covering a wide range of heterogeneity show that our frameworks substantially outperform existing baselines. We also provide theoretical analyses that establish performance bounds and convergence guarantees under mild assumptions.

Convolutional Neural Networks (CNN) have been widely adopted in multi-access edge computing system due to their powerful feature extraction and high inference accuracy. Harnessing the heterogeneous computational capacities of end de vices and edge servers, end-edge collaborative inference addresses resource constraints of end devices and latency challenges of real time inference. However, existing studies typically assume that all potential inference models are pre-employed on the server, which may not always align with real-world deployment scenarios. Moreover, the economic incentives of edge service providers are often overlooked in current distributed inference research. To address these issues, we propose an end-edge collaborative inference framework for CNN in multi-access edge computing system, named DisCNN. We first model the interaction between latency-sensitive end devices (EDs) and resource-constrained edge service providers as a Stackelberg game and prove the existence of a Subgame Perfect Equilibrium. Next, we derive the optimal response characteristics of the EDs and develop an efficient algorithm to allocate computation and communication resources to EDs while partitioning the CNN accordingly. To compute the optimal offloading set, we introduce a linear-time algorithm with a bounded approximation ratio. Then, to jointly optimize caching, resource allocation, and pricing, we design an efficient approximate algorithm to compute a near-optimal solution. Finally, extensive simulation results validate the effectiveness of the proposed framework.

Multi-space crowd sensing has emerged as a promising paradigm for 3D urban perception. However, it faces critical challenges including space coupling, task heterogeneity, and dynamic resource availability. To address these issues, the Multi-Space Fairness Task Allocation (MSFTA) problem is formulated, aiming to maximize task completion while ensuring fairness across spatial dimensions. The problem is proven to be NP-hard, and a dynamic collaborative optimization framework is proposed. Within this framework, a Multi-Space Clustering QuadTree Voronoi Partition (MCQVP) is developed for fine-grained multi-dimensional partitioning by leveraging DBSCAN and quadtree structures. In addition, a Group Urgency-Based Multi-Shortest Path (GUBMSP) scheduler is incorporated to prioritize time-sensitive task groups via urgency-aware critical paths. Furthermore, a Fairness-Aware Pareto Multi-Objective Ant-Q Learning (FA-PMOAQL) allocator is introduced to integrate Q-learning and ant-colony optimization under fairness-aware multi-objective guidance. These designs establish a unified framework that not only improves task allocation efficiency through multi-space partitioning and urgency-driven scheduling, but also ensures equitable resource utilization by embedding fairness into the learning process. Comparison experiments on Tokyo and New York datasets demonstrate that the proposed approach achieves up to 12.8% higher task completion rate compared with baseline algorithms, while maintaining relatively low runtime. In cross-layer scenarios, the completion rate improves by 20% when agent resources increase, and under heavy task loads it sustains competitive performance with only moderate decline.

Edge computing enables distributed machine learning models to be deployed and trained near the user space. However, the intricate nature of edge computing raises several challenges to distributed machine learning frameworks: 1) inferior convergence arising from non-independent and identically distributed (non-IID) edge data; 2) inefficient structural adaptation, where device dynamism complicates the adjustment of aggregation structure; and 3) reduced training efficiency, as resource heterogeneity and fluctuations create systemic stragglers. To address these issues, a distributed hierarchical model training framework has been proposed by considering the dynamic aggregation structure and frequency in this paper. This framework designs an Edge Aggregation Structure and Frequency method, namely EASF, for distributed model training in heterogeneous edge computing environments. First, a dynamic distributed aggregation structure method is formulated to consider various data distribution patterns. This method constructs and modifies the aggregation structure in a distributed manner to adapt to variations in working edge devices. Second, a self-adapted aggregation frequency method and a timeout abandonment mechanism are proposed to allow each node to update its aggregation frequency adaptively. Lastly, a theoretical analysis demonstrates the convergence property of the EASF method in dynamic environments. Extensive experiments have been conducted on a set of open testbeds. Results show that the EASF significantly improves the efficiency and accuracy of hierarchical model training in heterogeneous edge computing.

Collaborative Edge Computing (CEC) enables effective load balancing by decomposing tasks across edge servers. However, due to limited computing and storage resources in CEC networks, eliminating computational redundancies becomes particularly important for improving overall efficiency and conserving resources. To address this, we propose a novel compute-storage cooperation framework that jointly optimizes task offloading and computation result caching to minimize system-wide delay and caching cost. The optimization problem is decomposed into two subproblems: reusable task scheduling and reusable data caching. Accordingly, the CoRe-S algorithm and the VaRe-C algorithm along with a proactive pre-caching mechanism are proposed to solve these subproblems, respectively. By leveraging temporal and spatial correlations among computational tasks, the proposed framework directly caches computation results to reduce redundant processing. In addition, the age of data is incorporated into the evaluation metric to better assess the value of cached results, thereby enhancing reuse efficiency. Theoretical analysis and extensive simulations are conducted to validate the effectiveness and superiority of the proposed algorithms. Compared with state-of-the-art baselines, our method reduces the total cost by up to 41.89% and achieves a cache hit rate of 53.1%.

This paper aims to enhance the performance of Vehicular Platooning (VP) systems integrated with Wireless Federated Learning (WFL). In highly dynamic environments, vehicular platoons experience frequent communication changes and resource constraints, which significantly affect information exchange and learning model synchronization. To address these challenges, we first formulate WFL in VP as a joint optimization problem that simultaneously considers Age of Information (AoI) and Federated Learning Model Drift (FLMD) to ensure timely and accurate control. Through theoretical analysis, we examine the impact of FLMD on convergence performance and develop a two-stage Resource-Aware Control framEwork (RACE). The first stage employs a Lagrangian dual decomposition method for resource configuration, while the second stage implements a multi-agent deep reinforcement learning approach for vehicle selection. The approach integrates Multi-Head Self-Attention and Long Short-Term Memory networks to capture spatiotemporal correlations in communication states. Experimental results demonstrate that, compared to baseline methods, the proposed framework improves AoI optimization by up to 45%, accelerates learning convergence, and adapts more effectively to dynamic VP environments on the AI4MARS dataset.

Facilitated by mobile edge computing, client-edge-cloud hierarchical federated learning (HFL) enables communication-efficient model training in a widespread area but also incurs additional security and privacy challenges from intermediate model aggregations and remains vulnerable to the single point of failure issue. To tackle these challenges, we propose a blockchain-based HFL (BHFL) system that operates a permissioned blockchain among edge servers for model aggregation without the need for a centralized cloud server. The employment of blockchain, however, introduces additional overhead. To enable a compact and efficient workflow, we design a novel lightweight consensus algorithm, named Proof of Federated Edge Learning (PoFEL), to reuse computational work performed for local model training. Specifically, the leader node is selected by evaluating the intermediate FEL models from all edge servers instead of other additional mechanisms used solely for leader elections. This design thus improves the system efficiency compared with traditional BHFL frameworks. To prevent model plagiarism and bribery voting during the consensus process, we propose Hash-based Commitment and Digital Signature (HCDS) and Bayesian Truth Serum-based Voting (BTSV) schemes. Finally, we devise an incentive mechanism to motivate continuous contributions from clients to the learning task. Experimental results demonstrate that our proposed BHFL system with the corresponding consensus protocol and incentive mechanism achieves effectiveness, low computational cost, and fairness.

Recent advances in distributed swarm learning (DSL) offer a promising paradigm for edge Internet of Things. Such advancements enhance data privacy, communication efficiency, energy saving, and model scalability. However, the presence of non-independent and identically distributed (non-i.i.d.) data pose a significant challenge for multi-access edge computing, degrading learning performance and diverging training behavior of vanilla DSL. Further, there still lacks theoretical guidance on how data heterogeneity affects model training accuracy, which requires thorough investigation. To fill the gap, this paper first study the data heterogeneity by measuring the impact of non-i.i.d. datasets under the DSL framework. This then motivates a new multi-worker selection design for DSL, termed M-DSL algorithm, which works effectively with distributed heterogeneous data. A new non-i.i.d. degree metric is introduced and defined in this work to formulate the statistical difference among local datasets, which builds a connection between the measure of data heterogeneity and the evaluation of DSL performance. In this way, our M-DSL guides effective selection of multiple works who make prominent contributions for global model updates. We also provide theoretical analysis on the convergence behavior of our M-DSL, followed by extensive experiments on different heterogeneous datasets and non-i.i.d. data settings. Numerical results verify performance improvement and network intelligence enhancement provided by our M-DSL beyond the benchmarks.

To gain panoramic awareness of spectrum coverage in complex wireless environments, data-driven learning approaches have recently been introduced for radio map estimation (RME). While existing deep learning based methods conduct RME given spectrum measurements gathered from dispersed sensors in the region of interest, they rely on the centralized data collected at a fusion center, which unfortunately raises critical concerns on data privacy leakages and high communication overloads. Federated learning (FL) enhances data security and communication efficiency in RME by allowing multiple clients to collaborate in model training without directly sharing local data. However, the performance of the FL-based RME might be hindered by the problem of task heterogeneity across clients located in different environments. To fill this gap, we propose a physicsinspired distributed RME solution for heterogeneous settings in this paper. The key idea is to develop a novel distributed RME framework empowered by leveraging the domain knowledge of radio propagation models. To do so, we design a new distributed learning approach that splits the entire RME deep model into two modules. A global autoencoder module is shared among clients to capture the common pathloss influence on radio propagation patterns, while a client-specific autoencoder module focuses on learning the individual features produced by local shadowing effects from the unique building distributions in local environment. Simulation results show that our proposed method outperforms the benchmarks in achieving higher performance.

Enriching information of spectrum coverage, radiomap plays an important role in many wireless communication applications, such as resource allocation and network optimization. To enable real-time, distributed spectrum management, particularly in the scenarios with unstable and dynamic environments, the efficient transmission of spectrum coverage information for radiomaps from edge devices to the central server emerges as a critical problem. In this work, we propose an innovative physics-enhanced semantic communication framework tailored for efficient radiomap transmission based on generative learning models. Specifically, instead of bit-wise message passing, we only transmit the key “semantics” in radiomaps characterized by the radio propagation behavior and surrounding environments, where semantic compression schemes are utilized to reduce the communication overhead. Incorporating the novel concepts of Radio Depth Maps, the radiomaps are reconstructed from the delivered semantic information backboned on the conditional generative adversarial networks. Our framework is further extended to facilitate its implementation in the scenarios of multi-user edge computing, by integrating with federated learning for collaborative model training while preserving the data privacy. Experimental results show that our approach achieves high accuracy in radio coverage information recovery at ultra-high bandwidth efficiency, which has great potentials in many wireless-generated data transmission applications.

Federated Learning (FL) enables collaborative model training across multiple edge devices without sharing raw data, yet existing FL frameworks often assume static data domains, limiting their applicability to real-world scenarios where data evolves over time. To address this, Federated Continual Learning (FCL) integrates continual learning into FL, but conventional strategies such as data replay are impractical due to privacy and storage constraints. In this paper, we propose PP-FCL, a privacy-preserving FCL framework that mitigates catastrophic forgetting without storing sensitive client data. PP-FCL employs a server-side generative model to synthesize representative samples of previously learned tasks, enhancing data diversity, preserving characteristic class features, and refining decision boundaries. On the client side, an improved contrastive incremental learning loss and a carefully designed feature distillation method decouple old and new knowledge, ensuring a balanced trade-off between plasticity and stability. As a result, PP-FCL not only enhances the model’s representational capabilities but also adapts effectively to non-stationary data distributions, maintaining robust performance in privacy-sensitive, evolving federated environments. Empirical results on CIFAR-10, CIFAR-100 and TinyImageNet demonstrate that PP-FCL outperforms state-of-the-art baselines by approximately 5–6% in average accuracy. This substantial improvement highlights PP-FCL’s effectiveness in preserving model performance under evolving conditions, ensuring robust and adaptive learning in dynamic federated environments.

As on-device large language model (LLM) systems become increasingly prevalent, federated fine-tuning enables advanced language understanding and generation directly on edge devices; however, it also involves processing sensitive, user-specific data, raising significant privacy concerns within the federated learning framework. To address these challenges, we propose DP-FedLoRA, a privacy-enhanced federated fine-tuning framework that integrates LoRA-based adaptation with differential privacy in a communication-efficient setting. Each client locally clips and perturbs its LoRA matrices using Gaussian noise to satisfy (∊, δ)-differential privacy. We further provide a theoretical analysis demonstrating the unbiased nature of the updates and deriving bounds on the variance introduced by noise, offering practical guidance for privacy-budget calibration. Experimental results across mainstream benchmarks show that DP-FedLoRA delivers competitive performance while offering strong privacy guarantees, paving the way for scalable and privacy-preserving LLM deployment in on-device environments.

This paper examines the theoretical performance of distributed diffusion models in environments where computational resources and data availability vary significantly among workers. Traditional models centered on single-worker scenarios fall short in such distributed settings, particularly when some workers are resource-constrained. This discrepancy in resources and data diversity challenges the assumption of accurate score function estimation foundational to single-worker models. We establish the inaugural generation error bound for distributed diffusion models in resource-limited settings, establishing a linear relationship with the data dimension $d$ and consistency with established single-worker results. Our analysis highlights the critical role of hyperparameter selection in influencing the training dynamics, which are key to the performance of model generation. This study provides a streamlined theoretical approach to optimizing distributed diffusion models, paving the way for future research in this area.

Low-Rank Adaptation (LoRA) has become the de facto parameter-efficient fine-tuning (PEFT) method for large language models (LLMs) by constraining weight updates to low-rank matrices. Recent works such as Tied-LoRA, VeRA, and VB-LoRA push efficiency further by introducing additional constraints to reduce the trainable parameter space. In this paper, we show that the parameter space reduction strategies employed by these LoRA variants can be formulated within a unified framework, Uni-LoRA, where the LoRA parameter space, flattened as a high-dimensional vector space R^D, can be reconstructed through a projection from a subspace R^d, with d << D. We demonstrate that the fundamental difference among various LoRA methods lies in the choice of the projection matrix, P ∈ R^{D×d}. Most existing LoRA variants rely on layer-wise or structure-specific projections that limit cross-layer parameter sharing, thereby compromising parameter efficiency. In light of this, we introduce an efficient and theoretically grounded projection matrix that is isometric, enabling global parameter sharing and reducing computation overhead. Furthermore, under the unified view of Uni-LoRA, this design requires only a single trainable vector to reconstruct LoRA parameters for the entire LLM -- making Uni-LoRA both a unified framework and a “one-vector-only” solution. Extensive experiments on GLUE, mathematical reasoning, and instruction tuning benchmarks demonstrate that Uni-LoRA achieves state-of-the-art parameter efficiency while outperforming or matching prior approaches in predictive performance.

Generative AI has flourished over the past decade, with generative models advancing in both the industrial and academic sectors. Given various applications, some scenarios have seen the misuse of generative AI, particularly in the integration with the Internet of Things (IoT). IoT devices often handle personal and sensitive data, raising serious concerns about privacy leakage and security breaches when generating data. As a promising countermeasure, machine unlearning has emerged to solve the problems posed by these generative models by effectively removing specific concepts or sensitive information from trained models. In this survey, anchored in generative models, machine unlearning approaches are reviewed, categorized, and discussed comprehensively and systematically. Existing unlearning approaches are classified into gradient-based techniques, task vectors, knowledge distillation, data sharding, and reliable unlearning methods. Apart from previous works, this survey extends the review of attack methods that aim to exploit the vulnerability in generative models and assess the robustness of these unlearning methods. In addition, popular metrics and datasets in machine unlearning research are summarized and evaluated based on effectiveness, efficiency, and security. Finally, we shed light on the future directions of this emerging research topic by discussing applications, highlighting challenges, and exploring research frontiers for the current machine unlearning community and the new investigators to come.

Fine-tuning pretrained models has emerged as a pivotal technique in machine learning, especially in situations with limited, task-aligned training data. However, challenges surface when data sharing encounters obstacles due to stringent privacy regulations or user apprehension regarding personal data disclosure. Earlier works based on secure multiparty computation (SMC) and fully homomorphic encryption (FHE) for privacy-preserving machine learning (PPML) focused more on privacy-preserving inference than privacy-preserving training. While privacy-preserving transfer learning has made progress, efficiency and accuracy tradeoffs remain a key limitation. In response to this gap, we introduce BlindTuner, a system that enables privacy-preserving fine-tuning by training data efficient image transformers (DEiT) directly on homomorphically encrypted data. Additionally, BlindTuner performs privacy-preserving inference, extending its usability beyond training. Experimental results show that BlindTuner achieves accuracy comparable to unencrypted models while delivering training speedups between$1.16{\times }$and$600{\times }$over existing approaches. Furthermore, we explore its application in privacy-preserving federated fine-tuning, enabling solution for collaborative learning without compromising privacy.

With the explosive growth of mobile data, Mobile Crowd Sensing (MCS) has become a popular paradigm for large-scale data collection. The difficulty of data collection and the gaps in workers’ sensing capabilities are key factors to consider in worker recruitment and task assignment. To address these issues, we designed a two-stage cooperative incentive mechanism. In the first stage, a shelving level is introduced to assess task difficulty. Tasks are divided into high-quality and low-quality groups based on quality scores, while workers are categorized into high-ability and low-ability groups based on their historical performance. The Improved Chaotic Particle Swarm Optimization (ICPSO) algorithm is then applied to generate optimal task combinations. In the second stage, we address benefit distribution among cooperating workers by introducing time-dependent rewards and employing Stackelberg game theory to analyze the optimal completion time for both high-ability and low-ability workers. This analysis determines the optimal reward distribution and ability value allocation, with proof of the existence and uniqueness of a Nash equilibrium. Comparative experiments conducted on real-world datasets demonstrate that our cooperative task mechanism outperforms existing task bundling methods, validating its rationality and effectiveness.

Multimodal sentiment analysis plays a critical role in numerous IoT-driven applications, such as personalized smart assistants, healthcare monitoring systems, and intelligent transportation networks, where accurate interpretation of user emotions is vital for enhancing service quality. However, a severe threat of privacy leakage in the multimodal sentiment analysis has been overlooked by previous works. To fill this gap, we propose a Differentially Private Correlated Representation Learning (DPCRL) model to achieve privacy-preserving multimodal sentiment analysis by combining a correlated representation learning scheme with a differential privacy protection scheme. Our correlated representation learning scheme aims to achieve heterogeneous multimodal data transformation to meet the requirements of privacy-preserving multimodal sentiment analysis by learning the correlated and uncorrelated representations, where especially, a pre-determined correlation factor is employed to flexibly adjust the expected correlation among the correlated representations. The differential privacy protection scheme is used to obtain the disturbed correlated and uncorrelated representations by adding Laplace noise for -differential privacy. In particular, the correlation factor can help alleviate the side-effect of the added Laplace noise on the sentiment prediction performance. Finally, via conducting a series of real-data experiments, we validate that our proposed DPCRL model is superior to the state of the art for privacy-preserving multimodal sentiment analysis.

With the popularity of smart mobile devices embedded with rich sensors, mobile crowdsensing (MCS) has gradually attracted the attention of researchers in recent years. Task allocation is a key research problem in MCS systems, where platforms recruit workers and assign them crowd tasks. While previous research has focused on the utility of recruiting workers, the location factor of workers has been ignored. Therefore, this paper proposes a two-stage worker recruitment framework named BW-Selector, which recruits workers in two stages. In the offline stage, this paper proposes an opportunity-crowd worker recruitment algorithm, which first divides the task area with a Voronoi diagram, and then builds a prediction model based on long short-term memory (LSTM) to predict the movement trajectory of workers and solve the cold start in the traditional MCS system. In the online stage, for maximizing the task space coverage under the premise of a limited task budget, this paper proposes a participatory-crowd worker recruitment algorithm based on adaptive threshold selection. Finally, through experiments on real datasets, it is verified that BW-Selector has better performance in terms of task space coverage and running time under the same constraints compared with other methods.

Unmanned agents represent a significant advancement in unmanned control and constitute an important element in the future agent warfare. Their autonomous decision-making capabilities are integral to accomplishing tasks independently. To address challenges inherent in multiparty game scenarios that traditional method struggle with and enhance the applicability and accuracy of game decision-making, this article proposes a novel multiagent confrontation method for unmanned vessels, tailored to a three-party dynamic multistrategy evolutionary game in incomplete information scenarios. The approach introduces a new incentive mechanism designed to enhance both individual and collective profits of agents. Using evolutionary game theory, a three-party model is developed, incorporating interactions among player, enemy, and neutral agents. The model tracks the evolution of strategies to identify stable equilibria across various perceptual conditions. Simulations validate the effectiveness of the proposed method in selecting optimal strategies for unmanned vessels in complex battlefield scenarios, demonstrating its potential for improving autonomous decision-making in multiparty confrontations.

A hypergraph is a mathematical structure capable of representing complex relationships among multiple entities, with widespread applications. However, as the size of hypergraphs continues to grow, their analysis and processing become increasingly challenging. Therefore, how to effectively process large hypergraphs to enable efficient analysis with limited computational resources has become an important research problem. In this article, we propose a method called hypergraph adaptive sampling (HAS), a framework that achieves adaptive batch sampling of hyperedges. This framework adaptively adjusts the sampling probability of hyperedges to ensure that the sampled subhypergraph closely resembles the original hypergraph in terms of structural characteristics, effectively reducing information loss caused by sampling. Extensive experiments are conducted on six public datasets to evaluate, and the results demonstrate that HAS outperforms other algorithms in preserving hypergraph properties. Using our hypergraph sampling method, even when only 20% of the edges of the original image are sampled, the sampled subhypergraph still maintains a small structural error compared to the original hypergraph. The KL divergence of the subhypergraph degree distribution is 0.09, which is 4.1${\boldsymbol\times}$better than the baseline. Last, the efficiency and adaptability of HAS make it applicable not only to hypergraph analysis but also for providing a general and scalable solution for efficiently processing large-scale hypergraph data.

Graph unlearning aims to provably remove some training data from graph neural networks (GNNs) while eliminating their impact on model predictions. Although retraining the GNNs from scratch is a direct and legitimate solution, it entails substantial computational resources. To address this issue, graph unlearning methods have been proposed in the domain of graph data. However, applying existing graph unlearning methods directly to hypergraph data affects model utility. Specifically, the graph unlearning methods severely damage the higher-order structural information of hypergraphs and fail to remove all the information that needs to be unlearned. In this paper, we propose Hyperedge Size-Based Core-Sharing Decomposition, a novel hypergraph unlearning framework tailored to the structural characteristics of hypergraph data. Its contributions include a subgraph partitioning method specific to hypergraphs and an aggregation method based on node coverage. We conduct extensive experiments on seven real-world hypergraph datasets to demonstrate the unlearning efficiency and model utility. Compared to the baseline methods, our approach achieves up to 5% higher accuracy and reduces the average unlearning time by 28%. Furthermore, our node-coverage-based aggregation approach achieves up to 6% higher accuracy.

Age of Information (AoI) has been proposed as a new performance metric to capture the freshness of data. At wireless-powered network edge, the source nodes first need to be charged ready for update transmissions, which means the system AoI is not only decided by the scheduling of update transmissions but also by the designing of charging plan. However, the existing works either only focused on the point of scheduling update transmissions or have a rigid assumption that only one source node can be charged per time. Aiming at making the work more practical and general, we investigate the average AoI optimization problem at wireless-powered network edge with directional charging. Firstly, the theoretical bound of the weighted sum of average AoI of the entire network with a directional charger is analyzed, which is proved to be related to nodes' maximum transmitting interval and the charging strategy. An optimal charging time computation algorithm is proposed to obtain the maximum transmitting interval of each source node by considering the overlapped areas of different charging orientations. After then, an AoI-aware periodical charging scheduling algorithm is proposed to compute a periodical charging schedule while the average AoI is bounded, including a charging period$T$and the charging orientations assigned to each time slot within$T$. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Furthermore, several approximate algorithms are also proposed for average AoI optimization with multiple chargers and network bandwidth constraint. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI.

With the rise of sensor-equipped mobile devices, Mobile Crowd Sensing (MCS) has emerged as an efficient method for information gathering. In smart city environmental sensing, workers can acquire data by merely being within the sensing area. Currently, most studies select opportunistic workers based on the workers’ prior preferences and ignore the effect of movement trajectories on potential opportunistic workers. This may result in the selected opportunistic workers being less-than-ideal, or even ignoring the failure of some tasks to be accomplished, thus resulting in a waste of resources. Therefore, this paper proposes a Recruitment Framework for judging Opportunistic Workers based on Movement Trajectories (RFOW-MT), a two-phase framework for worker recruitment. In the offline phase, combining the neural network model Long Short-Term Memory (LSTM) and Geohash algorithm, an algorithm to detect the set of candidate opportunistic workers is proposed, solving the problems of location privacy and search efficiency. In the online phase, in order to maximize the task spatial coverage under the task budget constraint, a task allocation algorithm based on geographic location packed grouping is proposed. Finally, RFOW-MT outperforms other methods in terms of task spatial coverage and runtime as verified by experiments on real datasets.

Mobile edge computing (MEC) considerably enhances the capabilities and performance of connected autonomous vehicles (CAVs) by deploying edge servers (ESs) on roadside units (RSUs) near CAVs, thereby ensuring low-latency services. Given the constrained and costly nature of ES resources (computing, storage, and bandwidth), equitable ES utilization is critical for CAV operations. However, fairness considerations are often overlooked in current budgeted edge server placement (ESP) strategies, potentially worsening resource imbalances and compromising user experience. This paper investigates the fairness-aware budgeted edge server placement (FESP) problem within RSUs, proving its NP-hardness. To address FESP, we first propose FESP-O, an integer programming-based optimal approach for small-scale problems, followed by FESP-APX, an approximation approach for large-scale scenarios that provides near-optimal solutions. We analyze the time complexity and approximation ratio of our proposed algorithms and validate their efficacy through experiments on real-world datasets. Extensive experimental results demonstrate significant performance improvements over baseline and state-of-the-art methods, indicating practical suitability and efficiency.

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 224% compared with state-of-the-art distributed DNN inference schemes.

Wireless Power Transfer technology, which can charge IoT devices over the air, has become a promising technology for IoT networks. In wireless-powered IoT networks, broadcasting is a fundamental networking service for disseminating messages to the whole network. To seek a fast and collision-free broadcast schedule, the problem of Minimum Latency Broadcast Scheduling (MLBS) has been well studied when nodes are energy-abundant. However, in wireless-powered networks, a node can only receive or transmit packets after it has harvested enough energy. In such networks, it is of great importance to exploit the divergent harvested energy to reduce the broadcast latency. Unfortunately, existing works always assume a predetermined tree and a fixed transmission power for broadcast scheduling, which greatly limits their performance. Thus, in this article, we investigate the first work for the MLBS problem in wireless-powered networks without relying on predetermined trees. First, the problem is formulated and proved to be NP-hard. Then, two structure-adaptive scheduling algorithms are proposed with a theoretical bound, which can intertwine the construction of broadcast tree with the computation of an energy-aware schedule simultaneously. Furthermore, a power-aware scheduling method is also proposed to take the structure of the broadcast tree, the adjustment of nodes’ transmission powers, and the interference during transmissions into account simultaneously. Additionally, the algorithm for the MLBS problem under the physical interference model is also studied. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Mobile Crowd Sensing (MCS) is a widely adopted approach for data collection across diverse applications. However, as the number of tasks and participants in MCS continues to grow, task allocation and dynamic pricing challenges have become increasingly complex. Existing research primarily focuses on single-task allocation problems, often overlooking the diversity and complexity inherent in multi-task, multi-worker scenarios. To address these challenges, this paper proposes the Enhanced Heuristic Search with Tabu and Local Search (EH-STLS) algorithm, alongside a Kolmogorov-Arnold Deep Q Network (KDQN) model, both grounded in a Stackelberg game framework. The EH-STLS algorithm employs a multi-objective optimization framework that combines tabu search and local search strategies to improve the efficiency of worker-task matching while ensuring high task quality. The KDQN model views task publishers as leaders and treats crowdsourcing platforms, encryption agencies, and workers as followers to achieve optimal dynamic pricing and utility allocation. Extensive experiments on synthetic datasets generated from real-world data reveal that the proposed methods substantially outperforms the baseline algorithms regarding task allocation quality and pricing efficiency, achieving up to a 21.7% increase in task quality and a 16.4% improvement in pricing effectiveness.

Current recommendation systems recommend goods by considering users' historical behaviors, social relations, ratings, and other multi-modals. Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed. With the development of brain-computer interfaces, it is time to explore next-generation recommenders that show users' real-time thoughts without delay. Electroencephalography (EEG) is a promising method of collecting brain signals because of its convenience and mobility. Currently, there is only few research on EEG-based recommendations due to the complexity of learning human brain activity. To explore the utility of EEG-based recommendation, we propose a novel neural network model, QUARK, combining Quantum Cognition Theory and Graph Convolutional Networks for accurate item recommendations. Compared with the state-of-the-art recommendation models, the superiority of QUARK is confirmed via extensive experiments.

This paper studies super-resolution harmonic retrieval for strictly non-circular coherent signals. We develop gridless sparse representations of both their covariance and pseudo-covariance matrices over a common matrix-form atom set. This enables the decoupled atomic norm minimization (D-ANM) technique to exploit the sparsity of the covariance and pseudo-covariance matrices jointly. Further, by effectively utilizing the inherent mutual coupling characteristics between the covariance and pseudo-covariance matrices, additional constraints are properly imposed to reflect and enforce desired structure information represented by such matrices and their augmented matrix. It leads to a novel structure-based sparse optimization method, called double decoupled atomic norm minimization (DD-ANM). In addition, performance analysis is provided for the proposed DD-ANM method in practical settings. Simulation results reveal that the proposed DD-ANM outperforms the benchmark methods in terms of lower estimation errors.

Federated learning (FL) is a promising distributed learning framework in terms of privacy protection and communication saving. Most existing FL techniques are developed for independent-and-identically-distributed (IID) datasets, but suffer from performance degradation under Non-IID datasets. To cope with this issue, most existing work designs solutions from data perspectives (e.g., sharing some data samples between local devices) to eliminate the heterogeneity of distributed datasets, which causes extra communication overhead and may expose user privacy that contradicts FL's original intention. Unlike the existing data-based methods, we propose a generative adversarial network (GAN) based FL, named as GANFed, which is designed from a feature perspective. Specifically, we embed a discriminator into the FL network, which works with the shallow layers as a generator to form a GAN in FL. By incorporating such a GAN, the output of the shallow layers tends to present more IID features compared with the original Non-IID input data. These extracted features from the shallow layers are then used to train the deep layers of the FL network. In this way, the proposed GANFed reduces the weight divergence of the local models, and hence improves the performance of FL. Without data exchange, our GANFed avoids the leakage of user privacy and reduces the communication overhead. Experimental results show that our GANFed outperforms the standard FedAvg on Non-IID dataset in terms of improved test accuracy.

Machine learning models contain much information about the training dataset, so even if some data points are deleted, the private information can still be inferred. To counteract this problem, "machine unlearning", as an emerging data management approach, is proposed to remove data from the databases and the influence of data from the trained models. Such a technique is vital in the current era of data-driven applications, where the privacy and security of users can be guaranteed. Yet, machine unlearning is still in its early stage, and there are rare existing methods for machine unlearning in the federated setting that is a more practical and crucial scenario. Therefore, this paper investigates the federated machine unlearning problem where the local clients of a federated system intend to delete their local private data appropriately. The proposed method is termed Approximate Federated unlearning (Appro-Fun), which adopts differential privacy and second-order optimization to achieve (ϵ, δ)-approximate unlearning on trained models. Rigorous theoretic analysis presents the performance guarantee of Appro-Fun, and real-data experiments validate the advantages of Appro-Fun compared with the state-of-the-art.

The Industrial Edge Computing (IEC) network has recently received considerable attention, where industrial devices offload their computation-intensive and delay-sensitive tasks to servers located at the network edge. Task offloading scheduling is a fundamental problem in IEC networks to achieve satisfactory quality of service. Many prior efforts have been devoted to scheduling task offloading for networks with complete information, while the complete information is hard or even infeasible to acquire by the scheduler. Therefore, their performance degrades in IEC networks with incomplete information. Scheduling task offloading for IEC networks with incomplete information is urgent and presents great technical challenges. This paper proposes a group-centric task offloading framework tailored for IEC networks with incomplete information, and models the minimum delay scheduling problem as a Partially Observable Markov Decision Process. Then, the SGOS algorithm integrating the Long Short-Term Memory with Soft Actor-Critic networks in reinforcement learning is proposed to devise online task offloading schedules for IEC networks with incomplete information. Extensive experimental results verify that the SGOS algorithm can achieve the best performance compared with base-line schemes in terms of major metrics, including convergence, delay, and workload balance.

When multimodal AI systems increasingly utilize diverse data sources to achieve advanced understanding and interaction, they inevitably collect vast amounts of sensitive information, thus highlighting the urgent need for robust privacy safeguards, especially as these technologies expand into fields like healthcare, finance, and education. Existing research on data privacy in AI, encompassing adversarial training-based models, differential privacy-based models, and differentially private transform-based models, often neglects the inter-correlation inherent in multi-sensor data. To address this gap, we propose the differentiAl Private OnLine muLti-sensor data predictiOn model (APOLLO), which simultaneously considers intra-correlation and inter-correlation to enhance privacy protection while maintaining predictive performance. Under the proposed APOLLO frame-work, we design two implementations: APOLLO I, which ensures$\epsilon$-differential privacy by adding Laplace noise to each correlated data segment, and APOLLO II, which applies additional noise to make the concatenated multi-sensor data realize$\epsilon{-}$differential privacy. Furthermore, we conduct the theoretical analysis to reveal the relationship between performance influence and the privacy budget, providing guidelines for noise addition with the aim of achieving certified performance. Comprehensive experiments validate the effectiveness of the APOLLO model, establishing a new standard for privacy-preserving multi-sensor data prediction.

Mobile crowdsourcing is rapidly growing with Artificial Intelligent of Things. At the same time, the type and complexity of the tasks requested by the requester change and diversify. Therefore, how to design allocation algorithms for the situation of increasing task complexity is particularly critical. In this paper, to cope with this problem, the idea of worker coalition collaboration and reputation evaluation mechanisms are introduced into it. A two-stage allocation based on same-region and cross-region is performed in the divided regional grid. In the first stage, multi-worker and multi-task allocation is realized by combining the reverse auction theory based on the workers’ historical reputation value, which motivates the workers with high reputation value to choose their tasks and contribute data with high sensed quality. The second stage utilizes genetic algorithms to select a coalition of workers for cross-region sensed execution for tasks that do not meet sensed quality requirements. This process will provide additional payoff incentives to compensate for travel costs within the worker coalition, increasing the number of tasks completed and maximizing social welfare. Finally, multiple comparison experiments on the real dataset Yelp are conducted for validation.

The evolution of 5G and 6G technologies has boosted mobile network speed, reduced delays, and widened coverage, empowering Mobile Crowd Sensing (MCS) to overcome surface and terrain obstacles. However, this advancement brings new hurdles for online task assignment. While most MCS methods suit surface applications, they struggle with allocation in complex environments. This paper focuses on MCS in varied settings like surface, air, and high altitude. Currently, planning-based task assignment works better for one-to-one or one-to-many scenarios, with limited options for many-to-many situations. Improving platform utility, attracting top-quality crowd workers, and enhancing task completion efficiency are vital. To tackle these challenges, the paper introduces a spatial division algorithm using 3D Voronoi diagrams for complex environments. This algorithm utilizes task coordinates to delineate assignment spaces. Additionally, it introduces a two-stage many-to-many online task assignment algorithm (MOTA) that forecasts crowd workers’ arrival probabilities and combines auction-based incentives with differential evolution algorithms. MOTA ensures efficient matching of workers and tasks within spatio-temporal constraints, balancing both parties’ interests. Finally, comparative experiments on real datasets assess the proposed MOTA algorithm’s usability and effectiveness based on overall gain, running time, task count, and assignment rate.

With the rapid development of crowd sensing computing, Mobile crowdsourcing (MCS) has become an indispensable part of today’s society. While MCS brings convenience to people, it also exposes them to the risk of privacy leakage. In addition, the demand for data is increasing, and the personalized privacy requirements of crowd workers may affect the service quality. In order to address these problems, this paper proposes a personalized privacy protection incentive mechanism (PPPIM) for MCS based on homomorphic encryption and edge computing. Firstly, this paper designs a personalized privacy metric, using social attributes and private attributes of crowd workers to calculate the privacy level required by crowd workers. Then, based on homomorphic encryption and edge computing, a personalized residual federated security learning scheme (PRFSL) is proposed to ensure the security, timeliness, integrity of task data and the privacy of crowd workers’ needs to improve encryption efficiency. Finally, based on the evolutionary game, a personalized privacy incentive mechanism is proposed to improve the overall service utility. Experimental comparisons based on real datasets show that the proposed scheme can not only ensure the security, timeliness, and integrity of task data more effectively. It can also effectively reduce data processing time, improve the probability of crowd workers actively completing tasks and the overall service quality utility.

In this paper, we propose Byzantine-Robust Decentralized Federated Reinforcement Learning (BR-DeFedRL), an innovative framework that effectively combats the harmful influence of Byzantine agents by adaptively adjusting communication weights, thereby significantly enhancing the robustness of the learning system. By leveraging decentralized learning, our approach eliminates the dependence on a central server. Striking a harmonious balance between communication round count and sample complexity, BR-DeFedRL achieves efficient convergence with a rate of $\mathcal{O}\left( {\frac{1}{{TN}}} \right)$, where T denotes the communication rounds and N represents the local steps related to variance reduction. Notably, each agent attains an ϵ-approximation with a state-of-the-art sample complexity of $\mathcal{O}\left( {\frac{1}{{\varepsilon N}} + \frac{1}{\varepsilon }} \right)$. Extensive experimental validations further affirm the efficacy of BR-DeFedRL, making it a promising and practical solution for Byzantine-robust decentralized federated reinforcement learning.

To address the online learning problem in distributed systems, Streaming Federated learning (SFL) enables immediate model training by clients upon collecting new data, finding wide applications in AI-enabled Internet-of-Things and sensor networks. Given the variability in data distribution across different historical periods, the ability to recall and rapidly apply previously encountered data distributions significantly enhances the efficiency and accuracy of model training. In this paper, a demo based on the real-world temperature datasets is presented to demonstrate the importance of history knowledge in local training and the federating process of SFL, which also shows that vanilla federated learning without considering the history knowledge may even be harmful to model training. Observing this, we propose Fed-HIST, a Federated learning framework that enables the clients to learn from the HISTory knowledge of the whole distributed learning system. Unlike direct raw data storage, Fed-HIST employs model architectures to capture the data distributions, offering a more space-efficient and privacy-preserving method of knowledge storage on a server pool. Additionally, a model similarity comparison scheme is designed to retrieve beneficial knowledge from the pool uploaded by the clients in the past. Such a history-aware federation can enhance the efficiency of training each client, only requiring the recurrence of similar data distributions among SFL participants. We validate our framework through extensive simulations on MNIST, Fashion-MINST, CIFAR10, and CIFAR100 datasets, benchmarking against 9 baselines and highlighting the importance of federating from history in SFL problem through necessary ablation studies.

With the rapid development of machine learning technologies, data-driven spectrum prediction enables intelligent dynamic spectrum access to alleviate the bottleneck of spectrum resource scarcity and congestion. However, spectrum prediction still faces some key challenges, including how to exploit the implicit but crucial multi-band correlations in wideband spectrum data, and how to capture the temporal dynamics across different bands. Due to the ignorance of such crucial features inherent from spectrum occupancy patterns, existing learning-based spectrum prediction methods unfortunately suffer from inaccurate prediction performance. To fill this gap, this paper develops a novel model of graph convolutional regression neural network (GCRNN), by introducing efficient graph structure learning (GSL-GCRNN) for dynamic multi-band spectrum prediction. The proposed GSL-GCRNN model is designed to adaptively learn both the multi-band and temporal correlations in dynamic wideband spectrum scenarios. Empowered by the graph structure estimator, graph convolutional networks are fueled to effectively extract the correlations in the frequency domain, followed by gated recurrent unit networks to further extract the temporal correlations of each band. It is worth noting that the graph structure estimator further enables to learn the multi-band correlations across different time periods on-the-fly, enhancing the accuracy of wideband spectrum prediction in dynamic environments. Simulation results verify that our GSLGCRNN approach outperforms the benchmark methods.

Graph de-anonymization is a technique used to reveal connections between entities in anonymized graphs, which is crucial in detecting malicious activities, network analysis, social network analysis, and more. Despite its paramount importance, conventional methods often grapple with inefficiencies and challenges tied to obtaining accurate query graph data. This paper introduces a neural-based inexact graph de-anonymization, which comprises an embedding phase, a comparison phase, and a matching procedure. The embedding phase uses a graph convolutional network to generate embedding vectors for both the query and anonymized graphs. The comparison phase uses a neural tensor network to ascertain node resemblances. The matching procedure employs a refined greedy algorithm to discern optimal node pairings. Additionally, we comprehensively evaluate its performance via well-conducted experiments on various real datasets. The results demonstrate the effectiveness of our proposed approach in enhancing the efficiency and performance of graph de-anonymization through the use of graph embedding vectors.

In recent years, immense developments have occurred in the field of Artificial Intelligence (AI) and the spread of broadband and ubiquitous connectivity technologies. This has led to the development and commercialization of Digital Twin (DT) technology. The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks (DTNs), which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets. DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing, computing, and DT modeling. The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges. The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. Also, currently, there is not enough literature that focuses on privacy and security issues in DTN applications. In this survey, we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance. Next, we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components. We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs. We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues. Finally, we provide some open research issues and problems in the field of DTN privacy and security.

Abstract Deep learning‐based models have become ubiquitous across a wide range of applications, including computer vision, natural language processing, and robotics. Despite their efficacy, one of the significant challenges associated with deep neural network (DNN) models is the potential risk of copyright leakage due to the inherent vulnerability of the entire model architecture and the communication burden of the large models during publishing. So far, it is still challenging for us to safeguard the intellectual property rights of these DNN models while reducing the communication time during model publishing. To this end, this paper introduces a novel approach using knowledge distillation techniques aimed at training a surrogate model to stand in for the original DNN model. To be specific, a knowledge distillation generative adversarial network (KDGAN) model is proposed to train a student model capable of achieving remarkable performance levels while simultaneously safeguarding the copyright integrity of the original large teacher model and improving communication efficiency during model publishing. Herein, comprehensive experiments are conducted to showcase the efficacy of model copyright protection, communication‐efficient model publishing, and the superiority of the proposed KDGAN model over other copyright protection mechanisms.

Compressive Sensing (CS) technology, which handles large amounts of data in its low-dimensional form, has been shown to enjoy excellent transmission and storage efficiency. To further improve the stability and robustness of CS-based wireless communication system, Reconfigurable Intelligent Surface (RIS) technology has been introduced to enhance the transmission link connectivity. Current researches generally assume perfect RIS; however, some of its elements may be damaged and fail to work, which degrades the sparse signal recovery. Therefore, this paper establishes a double-sparsity optimization model to jointly recover the sparse signal and diagnose the RIS element failure. For the scenarios with and without Channel State Information (CSI) at the receiver, Double-Sparsity based algorithm (DS), and Atomic norm and Double-Sparsity based algorithm (ADS) exploiting Alternating Direction Method of Multipliers (ADMM) framework are proposed respectively. Additionally, a novel ℓB,B norm is incorporated to further constrain the block and binary characteristics of RIS failures, and the resulting improved versions of DS and ADS are termed as Binary and Block-Sparsity based algorithm (BBS) and Atomic norm, Binary and Block-Sparsity based algorithm (ABBS). Simulations verify the effectiveness and robustness of the proposed algorithms, and show the improved performance of the BBS and ABBS algorithms compared with the DS and ADS algorithms.

Federated learning (FL) facilitates collaborative model training in the autonomous Internet of Things (IoT) system while preserving the privacy of local data on IoT clients. Nonetheless, the inherent non-IID characteristic of local data leads to poor convergence of a global model. Moreover, the global model fails to satisfy the personalized task demands of all clients. To address the above issues, this article studies client grouping and local model aggregation in FL from two perspectives: 1) measure of client data distribution and 2) distribution similarity among clients. To this end, a novel confidence-based similarity-aware personalized FL algorithm (FedCS) for personalized autonomous IoT is proposed by developing three key innovations, namely, a public average confidence (PAC) measure, a client grouping strategy with dynamic sampling (CGDS), and a sequential aggregated weight (SAW) strategy. Specifically, the PAC measure utilizes a public data set on the server side to estimate the client’s data distribution, which promotes a fair estimate of distribution similarity among clients while minimizing privacy risks. The CGDS strategy focuses on distribution similarity among clients and approximates the client grouping problem as an auxiliary task selection problem in multitask learning. This strategy assigns a client into multiple groups and enables the valuable information from each client to circulate among multiple groups. The SAW strategy further incentivizes more similar clients within a group to share greater knowledge and generates an adaptive aggregated weight for each client within a group. A thorough experiment on CIFAR10 and two healthcare benchmarks shows that FedCS achieves a superior performance.

The Internet of Things (IoT) is penetrating many aspects of our daily life with the proliferation of artificial intelligence applications. Federated learning (FL) has emerged as a promising paradigm enabling many intelligent IoT applications; however, the transmitted model gradients or weights still encode private information, which can be exploited to launch inference attacks. One popular way is to apply local differential privacy (LDP) into FL. However, existing work does not provide a practical solution due to two issues. First, the fine-grained range difference of weights in different layers of an FL model has not been explicitly considered. Second, the accumulated privacy budget may cause a budget explosion. In this article, we propose a local differentially private scheme to train clustered FL models on heterogeneous IoT data by using adaptive clipping, weight compression, and parameter shuffling (namely, ACS-FL), aimed at mitigating the curse of dimensionality, the amount of LDP noise, and the communication overhead of IoT devices. Empirical evaluations on MNIST, fashion-MNIST, and Federated Extended MNIST demonstrate that ACS-FL achieves a superior performance in balancing the tradeoff between privacy and utility.

Federated learning (FL) enables multiple IoT devices to collaboratively train a global model without centralizing raw data. However, achieving optimal performance for each device is challenging due to the heterogeneity of IoT data. Moreover, IoT devices, constrained in computational power and energy, necessitate innovative privacy protection solutions in FL. In this paper, we propose privacy-enhanced FL methods to address data heterogeneity in IoT. Our approach includes a dual-model framework for each device, combining a shared model to facilitate inter-client communication with a personalized model. This personalized model is developed from local aggregation, utilizing layer-wise aggregation weights generated by local hypernetworks. This integration effectively balances collective intelligence with client-specific knowledge, addressing data heterogeneity for improved personalization. Additionally, we propose a Local Gradient Shielding (LGS) method to protect training data. This method conceals gradients in locally trained personalized models, preventing data reconstruction and ensuring client privacy with minimal resource consumption. Extensive evaluations on benchmark datasets, showcasing various types and degrees of data heterogeneity, demonstrate the superiority of our approaches over current baselines.

With the advent of functional encryption (FE), new possibilities for the computation of encrypted data have arisen. FE enables data owners to grant third-party access to perform specified computations without disclosing their inputs. It also provides computation results in plaintext, unlike fully homomorphic encryption (FHE). The ubiquitousness of machine learning (ML) has led to the collection of massive private data in the cloud computing environment. This raises potential privacy issues and underscores the need for more private and secure computing solutions. Numerous efforts have been made in privacy-preserving ML (PPML) to address security and privacy concerns. There are approaches based on FHE, secure multiparty computation (SMC), and, more recently, FE. Compared to FHE-based PPML techniques, FE-based PPML is still in its infancy. In this article, we provide a survey of PPML works based on FE, summarizing state-of-the-art literature. We focus on inner product-FE, function-hiding inner product encryption, and quadratic-FE-based ML models for PPML applications. We analyze the performance and usability of the available FE libraries and their applications to PPML. We also discuss future research directions for FE-based PPML approaches. To the best of our knowledge, this is the first work to survey FE-based PPML approaches.

In Mobile Crowdsourcing (MCS), the task information released by task publishers and the sensed data submitted by workers may expose their privacy, while the rapid growth of MCS imposes increasing data processing pressure on cloud platforms and mobile devices. To address these challenges, a bilateral privacy protection scheme based on adaptive location generalization and grouping aggregation is presented in this paper. The scheme uses federated learning as a framework and utilizes edge computing to reduce the data processing burden on cloud platforms and mobile devices. This paper proposes the adaptive location generalization algorithm (KM-ALG) and a real task location release mechanism based on the RSA algorithm to protect the task location privacy of the task publisher. For workers’ privacy protection, the lightweight multiple perturbation algorithm based on localized differential privacy (LDP-MP) proposed in this paper is used to protect workers’ data privacy. Aiming at the problem of data quality loss caused by perturbation, a perturbation elimination mechanism based on homomorphic encryption technology is proposed. In order to prevent workers’ sensed data from leaking location information, a grouping aggregation mechanism is used to destroy the correspondence between workers and submitted data, thereby protecting workers’ location privacy. In addition, a task allocation scheme adapted to task location privacy protection is also proposed. Finally, the effectiveness of the proposed algorithm is verified through experiments on multiple real data sets.

The distinct attributes of Internet of Things (IoTs) devices, including the disparity between training and testing data distributions and limited availability of training data, pose challenges for deep learning models in effectively addressing unseen domain segmentation tasks. Federated Learning (FL) can increase the participation of various data contributors, thus has great potential to develop a unified framework to shed light on the relationship between unseen domains and generalized domains. In this paper, we proposed an FL-based unseen domain segmentation model. The architecture includes (1) an external memory module as an object feature guide to reduce the feature ambiguity of unseen domain objects. (2) A re-attention activation mechanism for better completing localization of unseen domain objects, enhancing the features of potential targets and suppressing interference features. (3) A self-supervised learning paradigm for achieving specific object feature exploration. Based on flexible splitting and combining, our model is able to capture both personalization and generalization capabilities, the client side retains a strong personalization ability, while the server side has a strong generalization ability. Moreover, taking into account the inherent limitations in computing and storage resources commonly associated with IoT devices, the introduced model leverages the concept of optional dependencies to enable efficient inference within resource-constrained client environments. Our proposed model is validated through extensive experiments. The approach proposed in this paper outperforms the generalization capabilities of state-of-the-art work on several benchmarks.

With the rapid progression of mobile crowdsourcing (MCS) technology, its growing influence in our daily lives has established it as a crucial component of modern society. However, while the convenience of MCS is widely appreciated, it also poses significant threats to personal privacy, particularly location privacy. This article introduces a novel system for personalized location privacy protection in MCS. The system is divided into three main parts. The first part presents an innovative algorithm that calculates the location privacy level of crowd workers. This algorithm is crucial in determining the location privacy level required by each individual crowd worker. The second part involves the design of a personalized differential privacy protection (P-DP) algorithm, which is based on the exponential mechanism. This algorithm provides varying degrees of privacy protection strength, tailored to the location privacy protection level of each crowd worker. Furthermore, we incorporate a trusted third party (TP) server to act as an intermediary. This server eliminates any correlation between the crowd workers and the data. It is also tasked with calculating the location privacy level and reward for each crowd worker. The third part of the system is the personalized localized differential privacy (LDP) protection (P- LDP) algorithm, this algorithm is designed to further solve the problem of privacy disclosure caused by the TP server being attacked. Finally, we have conducted a comprehensive evaluation of the proposed location privacy protection system using real data sets, and the results demonstrate that the system can effectively balance the location privacy protection of crowd workers and the availability of location data, thereby improving the efficiency and reliability of MCS.

The exponential growth of Internet of Things (IoT) data has pushed the boundaries of data analysis, but it has also raised concerns about the increasing commoditization of personal privacy. The intriguing problem of trading private IoT data is the focal point of this paper. We delve into three fundamental questions: Firstly, we determine the minimum privacy that a broker must purchase from data owners to achieve aggregate statistics with a fixed accuracy goal. Secondly, we propose an innovative arbitrage-free pricing framework that empowers brokers to set prices for aggregate statistics, maximizing utility while ensuring fairness. Lastly, we address the challenge of fairly compensating data owners for their privacy losses while providing economic guarantees. To achieve these objectives, we propose a data trading framework in this paper. Our framework considers ubiquitous data correlations and the potential involvement of untrusted brokers, ensuring that the total compensations remain within the brokers’ budget; each owner is guaranteed to receive non-negative revenues, and cunning data owners cannot exploit misreported privacy demands to gain extra compensations. Experiments on the MovieLens dataset demonstrated the robustness of our framework.

The novel coronavirus has been spreading worldwide and emerged as a public health crisis. As the rapid rise of infected population count, a wide variety of stringent non-pharmaceutical interventions have been taken by cities and countries around the globe, including mobility reduction, social distancing and regional lockdown. The efficacy of these interventions is hard to quantify as individuals violate policies, travel inadvertently or deliberately, and spread the virus without themselves being infected. Furthermore, the publicly available pandemic data on infectious rates and other epidemiological data are unreliable and limited, and are even underestimated. In this paper, we intend to interpret and forecast the spreading dynamics of Covid-19 and quantify the efficacy of quarantine control adopted by Wuhan, Italy, South Korea and the United States of America, employing a hybrid model of an epidemiological model and a data-driven neural network model. Furthermore, since the Covid-19 has prompted global travel restrictions, aggravated unemployment, and influenced the global economy, which exemplify the great societal cost of interventions in the battle of halting Covid-19 spreading. We intend to develop optimal quarantine control under which the tradeoff between Covid-19 containment and the societal cost of quarantine control can be optimized. Optimal quarantine control enables communities have opportunities to catch their breath to reserve healthcare resources preemptively, while the Covid-19 spreading can be halted. Our results unequivocally indicate that governments that taken stringent interventions starting from the initial stage can efficiently halt the spreading of Covid-19; furthermore, the total societal cost of such interventions is greatly smaller.

With the development of the Industrial Internet of Things (IoT), the work of large-scale data collection makes spatiotemporal crowdsensing (SC) play an important role. Mobile devices equipped with sensors could act as workers to collect and process data for uploading. In the task allocation process, a fully static allocation fails to meet the needs of realistic conditions, while a completely dynamic allocation fails to achieve the desired results. Therefore, we assume a task-scheduled execution scenario that combines the above two conditions. In the pre-allocation process, an original time location constraints (ORTA) allocation algorithm is first proposed. Then it is optimized (OPTA) to fully utilize the remaining time of the workers and increase the matched number. In addition, the design of the incentive mechanism is an effective means to improve the task completion rate of the platform. To efficiently utilize the limited platform budget in the long run, a Q-learning-based algorithm is proposed to identify target inspire tasks and subsequently increase their reward to attract workers’ participation. Finally, comparison experiments are conducted on real datasets to verify the effectiveness of our algorithm. Furthermore, the experiments on a Raspberry Pi local terminal are conducted under a satellite-based environment.

Mobile crowdsourcing (MCS) is a new paradigm that uses various mobile devices to collect sensed data. Mobile edge computing (MEC) can effectively utilize the device resources of mobile edge, greatly relieve the pressure of network bandwidth and improve the response speed. In this article, we construct a four-party evolutionary game model consisting of the platform, crowd workers, task requesters, and edge servers. The computing tasks are conducted on edge servers, which greatly reduce remote data transmission and network operating costs and improve service quality. Taking into account the collusion between the platform and workers, and that between the platform and requesters, we analyze the stability of the strategic equilibrium in MCS using replicator dynamics methods. The optimal payoff strategies of the participants in different initial states are obtained. To prevent cheating and false-reporting problems, reward and punishment strategies are provided. Finally, the stability of the equilibrium of the four-party evolutionary game system is verified by simulation experiments, and an incentive strategy is designed to motivate all parties to choose the trust strategies.

Camouflaged objects often blend in with their surroundings, making the perception of a camouflaged object a more complex procedure. However, most neural-network-based methods that simulate the visual information processing pathway of creatures only roughly define the general process, which deficiently reproduces the process of identifying camouflaged objects. How to make modeled neural networks perceive camouflaged objects as effectively as creatures is a significant topic that deserves further consideration. After meticulous analysis of biological visual information processing, we propose an end-to-end prudent and comprehensive neural network, termed IdeNet, to model the critical information processing. Specifically, IdeNet divides the entire perception process into five stages: information collection, information augmentation, information filtering, information localization, and information correction and object identification. In addition, we design tailored visual information processing mechanisms for each stage, including the information augmentation module (IAM), the information filtering module (IFM), the information localization module (ILM), and the information correction module (ICM), to model the critical visual information processing and establish the inextricable association of biological behavior and visual information processing. The extensive experiments show that IdeNet outperforms state-of-the-art methods in all benchmarks, demonstrating the effectiveness of the five-stage partitioning of visual information processing pathway and the tailored visual information processing mechanisms for camouflaged object detection. Our code is publicly available at: https://github.com/whyandbecause/IdeNet.

By combining edge computing and parallel computing, distributed edge computing has emerged as a new paradigm to exploit the booming IoT devices at the edge. To accelerate computation at the edge,i.e., the inference tasks for DNN-driven applications, the parallelism of both computation and communication needs to be considered for distributed edge computing, and thus, the problem of Minimum Latency joint Communication and Computation Scheduling (MLCCS) is proposed. However, existing works have rigid assumptions that the communication time of each device is fixed and the workload can be split arbitrarily small. Aiming at making the work more practical and general, the MLCCS problem without the above assumptions is studied in this paper. Firstly, the MLCCS problem under a general model is formulated and proved to be NP-hard. Secondly, a pyramid-based computing model is proposed to consider the parallelism of communication and computation jointly, which has an approximation ratio of$1+\delta$, where$\delta$is related to devices' communication rates. An interesting property under such a computing model is identified and proved,i.e., the optimal latency can be obtained under arbitrary scheduling order when all the devices share the same communication rate. When the workload cannot be split arbitrarily, an approximation algorithm with a ratio of at most$2\cdot (1+\delta )$is proposed. Additionally, for handling the dynamically changing network scenarios, several algorithms are also proposed accordingly. Finally, the theoretical analysis and simulation results verify that the proposed algorithm has high performance in terms of latency. Two testbed experiments are also conducted, which show that the proposed method outperforms the existing methods, reducing the latency by up to 29.2% for inference tasks at the edge.

Due to the large volume of IoT data, conventional sensor network based and the cloud base IoT systems cannot handle latency-sensitive and resource-consuming IoT applications. Sensor networks do not have enough computation resources and also suffer from a limited network lifetime. On the other hand, the cloud based IoT system is far away from the users and the physical world, and cannot satisfy the real-time requirement of IoT applications. We adopt the IoT edge network to address these challenges and process IoT applications in modern IoT systems. The IoT edge network is an emerging computing architecture in the IoT. Compared to the sensor nodes in conventional sensor networks, the edge servers have more computation resources. Compared to the remote cloud, the edge servers are closer to the users and the physical world. However, processing IoT applications in the edge network still remains challenging. First, how to process concurrent IoT applications has not been fully investigated. Second, the inner relationship between the network resource and the application latency has not been deeply analyzed. Third, the function conflict problem in edge servers has not been taken seriously. To solve the above challenges, we propose the Energy and Latency Efficient Processing Plan for Concurrent IoT Applications Problem which aims to construct an application processing plan by jointly considering the concurrency, the energy-latency relationship, and the function conflict problems. We prove that such a problem is NP-Hard, and algorithms are proposed accordingly. Furthermore, we also estimate the performance of the proposed algorithms by numerical results.

Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by considering the workers' ability benefits, distance benefits, and trust benefits in this paper. The worker recruitment problem is modeled as an Undirected Complete Recruitment Graph (UCRG), for which a specific Tabu Search Recruitment (TSR) algorithm solution is proposed. An optimal execution team is recruited for each task by the TSR algorithm, and the collaboration team for the task is obtained under the constraint of privacy loss. To enhance the efficiency of the recruitment algorithm on a large scale and scope, the Mini-Batch K-Means clustering algorithm and edge computing technology are introduced, enabling distributed worker recruitment. Lastly, extensive experiments conducted on five real datasets validate that the recruitment algorithm proposed in this paper outperforms other baselines. Additionally, TREF proposed herein surpasses the performance of state-of-the-art trust evaluation methods in the literature.

Age of Information, which emerged as a new metric to quantify the freshness of information, has attracted increasing interests recently. To optimize the system AoI, most existing works try to compute an efficient schedule from the point of data transmission. Unfortunately, at wireless-powered network edge, the charging schedule of the source nodes also needs to be decided besides data transmission. Thus, in this paper, we investigate the joint scheduling problem of data transmission and energy replenishment to optimize the maximum peak AoI at network edge with directional chargers. To the best of our knowledge, this is the first work that considers such two problems simultaneously. Firstly, the theoretical bounds of the maximum peak AoI with respect to the charging latency are derived. Secondly, for the minimum peak AoI scheduling problem with a single charger, an optimal scheduling algorithm is proposed to minimize the charging latency, and then a data transmission scheduling strategy is also given to optimize the maximum peak AoI. The proposed algorithm is proved to have a constant approximation ratio of up to 1.5. As for the scenario with multiple chargers, an approximate algorithm is also proposed to minimize the charging latency and the maximum peak AoI. Additionally, when the network bandwidth constraint is considered, the algorithm which considers the parallelism of the charging process and data transmission process is also proposed to reduce the latency and the maximum peak AoI. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency and AoI.

The energy-harvesting sensor network is a new network architecture to further prolong the lifetime of sensor networks and enhance the quality of IoT services. Due to the inherent problems of energy-harvesting sensor networks, it is really hard to collect fresh and useful sensory data. To solve the above problems, we investigate the data collection scheme in edge-assisted energy-harvesting sensor networks and try to collect fresh and useful sensory data from such networks. Enlightened by the concept of the age of information, we define a new metric, the age of useful information (AoUI), to measure the usefulness and freshness of the sensory data. Furthermore, we define the Minimizing the Maximum Age of Useful Information problem (Min-AoUI) to construct a sensory data collection method to minimize the AoUI of the sensory data. We prove that the Min-AoUI problem is NP-Hard, and approximation algorithms are proposed to solve this problem. The time complexity and the approximation ratio of this algorithm are analyzed. The performance of the algorithm is also verified by extensive experimental results.

Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination's perspective. To optimize the system AoI, most existing works focused on the point of scheduling of update transmissions. While at wireless-powered network edge, the source nodes can only transmit their updates after being charged ready, which means the system AoI is not only determined by the update transmission strategies, but also the charging strategies. Thus, in this paper, we investigate the first work to optimize the weighted peak AoI from the point of charging at wireless-powered network edge. Firstly, the problem of optimizing the weighted sum of average peak AoI with a directional charger is formulated, and then transformed to a charging time optimization problem with respect to the charging orientations and peak AoI, and an approximate algorithm is proposed to obtain the required charging time for each source node. Secondly, an age-based scheduling algorithm is proposed to compute the charging decisions and transmission decisions simultaneously, which can not only optimize the weighted sum of average peak AoI, but also guarantee the maximum peak AoI of each source node is bounded. The proposed algorithm is proved to have an approximation ratio of up to (1+$\varphi$), where$\varphi$is a small value related to the weight of each source node. When there exist multiple chargers, an approximate algorithm is also proposed to minimize the weighted sum of average peak AoI by scheduling the orientations of these chargers cooperatively. Finally, the extensive simulations demonstrate the high performance of the proposed algorithms in terms of peak AoI.

Data-driven machine learning techniques have been advocated for signal detection in complex wireless environments. However, when applied to wideband spectrum sensing scenarios, they face practical challenges including very large data dimensionality, insufficient training data, and implicit inter-band dependencies. Current literature focuses on deep convolutional models, whose inherent model structure is not well suited for representing the diverse spectrum occupancy patterns of practical wideband networks, causing inefficient performance-complexity tradeoff and excessive sensing time. To address these issues, this paper develops a novel Spectrum Transformer with multi-task learning for wideband spectrum sensing at high sample efficiency. Empowered by the multi-head self-attention mechanism, the transformer architecture is designed to effectively learn both the inner-band spectral features and the inter-band spectrum occupancy correlations in the wideband regime. Simulations show that the proposed Spectrum Transformer outperforms the existing methods based on convolutional neural networks especially in the small-data case, by achieving higher sensing accuracy with an 89% reduction in model complexity.

The study of generative models is a promising branch of deep learning techniques, which has been successfully applied to different scenarios, such as Artificial Intelligence and the Internet of Things. While in most of the existing works, the generative models are realized as a centralized structure, raising the threats of security and privacy and the overburden of communication costs. Rare efforts have been committed to investigating distributed generative models, especially when the training data comes from multiple heterogeneous sources under realistic IoT settings. In this paper, to handle this challenging problem, we design a federated generative model framework that can learn a powerful generator for the hierarchical IoT systems. Particularly, our generative model framework can solve the problem of distributed data generation on multi-source heterogeneous data in two scenarios, i.e., feature related scenario and label related scenario. In addition, in our federated generative models, we develop a synchronous and an asynchronous updating methods to satisfy different application requirements. Extensive experiments on a simulated dataset and multiple real datasets are conducted to evaluate the data generation performance of our proposed generative models through comparison with the state-of-the-arts.

Machine unlearning is an emerging need that aims to remove the influence of deleted data from a learned model in a timely manner. Thus, unlearning is important for privacy and security in data management. Nevertheless, existing machine unlearning methods fail to perform exactly and efficiently in a federated setting. In this paper, we study the unlearning problem in federated learning, which provides a data deletion mechanism in the federated setting. First of all, a quantized federated learning (Q-FL) algorithm is developed to facilitate exact unlearning. Based on the quantized federated learning system, an exact and efficient federated unlearning (Exact-Fun) algorithm is designed to realize the goal of data deletion. Through theoretic analysis and experimental evaluation, our proposed methods not only have the desired unlearning effectiveness but also achieve high unlearning efficiency compared with the existing works.

3D grey image segmentation has become a promising approach to facilitate practical applications with the help of advanced deep learning models. Although a number of previous works have investigated the vulnerability of deep learning models to backdoor attack, there is no work to study the severe risk of backdoor attack on 3D grey image segmentation. To this end, we propose two backdoor attack methods on 3D grey image segmentation, including Full-control Backdoor Attack (FCBA) and Partial-control Backdoor Attack (PCBA), on 3D grey image segmentation by leveraging a frequency trigger injection function and a rotation-based label corruption function. Our proposed trigger injection function is applied to insert a 3D trigger pattern into the benign 3D grey images in the frequency domain while ensuring the invisibility of the trigger pattern. And the proposed rotation-based label corruption function is employed to yield the crafted labels with the aim of decreasing the performance of segmentation. Finally, through comprehensive experiments on a real-world dataset, we demonstrate the effectiveness of our proposed backdoor models, the frequency trigger injection function, and the rotation-based label corruption function.

By combing edge computing and parallel computing, distributed edge computing has emerged as a new paradigm to accelerate computation at the edge. Considering the parallelism of both computation and communication, the problem of Minimum Latency joint Communication and Computation Scheduling (MLCCS) is studied recently. However, existing works have rigid assumptions that the communication time of each device is fixed and the workload can be split arbitrarily small. Aiming at making the work more practical and general, the MLCCS problem without the above assumptions is studied in this paper. Firstly, the MLCCS problem under a general model is formulated and proved to be NP-hard. Secondly, a pyramid-based computing model is proposed to consider the parallelism of communication and computation jointly, which has an approximation ratio of 1 + δ, where δ is related to devices’ communication rates. An interesting property under such computing model is identified and proved, i.e., the optimal latency can be obtained under arbitrary scheduling order when all the devices share the same communication rate. When the devices own different communication rates, the optimal scheduling order is also obtained. Additionally, when the workload cannot be split arbitrarily, an approximation algorithm with ratio of at most 2 (1 + δ) is proposed. Finally, the theoretical analysis and simulation results verify that the proposed algorithm has high performance in terms of latency. Two testbed experiments are also conducted, which show that the proposed method outperforms the existing methods, reducing the latency by up to 29.2% in real-world applications.

Age of Information, which emerged as a new performance metric to quantify the data freshness, has drawn increasing interests recently. At wireless-powered network edge, the source nodes can only transmit their updates after being charged ready, which means the system AoI is not only determined by the update transmission strategies, but also the charging strategies. However, the existing works either only focused on the point of scheduling of update transmissions or have a rigid assumption that only one source node can be charged per time. Aiming at making the work more practical and general, we investigate the average$A$oI optimization problem at wireless-powered network edge without such limitations in this paper. Firstly, the lower bound of the weighted sum of average$A$oI of the whole network with a directional charger is analyzed, which is proved to be related to nodes' maximum transmitting interval and the charging strategy. An optimal charging time allocation algorithm is proposed to obtain the maximum transmitting interval of each source node by considering the overlapped areas of different charging orientations. After then, an AoI-aware periodical charging scheduling algorithm is proposed, which can obtain a periodical charging schedule including a charging period$T$and the charging orientation assigned to each time slot within$T$, while the average AoI is bounded. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI.

Battery-free wireless sensor network (BF-WSN) (including energy harvesting network and energy rechargeable network) is a new network architecture that has been proposed in recent years to solve the lifetime limitation problem of conventional WSNs. Battery-free sensor nodes can harvest energy from environmental energy resources or from artificial power stations. Thus, the lifetime of a BF-WSN is unlimited in terms of energy. The specific properties of BF-WSNs have brought new challenges in fundamental issues, such as energy management, networking, and data acquisition, which means the existing algorithms in WSNs cannot be adopted directly. The BF-WSN can be regarded as a totally new topic in Internet of Things (IoT) and has attracted much attention from researchers. Many algorithms have been proposed to solve the fundamental problems in BF-WSNs. The objective of this survey is to comprehensively summarize and analyze the existing works. In this survey, we first introduce the existing algorithms from three fundamental aspects, including energy management, networking, and data acquisition. Then, we present some specific applications of BF-WSNs.

As the number of IoT devices increases, sustainability is becoming a bottleneck of the production process in industrial systems. As a matter of fact, inefficient management and scarce resources significantly impeded the development of sustainability. In recent years, it has been observed that the digital twin (DT) technology plays a promising role in facilitating the interaction between the Internet of Things (IoT) assets and digital services. However, high-fidelity models of DTs raise the requirement of efficient data flows, which is limited by realistic constraints, such as data collection strategy and energy supply. We propose a sustainable data collection and management approach to construct DTs for physical assets. With this approach, data packets are uploaded to the data brokers, namely, agents, by a large number of IoT devices. The challenge lies in the balance between enduring data collection and the information loss associated with the stale data. In this article, we aim to optimize the metrics of data fidelity and reveal delay while guaranteeing both sustainable energy and sustainable information. Additionally, a shareable and sustainable blockchain-based DT management architecture is proposed, which does not rely on data exchanges with a single centralized server. Our analytical and simulation results demonstrate the applicability of our proposed architecture.

There is growing attention in the metaverse from a variety of fields, and many Internet of Things (IoT) companies are exploring the possibility of integrating their existing business into the metaverse space. However, it still remains challenging to put the metaverse into practice. Among those challenges, trust management is critical to guarantee secure interactions and effective sharing among metaverse assets. In this article, we construct a TMETA system as a pioneer work for further establishing the metaverse and utilizing digital assets, which helps IoT startup (IS) companies expand their business during the cold start phase with limited business knowledge and seed budget. In the TMETA system, IoT companies treat their interaction experience with resource providers (RPs) as digital assets. When an IS initiates IoT services with the assistance of expertise-diverse RPs, task assignments are determined by leveraging the knowledge and trustworthy advice of expert IoT companies. With the aid of digital twin (DT) and blockchain technology, IoT tasks will be assigned to their suitable RPs as the secured instruction from trust DTs of expert companies, which are evaluated and updated by trust evolution and advice aggregation. We demonstrate our proposed TMETA system’s practicability with synthetic and real-world data. The experimental results indicate that our proposed TMETA system can help ISs attain more favorable outcomes and reduce the task failure rate in various trust environments, thereby accelerating the cold start process.

Recent progress has witnessed the excellent success of neural networks in many emerging applications, such as image recognition, text classification, and speech analysis. In order to achieve secure communication, the utilization of neural networks has been realized yet has not raised sufficient research attention. In addition, the existing neural network-based communication system falls short due to its critical security flaws. In this article, we investigate the security vulnerabilities of the existing neural communication system. Based on our analysis, we design two kinds of attack models, includingtarget man-in-the-middle attackandtarget fraud attack. After that, to improve the security performance of neural communication systems, we develop a new defense mechanism to facilitate two-way secure communication by separating secret key from plaintext and incorporating defensive loss into the training process. Moreover, we show the effectiveness of our proposed neural communication system via theoretical proof. Finally, we implement comprehensive real data experiments to evaluate the performance of our attack and defense methods from the aspects of classification accuracy, communication efficiency and communication qualify, which confirms the advantages of our proposed neural communication system compared with the state-of-the-art.

Thanks to the booming development of artificial intelligence, 5G technology, and intelligent manufacturing technology, numerous intelligent edge devices contained in the industrial Internet of Things (IIoT) are endowed with the ability to mine knowledge from perceived massive data. Knowledge-driven IIoT plays an unprecedented role in application fields such as cyber-physical systems and Industry 4.0. However, knowledge is generally scattered across the distributed edge devices of IIoT. Therefore, in order to further achieve the edge intelligence in IIoT, it is very important to explore an efficient edge knowledge sharing method. In this article, we establish a decentralized knowledge sharing platform in IIoT. First, for public knowledge, a dynamics model that can quantitatively describe its sharing process is established by using the system dynamics theory. Furthermore, a control method for maximizing public knowledge sharing under constraints based on the optimal control theory is presented. Second, for private knowledge, a trusted transaction control method based on blockchain technology is proposed. By developing both smart contract and lightweight consensus mechanism, the efficient peer-to-peer sharing of private knowledge is realized, and the integrity of knowledge and the privacy of participants are protected. The results of extensive experiments show that the proposed method can eliminate the obstacles of knowledge sharing among edge devices in IIoT, and further promote the development of edge intelligence empowered 5G-enabled IIoT applications.

The easy and pervasive involvement of devices in Industrial Internet of Things has greatly benefited the implementation and adoption of various smart services. One prominent prerequisite of such trends is the extensive and continuous support and sharing of data and resources among devices. However, previous efforts usually treat the data sharing as one-time task among devices, which are incapable when the data are applied for the distributed and iterative training task of machine learning models. Therefore, this article proposes a novel framework for continuous data sharing in Industrial Internet of Things. The system consists of different system owners, each brings devices and participate the distributed training of models. Specifically, system owners hold different scales of devices, data, and resources, while devices own heterogeneous availability in different time periods. In this case, the goal is to properly assign devices for qualified model training process in different rounds, such that no devices will devote unlimited resources and the overall efforts and consumptions among different owners are balanced. Accordingly, three algorithms for device allocation are proposed, based on whether the availability of devices in each training round are known at the beginning of the training procedure. The analysis shows that all algorithms can achieve a rational allocation for devices and balance the performance among system owners. Finally, evaluation results reveal that the proposed solutions outperform baseline methods in providing better data sharing plans.

Training machine learning models in a decentralized way has attracted tremendous attention on intelligent Internet of Vehicles (IIoV). However, it is highly dynamic and asymmetric for the connections between vehicles in IIoV due to the mobility of vehicles and the complex communication environment, which poses great challenges on designing efficient distributed learning algorithms. To address this problem, we focus on the basic stochastic gradient descent (SGD) algorithm and propose a decentralized parallel SGD algorithm (DPSGD-WB) for the complex IIoV. The algorithm is based on weight-balancing to overcome the difficulty caused by the dynamic and asymmetric connectivity in IIoV. With rigorous analysis, we show that DPSGD-WB converges on the optimal rate of$O(1/\sqrt {Kn})$, where$n$is the number of vehicle terminals and$K$is the number of iterations. To the best of our knowledge, our proposed algorithm is the first known decentralized parallel SGD algorithm that can be implemented in asymmetric and dynamic intelligent IoV systems. Finally, extensive experiments demonstrate the efficacy of our algorithm.

Analyzing dynamic information networks, which contain evolving objects and links, to meet users various needs has attracted much attention in recent years. For sales companies, recruiting staff who are socialites would help to increase sales volume since such staff often sell more. For universities, employing active collaborators who are productive and well connected to many different scholars over time could bring many benefits to their development. However, no previous work has focused on the discovery of socialites and active collaborators who are worthy of investment in reality. In this paper, we advocate a new concept of attractive individuals to model such special objects. We also introduce the concept of attractive groups to represent groups of well-connected attractive individuals. We analyze the complexity of the attractive individual and group search problems. A time and space efficient algorithm is presented to detect attractive individuals. Furthermore, three algorithms are respectively proposed to find top-k representative attractive groups. Experiments on 6 real-world datasets show high performance of our methods and the significance of attractive individuals and groups in reality.

Searching for$k$-cliques in graphs has been an important problem in graph analysis due to its large number of applications. Previously, finding$k$-cliques in weighted graphs aimed at finding cliques with the largest sum of weight (with no distinction between the edge or the vertex weights), usually called the sum model. However, the algorithms under the sum model may result in solutions consisting of low-weight vertices or edges (outliers). To address this issue, we propose a new model named maximal$(S, C, K)$-clique in weighted graphs and study the problem of maximal ($S, C, K$)-clique search (MCS). We first propose an enumeration-based algorithm MCSE, which checks every$k$-clique to identify the maximal ($S, C, K$)-clique. To improve the efficiency, we further propose two improved algorithms MCSP and MCSC. Instead of checking every possible$k$-clique, MCSP focuses on ($S, C$) values that cannot be dominated and obtains the maximal$(S, C, K)$-cliques directly based on these values. MCSC is devised by further optimizing MCSP based on some key observations on maximal cliques and cliques’ nesting property. We also propose two index structures, BCS-Index and ICS-Index, to achieve optimal query. The former stores all maximal$(S, C, K)$-cliques, while the latter uses the clique's nesting property to reduce the space cost of index construction. Extensive experiments conducted on six real graphs demonstrate the efficiency and effectiveness of our proposed algorithms.

The data collected in Internet of Thing (IoT) systems (IoT data) have stimulated dramatic extension to the boundary of commercialized data statistic analysis, owing to the pervasive availability of low-cost wireless network access and off-the-shelf mobile devices. In such cases, many data consumers post their queries for urban statistic analysis in the system, like the scales of traffics, and then data contributors in IoT networks upload their contents, which can be evaluated by data brokers and responded to data consumers. However, huge volumes of devices bring large scales of data, constituting heavy burdens for data exchange. Even worse, contents in IoT systems are also sensitive as they are usually linked to private physical status of data contributors. The previous studies for IoT data trading fail to provide comprehensive estimation and pricing towards these difficulties. Therefore, this paper proposes a novel framework for the range counting trading over IoT networks by jointly considering data utility, bandwidth consumption, and privacy preservation. The range counting accumulates the number of data items falling in a concerned range of value, providing important information on the underlying data distribution. This paper first proposes a novel sampling-based method with histogram sketching for range counting estimation. The estimator is proved to be unbiased and achieves advanced performance on variance. Then the framework adopts a perturbation mechanism that can further preserve the results under differential privacy. The theoretical analysis shows that the mechanism can guarantee the privacy preservation under a given size of samples and the accuracy requirement of results. Finally, two types of pricing strategies for range counting trading are introduced for different circumstances, providing holistic consideration on how the parameters given in the estimator should be used for data trading. The framework is evaluated by estimating the air pollution levels and the traffic levels with different ranges on the 2014 CityPulse Smart City datasets. The evaluation results demonstrate that our framework can provide more accurate and reliable statistical information, with reduced bandwidth consumption and strengthened privacy preservation.

With the rapid development of the Internet of Things (IoT) and the rapid popularization of 5 G networks, the data that needs to be processed in Mobile Crowdsourcing (MCS) system is increasing every day. Traditional cloud computing can no longer meet the needs of crowdsourcing for real-time data and processing efficiency, thus, edge computing was born. Edge computing can be calculated at the edge of network so that greatly improve the efficiency and real-time performance of data processing. In addition, most of the existing privacy protection technologies are based on the trusted third parties. Therefore, in view of the semi-trustworthiness of edge servers and the transparency of blockchain, this paper proposes a triple real-time trajectory privacy protection mechanism (T-LGEB) based on edge computing and blockchain. Through combining the localized differential privacy and multiple probability extension mechanism, the T-LGEB mechanism is proposed to send the requests and data to the edge server in this paper. Then, through the spatio-temporal dynamic pseudonym mechanism proposed in the paper, the entire trajectory of task participants is divided into multiple unrelated trajectory segments with different pseudonymous identities in order to protect the trajectory privacy of task participants while ensuring high data availability and real-time data. Through a large number of experiments and comparative analysis on multiple real data sets, the proposed T-LGEB has extremely high privacy protection capabilities and data availability, and the resource consumption caused is relatively low.

The Internet of Things (IoT) has been widely deployed to support versatile applications, where an application can be satisfied by functionally compatible and non-functionally satisfiableIoTservices. Considering the fact that the capacities ofIoTdevices may change dynamically, whether or not, and to what extent, certain constraints can be satisfied during their execution, are to be explored. This observation motivates us to formalize the interpretation of qualitative and quantitative satisfaction for prescribed constraints, and thus, to achieveIoTservice monitoring at runtime. Specifically, we formulate the problem ofIoTservice monitoring as a constraint satisfaction problem, where multiple constraints, including spatial-temporal constraints, energy limitation, and capacity restrictions, are considered. Specification-based monitoring is developed based onSignalTemporalLogic (STL), where a novel accumulativerobustnessmetric is proposed, denotedAccumulativeSTL(ASTL), to emphasize the robust satisfaction over the entire time domain. Thereafter,IoTservice monitoring is converted toASTLformulae, and its constraint satisfaction is interpreted with qualitative and quantitative semantics at runtime. Case studies and extensive evaluations are conducted upon publicly-available datasets, where various influential factors are considered. Experimental results show that ourASTLperforms better than the state-of-the-art's techniques with more robust satisfaction.

Age of Information (AoI) is a new metric for measuring the freshness of sensory data in wireless sensor networks. The Battery-Free Wireless Sensor Network (BF-WSN) is proposed to break through the lifetime limitation of battery-powered wireless sensor networks. However, the emerging BF-WSN also brings challenges to the minimization of AoI, on account of its energy characteristics. In this paper, we investigate the AoI minimization data collection scheduling problem for BF-WSNs. The off-the-shelf works for the AoI minimization data collection scheduling problem either focus on simple networks with no more than three nodes or assume that battery-free sensor nodes have specific energy harvesting process, such as Bernoulli process and Poisson process. Different from these works, we first consider the AoI minimization data collection scheduling for one-hop BF-WSNs with multiple battery-free sensor nodes transmitting their sensory data to the sink node, where the energy harvesting processes of battery-free sensor nodes are non-specific. We propose the optimal offline algorithm and the online algorithm for the problem, respectively. The optimality of the offline algorithm and the competitive ratio of the online algorithm are theoretical proved and analyzed. Numerical results are provided to verify the performances of the proposed algorithms.

Multi-access Edge Computing (MEC) is an emerging computing architecture to release the resource burden of the centralized cloud and reduce the mobile application latency. Services management and MEC requests routing is a major problem in MEC systems. Existing works mainly focus on the one-hop centralized request routing strategies. However, the centralized one-hop routing method is not suitable enough since the MEC network is a distributed system, and the number of MEC requests increases dramatically. In this paper, we have proposed an online problem. In such problem, we jointly consider the mobile edge service management and the distributed multi-hop requests routing in an MEC network in which the MEC requests randomly generate. We prove that such problem is NP-Hard even in the off-line scenario. Furthermore, we propose an approximation algorithm to manage the MEC services and two distributed online algorithms to route MEC requests. The approximation ratio and competitive ratio of these algorithms have been analyzed. Experiments are carried out to evaluate the performance of the algorithms and simulation results imply that these algorithms are effective and efficient.

With the advent of intelligent technology, the users of spatio-temporal crowdsourcing and their participation in the crowdsourcing tasks continue to increase exponentially. This poses new challenges to the crowdsourcing field. One of the core research areas of spatio-temporal crowdsourcing is task assignment. Most of the existing research on task assignment is focused on offline optimal task assignment, where, the platform has already learned all the information about workers and tasks beforehand. However, these studies cannot obtain good results in real-world situations. At the same time, online task assignment problems often result in local optimal assignment. To solve these problems, more attention needs to be paid to online task assignments and the arrival time of workers. This paper proposes an Online Bilateral Assignment (OBA) problem based on the online assignment model. The competitive ratio of the Greedy algorithm is analyzed according to the OBA problem model. Also, another solution to the OBA problem according to the Greedy algorithm, the Improved-Baseline algorithm, is proposed. Additionally, a Bilateral Online Priority Reassignment algorithm (BOPR) is proposed. The BOPR algorithm realizes real-time task/worker assignment through the bilateral assignment as a solution for online task assignment. In order to guarantee the number of matching tasks, a priority queue is designed in the BOPR algorithm. Considering the waiting time deadlines of tasks and workers and the error rate for priority ranking, it avoids tasks and workers waiting too long and assigns each task to the best possible extent. On this basis, a two-stage assignment strategy is designed for unsuccessful tasks, which could minimize the error rate of the task and significantly improve the efficiency of task assignment. Finally, through experiments on real data sets, the algorithm's performance in terms of global utility value and the number of matches is evaluated.

Micro-video recommendation is attracting global attention and becoming a popular daily service for people of all ages. Recently, Graph Neural Networks-based micro-video recommendation has displayed performance improvement for many kinds of recommendation tasks. However, the existing works fail to fully consider the characteristics of micro-videos, such as the high timeliness of news nature micro-video recommendation and sequential interactions of frequently changed interests. In this paper, a novel Multi-aggregator Time-warping Heterogeneous Graph Neural Network (MTHGNN) is proposed for personalized news nature micro-video recommendation based on sequential sessions, where characteristics of micro-videos are comprehensively studied, users' preference is mined via multi-aggregator, the temporal and dynamic changes of users' preference are captured, and timeliness is considered. Through the comparison with the state-of-the-arts, the experimental results validate the superiority of our MTHGNN model.

Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination’s perspective. The problem of optimizing AoI has been attracting extensive interests recently. However, existing works mainly focused on scheduling data transmission for AoI optimization. While at wireless-powered network edge, the charging plan of source nodes also requires to be computed in advance, which means the system AoI is determined by not only the data transmission decision but also the charging plan. Thus, in this paper, we investigate the first work to optimize the weighted peak AoI from the point of charging at wireless-powered network edge with a directional charger. Firstly, to minimize the weighted sum of average peak AoI, the AoI minimization problem is transformed to a charging time optimization problem with respect to the overlapped charging areas and average peak AoI, and an approximate algorithm is proposed to obtain the required charging time for each source node. Then, an age-based scheduling algorithm is proposed to compute the charging and data transmission decisions for each source node simultaneously, which can not only optimize the weighted sum of average peak AoI but also guarantee the maximum peak AoI for each source node. The proposed algorithm is proved to have an approximation ratio of up to (1+φ), where φ is a much smaller value related to the weight of each source node. Finally, the simulation results verify the high performance of proposed algorithms in terms of average and maximum peak AoI.

Multi-access Edge Computing is an important technique in the Internet of Things (IoT). It can help people observe the physical world by caching IoT data at an edge server and provide data query services. In this paper, we investigate how to process numerous concurrent top-k queries on an edge server. Since the computation resource of an edge server is limited and costly, processing concurrent top-k queries in the edge is totally different from that in the cloud. Researchers always focus on reducing time/space complexity of processing single top-k query in the cloud. However, how to process numerous top-k queries on an edge server in a cost-efficient manner still remains an open problem. In order to solve the problem, we propose the query recombination concept which aims at using the correlation of queries to reduce resource consumption of query processing. By adopting query recombination, we can make use of a small set of queries to answer the other queries and reduce resource consumption as well. We prove that constructing an optimal query recombination is NP-hard. Three approximate algorithms are proposed accordingly. Simulations are carried out to evaluate the performance of the proposed algorithms further, and the results show that the proposed algorithms are effective and efficient.

Analyzing dynamic information networks, which contain evolving objects and links, has attracted much attention in recent years. For sales companies, recruiting staff who are socialites would help to increase sales volume since such staff often sell more. For universities, employing active collaborators who are productive and well connected to many different scholars over time could bring many benefits to their development. However, no previous work has focused on socialites and active collaborators who are worthy of investment in reality. In this paper, we advocate attractive individuals to model such special objects, and introduce attractive groups to represent groups of well-connected attractive individuals. Then we propose several algorithms to search attractive individuals and groups. Extensive experiments show high performance of our methods and the significance of attractive individuals and groups in reality.

With the service-oriented encapsulation of Internet of Things (IoT) devices, IoT services, which are functionally compatible and non-functionally satisfiable, are composed to support domain applications. The execution of IoT services may last for a relatively long time duration in which their capacities may vary significantly. In this setting, whether or not, and to what extent, certain constraints specified upon certain IoT services can always be satisfied during their execution, are to be explored. This observation motivates us to formalize the interpretation of qualitative and quantitative satisfaction for prescribed constraints, and thus, to conduct IoT service monitoring at runtime. Specifically, the requirement of IoT service monitoring is formulated as a constraint satisfaction problem. Specification-based monitoring is developed leveraging Signal Temporal Logic (STL), where a novel accumulative robustness metric, denoted Accumulative STL (ASTL), is proposed to emphasize the robust satisfaction over the entire time domain. Hence, an ASTL-based mechanism is proposed to support IoT service monitoring, where prescribed constraints are converted to ASTL formulae, and interpreted with qualitative and quantitative semantics at runtime. Case studies and extensive evaluations are conducted upon publicly-available datasets. Experimental results show that ASTL performs better than the state-of-the-art techniques with more robust satisfaction.

A Mobility Digital Twin is an emerging implementation of Digital Twin in the transportation domain, and has been attracting extensive attention from both industry and academia. Although a few research have been conducted on the mobility digital twin, there is no systematic work with an end-to-end digital twin model construction framework. In this paper, we propose an end-to-end system framework, including sensory data collection, offloading, and processing, that aims to facilitate a high-fidelity and real-time digital twin model construction for connected and automated vehicles. Additionally, preliminary experiments are conducted to demonstrate our research motivation and to guide the future system framework design.

Age of Information (AoI), emerged as a new metric to quantify the data freshness, has attracted increasing interests recently. Most existing works try to optimize the system AoI from the point of data transmission. Unfortunately, at wireless-powered network edge, the charging schedule of the source nodes also needs to be decided besides data transmission. Thus, in this paper, we investigate the joint scheduling problem of data transmission and energy replenishment to optimize the peak AoI at network edge with directional chargers. To the best of our knowledge, this is the first work that considers such two problems simultaneously. Firstly, the theoretical bounds of the peak AoI with respect to the charging latency are derived. Secondly, for the minimum peak AoI scheduling problem with a single charger, an optimal scheduling algorithm is proposed to minimize the charging latency, and then a data transmission scheduling strategy is also given to optimize the peak AoI. The proposed algorithm is proved to have a constant approximation ratio of up to 1.5. When there exist multiple chargers, an approximate algorithm is also proposed to minimize the charging latency and peak AoI. Finally, the simulation results verify the high performance of proposed algorithms in terms of AoI.

Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology, blockchain has been broadly used in fintech, smart homes, public health, and intelligent transportation due to its properties of decentralization, collective maintenance, and immutability. Although the dynamism of blockchain abounds in various fields, concerns in terms of network communication interference and privacy leakage are gradually increasing. Because of the lack of reliable attack analysis systems, fully understanding some attacks on the blockchain, such as mining, network communication, smart contract, and privacy theft attacks, has remained challenging. Therefore, in this study, we examine the security and privacy of the blockchain and analyze possible solutions. We systematical classify the blockchain attack techniques into three categories, then discuss the corresponding attack and defense methods based on these categories. We focus on (1) the attack and defense methods of mining pool attacks for blockchain security issues, such as block withholding, 51%, pool hopping, selfish mining, and fork after withholding attacks, in the attack type of consensus excitation; (2) the attack and defense methods of network communication and smart contracts for blockchain security issues, such as distributed denial-of-service, Sybil, eclipse, and reentrancy attacks, in the attack type of middle protocol; and (3) the attack and defense methods of privacy thefts for blockchain privacy issues, such as identity privacy and transaction information attacks, in the attack type of application service. Finally, we discuss future research directions for blockchain security.

IoT devices’ storage and computation capacities are constantly increasing in recent years, which brings critical challenges in data privacy protection. Federated learning (FL) and blockchain technology are two popular techniques used in IoT data aggregation, where FL enables data training with privacy protection, and blockchain provides a decentralized architecture for data storage and mining. However, very few the state-of-the-art works consider the applicability of the combination of FL and blockchain. In this paper, we adopt the federated averaging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance. We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy. Finally, we propose and implement a non-interactive Public Verifiable Secret Sharing (PVSS) algorithm with Distributed Hash Table (DHT) that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users. At last, we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.

Internet of Things (IoT) refers to an ecosystem where applications and services are driven by data collected from devices interacting with each other and the physical world. Although IoT has already brought spectacular benefits to human society, the progress is actually not as fast as expected. From network structures to control flow graphs, IoT naturally generates an unprecedented volume of graph data continuously, which stimulates fertilization and making use of advanced graph-powered methods on the diverse, dynamic, and large-scale graph IoT data.

Internet of Things (IoT) devices are being used widely in the fields of smart city, smart grid, environmental monitoring, Internet of Vehicles and other fields that need large-scale sensing data. However, the research on storage and computation power for IoT is still in its early stages. The game theory converts the interaction between two IoT devices into a game where the conflict is resolved by utilizing the game’s equilibrium conditions. Our goal with the game theory is to maximize the utility for every device in the IoT network. In this article, we review the recent game-theory-based solutions proposed in IoT networks. We summarize game theory concepts and categorize the common game models for ease of understanding for the reader. Later, we focus on analyzing solutions proposed in resource allocation, task scheduling, node selection, quality of service, and network security. Finally, we summarize research challenges and propose future research directions.

The demand of sharing video streaming extremely increases due to the proliferation of Internet of Things (IoT) devices in recent years, and the explosive development of artificial intelligent (AI) detection techniques has made visual privacy protection more urgent and difficult than ever before. Although a number of approaches have been proposed, their essential drawbacks limit the effect of visual privacy protection in real applications. In this article, we propose a cycle vector-quantized variational autoencoder (cycle-VQ-VAE) framework to encode and decode the video with its extracted audio, which takes the advantage of multiple heterogeneous data sources in the video itself to protect individuals’ privacy. In our cycle-VQ-VAE framework, a fusion mechanism is designed to integrate the video and its extracted audio. Particularly, the extracted audio works as the random noise with a nonpatterned distribution, which outperforms the noise that follows a patterned distribution for hiding visual information in the video. Under this framework, we design two models, including the frame-to-frame (F2F) model and video-to-video (V2V) model, to obtain privacy-preserving video streaming. In F2F, the video is processed as a sequence of frames; while, in V2V, the relations between frames are utilized to deal with the video, greatly improving the performance of privacy protection, video compression, and video reconstruction. Moreover, the video streaming is compressed in our encoding process, which can resist side-channel inference attack during video transmission and reduce video transmission time. Through the real-data experiments, we validate the superiority of our models (F2F and V2V) over the existing methods in visual privacy protection, visual quality preservation, and video transmission efficiency. The codes of our model implementation and more experimental results are now available athttps://github.com/ahahnut/cycle-VQ-VAE.

Summary In mobile opportunistic networks, the high mobility of humans and the resource limitation of smart mobile devices pose a great challenge to the design of efficient data dissemination, in which data packets generated from publishers need to be delivered to the subscribers via opportunistic encounters. The current data dissemination schemes generally concentrate on the similarity among nodes while ignoring the similarity between nodes and data packets, which leads to that data packets move back and forth among the nodes with similar social features instead of reaching to the subscribers efficiently. In this paper, an efficient interest‐aware data dissemination approach is proposed in mobile opportunistic networks, in which the similarity between nodes and data packets is used to determine whether a node is a potential destination. Moreover, both the social feature and the residual energy are considered to choose an appropriate relay node, and then determine the number of packet's replicas. The simulation results show that efficient interest‐aware data dissemination provides high efficiency and less transmission overheads compared with the traditional approaches.

The papers in this special section were presented at the 15th International Symposium on Bioinformatics Research and Applications (ISBRA 2019), which was held at Technical University of Catalonia, Barcelona, Spain on June 3-6, 2019.

The papers in this special section were presented at the 16th International Symposium on Bioinformatics Research and Applications (ISBRA 2020), which was held virtually, on December 1-4, 2020. The ISBRA symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of Bioinformatics and computational biology and their applications.

As a new paradigm to solve problems by gathering the intelligence of crowds, mobile crowdsourcing has become one of the hot spots in academic and industrial fields. Task requester, platform, and crowd workers are stakeholders in mobile crowdsourcing, which inevitably leads to conflicts of interest. In order to solve this problem, this article constructs a three-party evolutionary game model among task requester, platform, and crowd workers. This model also considers the collusion between crowd workers and the platform to make it more realistic. Then, the replication dynamics method is utilized to analyze the evolutionary stability strategy. The strategies of rewards and penalties are given to avoid free-riding and false-reporting problems. Finally, the stability of the equilibrium point in the three-party game system is verified through simulation experiments, and the effective methods to motivate each player to choose a trusted strategy are given.

This article studies the core maintenance problem in dynamic graphs. The core number is a fundamental index reflecting the cohesiveness of a graph, which is widely used in large-scale graph analytics. The core maintenance problem requires updating the core numbers of vertices after a set of edges and vertices are inserted into or deleted from the graph. Previous works focus on the scenario of single-edge updates and process the edges one by one when multiple edges are inserted/deleted. We initiate the studies of processing multiple edges concurrently to improve the efficiency of core maintenance. Specifically, we discover a structure of inserted/deleted edges,superior edge set, which can be processed together to greatly reduce unnecessarily repeated visits of vertices in the procedure of sequential edge processing. Based on the structure of the superior edge set, efficient algorithms are then devised for incremental and decremental core maintenance, respectively. Compared with single-edge processing algorithms, our algorithms show a significant speedup in the processing time. Furthermore, our algorithms admit parallel implementations, which can further improve the update efficiency. We also conduct extensive experiments on different types of real-world, temporal, and synthetic data sets, and the results illustrate that the proposed algorithms exhibit good efficiency, stability, and scalability.

Advances in communication technologies and artificial intelligence are accelerating the paradigm of industrial Internet of Things (IIoT). With IIoT enabling continuous integration of sensors and controllers with the network, intelligent analysis of the generated Big Data is a critical requirement. Although IIoT is considered a subset of IoT, it has its own peculiarities in terms of higher levels of safety, security, and low-latency communication in an environment of critical real-time operations. Under these circumstances, discriminative deep learning (DL) algorithms are unsuitable due to their need for large amounts of labeled and balanced training data, uncertainty of inputs, etc. To overcome these issues, researchers have started using deep generative models (DGMs), which combine the flexibility of DL with the inference power of probabilistic modeling. In this article, we review the state of the art of DGMs and their applicability to IIoT, classifying the reviewed works into the IIoT application areas of anomaly detection, trust-boundary protection, network traffic prediction, and platform monitoring. Following an analysis of existing IIoT DGM implementations, we identify challenges (i.e., weak discriminative capability, insufficient interpretability, lack of generalization ability, generated data vulnerability, privacy concern, and data complexity) that need to be investigated in order to accelerate the adoption of DGMs in IIoT and also propose some potential research directions.

Under the needs of processing huge amounts of data, providing high-quality service, and protecting user privacy in artificial intelligence of things (AIoT), federated learning (FL) has been treated as a promising technique to facilitate distributed learning with privacy protection. Although the importance of developing privacy-preserving FL has attracted a lot of attentions, the existing research only focuses on FL with independent identically distributed (i.i.d.) data and lacks study of non-i.i.d. scenario. What is worse, the assumption of i.i.d. data is impractical, reducing the performance of privacy protection in real applications. In this article, we carry out an innovative exploration of privacy protection in FL with non-i.i.d. data. First, a thorough analysis on privacy leakage in FL is conducted with proving the performance upper bound of privacy inference attack. Based on our analysis, a novel algorithm, 2DP-FL, is designed to achieve differential privacy by adding noise during training local models and when distributing global model. Especially, our 2DP-FL algorithm has a flexibility of noise addition to meet various needs and has a convergence upper bound. Finally, the real-data experiments can validate the results of our the oretical analysis and the advantages of 2DP-FL in privacy protection, learning convergence, and model accuracy.

Industrial Internet of Things (IIoT) has attracted increasing attention for improving the efficiency of manufacturing. Plenty of computation-intensive and latency-sensitive applications are required by IIoT networks, which pose significant challenges for the computation capacities of IIoT networks. To address these challenges, Edge-enabled Industrial Internet of Things (E-IIoT) emerges. Edge devices located at the edge of IIoT networks enlarge computation capacities of IIoT networks and improve their efficiency accordingly. How to schedule computation resources wisely is a major problem in E-IIoT networks. Since IIoT devices in an E-IIoT network monitor the industrial site collaboratively, tasks for processing sensory data collected by them are correlated accordingly. That means, scheduling highly correlated tasks to be processed at the same device can improve computation efficiency. Inspired by this fact, we propose a correlation aware scheduling (CAS) algorithm for E-IIoT networks in this article. In specific, computation model decision and processing order decision are made by considering computation resources of devices and correlations among tasks in the algorithm to minimize latency of E-IIoT networks. The NP-hardness of correlation aware latency minimization scheduling problem in E-IIoT networks is first proved. Theoretical analysis on approximation ratio of the CAS algorithm is provided, and simulation results demonstrate the effectiveness of the proposed algorithm in reducing latency.

Vehicle counting is a fundamental component in Intelligent Transportation System (ITS) for city traffic management. Although a number of vehicle counting approaches have been proposed, their essential drawbacks limit the efficacy of vehicle counting in real applications. In this paper, we propose a CityCam-to-Edge cooperative learning framework by cooperating multiple city cameras with an edge server to count vehicles more efficiently. Our learning framework consists of a lightweight feature extraction scheme deployed on the city cameras and a vehicle counting model implemented on the edge server. We devise the lightweight feature extraction scheme by leveraging multiple convolutional layers with few kernels in the design of deep learning architecture to reduce the utilization of parameters for feature extraction, so that the city cameras’ memory consumption and the data transmission time can be greatly reduced. Moreover, we design two novel vehicle counting models, F2F-M and O2O-M, to improve the counting performance by exploiting the temporal correlation among videos captured from multiple city cameras in a frame-to-frame manner and a video-to-video manner, respectively. By combining the lightweight feature extraction scheme and the proposed vehicle counting models, we obtain two end-to-end vehicle counting models, Lite-F2F-M and Lite-O2O-M. Finally, via conducting extensive experiments, we demonstrate that Lite-F2F-M and Lite-O2O-M models outperform the state-of-the-art in terms of vehicle counting accuracy and time efficiency.

Multi-label image recognition has been an indispensable fundamental component for many real computer vision applications. However, a severe threat of privacy leakage in multi-label image recognition has been overlooked by existing studies. To fill this gap, two privacy-preserving models, Privacy-Preserving Multi-label Graph Convolutional Networks (P2-ML-GCN) and Robust P2-ML-GCN (RP2-ML-GCN), are developed in this article, where differential privacy mechanism is implemented on the model’s outputs so as to defend black-box attack and avoid large aggregated noise simultaneously. In particular, a regularization term is exploited in the loss function of RP2-ML-GCN to increase the model prediction accuracy and robustness. After that, a proper differential privacy mechanism is designed with the intention of decreasing the bias of loss function in P2-ML-GCN and increasing prediction accuracy. Besides, we analyze that a bounded global sensitivity can mitigate excessive noise’s side effect and obtain a performance improvement for multi-label image recognition in our models. Theoretical proof shows that our two models can guarantee differential privacy for model’s outputs, weights and input features while preserving model robustness. Finally, comprehensive experiments are conducted to validate the advantages of our proposed models, including the implementation of differential privacy on model’s outputs, the incorporation of regularization term into loss function, and the adoption of bounded global sensitivity for multi-label image recognition.

With advances in wireless power transfer techniques, battery-free wireless sensor networks (BF-WSNs) which can support long-term applications, has been attracting increasing interests in recent years. Unfortunately, the problem of minimum latency aggregation scheduling (MLAS) is not well studied in BF-WSNs. Existing works always have a rigid assumption that there is only one single query which is targeted at the whole network. Aiming at making the work more practical and general, we investigate the general MLAS problem in BF-WSNs, which is targeted at any subset of nodes in the network and aimed for an arbitrary number of aggregation queries. First, the general MLAS problem when there is one single query is studied. To control the number of nodes participating in the aggregation process, a node selection algorithm is proposed to cover and connect the whole target nodes. Then, a latency and energy aware scheduling algorithm is proposed to integrate the construction of aggregation tree with the chosen nodes, and the computation of a conflict-free schedule simultaneously, relying on non-predetermined structures. Second, the general MLAS problem when there is a group of aggregation queries is studied. Through designing some special structures to avoid collisions between both current and existing aggregation schedules, an algorithm without any waiting time is proposed. Additionally, the algorithm under physical interference model and dynamic energy arrival model are also presented. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency and energy efficiency.

Broadcasting is an essential operation in wireless networks for disseminating the message from the source node to all other nodes. Unfortunately, the problem of Minimum Latency Broadcast Scheduling (MLBS) in duty-cycled wireless sensor networks is not well studied. In existing works, the construction of broadcast tree and the scheduling of transmissions are conducted separately, where a tree-based structure is used as the input of the scheduling algorithm. Relying on a pre-determined tree may result in a much large latency even using the optimal scheduling method. Thus, the MLBS problem in duty-cycled WSNs without the above limitation is investigated in this paper. First, to avoid relying on a pre-determined structure, a two-step scheduling algorithm is proposed to construct the broadcast tree and compute a collision-free schedule simultaneously. To the best of our knowledge, this is the first work that can integrate these two kinds of operations together. Second, a novel transmission mode, i.e., concurrent broadcasting, is first introduced for wireless networks and several techniques are designed to further improve the broadcast latency. Third, the multiple messages broadcasting and all-to-all broadcasting algorithms, which can generate a series of broadcast schedules independently without a pre-determined tree, are also proposed by taking care of the collisions in both the current and the previous broadcast schedules. Finally, the theoretical analysis and experimental results demonstrate the efficiency of the proposed algorithms in terms of latency.

Battery-free wireless sensor network (BF-WSN) is a newly proposed network architecture to address the limitation of traditional wireless sensor networks (WSNs). The special features of BF-WSNs make the coverage problem quite different and even more challenging from and than that in traditional WSNs. This paper defines a new coverage problem in BF-WSNs which aims at maximizing coverage quality rather than prolonging network lifetime. The newly defined coverage problem is proved to be at least NP-Hard. Two sufficient conditions, under which the optimal solution of the problem can be derived in polynomial time, are given in this paper. Furthermore, three approximate algorithms are proposed to derive nearly optimal coverage when the sufficient conditions are unsatisfied. The time complexity and approximate ratio of the three algorithms are analyzed. Extensive simulations are carried out to examine the performance of the proposed algorithms. The simulation results show that these algorithms are efficient and effective.

More and more applications are promoting cus-tomized or personalized services. In order for these applications to provide meaningful output, it collects users’ personal information over time. Some personal information (e.g. education level or income level) can only be captured by users actively updating their profile to reflect these changes. We refer to these as long-term time series data, as they do not change frequently. If applications can keep up to date on a diverse and large set of personal features, they can provide higher quality service. However, this quality of service comes at the cost of the user sacrificing their privacy. There has been numerous research on protecting privacy of time series data for context aware services, but the privacy leakage of personal information updates during the whole life-cycle of the series has received only scant attention.Motivated users concerned about their privacy, we discuss in detail the privacy leakage risk, focusing on long-term time-series data from the perspective of game theory. Then, we propose a reward-privacy model, targeting the privacy-aware data-updates for the entire life-cycle in context-aware services by leveraging a three-party Stackelberg game. We theoretically prove that a Nash Equilibrium exists in the proposed model, and then use simulations to validate that a Nash Equilibrium exists for different parameters of the productivity function. By using our proposed framework, users have guidance to decide not only the timing of submitting personal updates, but also the granularity or obscurity level for their data.

In this paper, we study exact hypercore maintenance in large-scale dynamic hypergraphs. A hypergraph, whose hyperedges may contain a set of vertices rather than two vertices in pairwise graphs, can represent complex interactions in more sophisticated applications. However, the exponential number of hyperedges incurs unaffordable costs to recompute the hypercore number of vertices and hyperedges when updating a hypergraph. This motivates us to propose an efficient approach for exact hypercore maintenance with the intention of significantly reducing the hypercore updating time comparing with recomputation approaches. The proposed algorithms can pinpoint the vertices and hyperedges whose hypercore numbers have to be updated by only traversing a small sub-hypergraph. Extensive experiments on real-world and temporal hypergraphs demonstrate the superiority of our algorithms in terms of efficiency.

Internet of Thing (IoT) systems provide novel opportunities for data acquisition, where sensing devices can flexibly collect and trade data with data brokers. A data broker may conduct sophisticated analysis on the collected data and further exchange statistics like histograms with data requestors. Considering the supply-demand correlations, several pivotal factors must be jointly treated including communication bandwidths, data utilities, privacy issues, total budget, etc. Unfortunately, the current efforts mainly apply the crowdsensing strategies during data trading and overlook the subsequent processing and analysis of the collected data. Therefore, this paper proposes a novel framework for efficient data trading in IoT systems throughout the data collection and data processing phases. In the framework, data contributors can flexibly arrive and departure from the monitored area in heterogeneous time slots. The incentives for data trading are correlated with data volume, channel condition, and privacy issues of each contributor. Meanwhile, a data broker samples partial sensing data and aggregates approximate histograms for data requestors. The objective is to minimize the total budget for data trading. First, the theoretical bound on the necessary budget for histograms with a given accuracy is proved. Then two algorithms are proposed for efficient data trading among data contributors, based on whether the behaviors of data contributors are known in advance. Both algorithms are analyzed and the corresponding guarantees on performance are discussed. Finally, the extensive evaluation results validate the advancement of the proposed algorithms.

The increasing of group polarization on social media seriously impacts on the health of public discourse and information dissemination. At present, detecting polarized structures in signed networks is well-motivated for studying the group polarization on social media. However, most studies restricted the number of polarized structures to only two, while neglecting the real-world scenario where signed networks consist of multiple polarized structures, that is an unreasonable assumption. To conquer the limitations of the existing work, in this paper, we present a novel cohesive subgraph model based on structural clusterable theory, named maximal multipolarized clique (MMC), which can be partitioned into k polarized subcliques such that the edges in subcliques are positive and the edges between subcliques are negative. This paper formulates the problem of Maximal Multipolarized Cliques Search (MMCS) in signed networks which is proved to be NP-hard. To address this problem, we first devise powerful pruning rules to reduce the signed network significantly and further develop an efficient algorithm to search all maximal multipolarized cliques in the reduced signed network. The experimental results on real-world signed networks demonstrate the efficiency and effectiveness of our algorithm.

With the rise of the Internet of Things (IoT) and fifth-generation (5G) networks, which have led to a surge in data processing and increased data transfer time, traditional cloud computing could no longer meet the needs of workers, so edge computing has emerged. Edge computing could meet the demand for low time consumption by processing data at the edge of the network and then transmitting it to a third-party platform. However, since the credibility of the third-party platform is unknown which can easily leak the privacy of workers. For the transparent mechanism of blockchain, a two-stage privacy protection mechanism based on blockchain is proposed to solve this problem. In the first stage, this paper proposes a double disturbance localized differential privacy (DDLDP) algorithm to disturb the location information of workers. In the second stage, all the sensing data are uploaded to the blockchain through edge nodes, processed by the edge cloud, and fed back to the requester. Blockchain technology not only guarantees the integrity of sensing data, but also prevents the possibility of third-party platforms from leaking workers' privacy. Through extensive performance evaluation and comparative experiments on real data sets, the DDLDP algorithm could effectively protect the privacy of workers and has higher service quality and data availability.

The massive amount of data generated by the Internet-of-Things (IoT) devices places enormous pressure on sensory data query processing. Due to the limitations of computation and data transmission capabilities in traditional wireless sensor networks (WSNs), the current query processing methods are no longer effective. Furthermore, processing vast amount of sensory data also overloads the cloud. To address these problems, we investigate query processing in an edge-assisted IoT data monitoring system (EDMS). Multiaccess edge computing (MEC) is an emerging topic in IoTs. Unlike WSNs, the edge servers in an EDMS can deploy the computation and storage resources to nearby IoT devices and offer data processing services. Therefore, queries toward massive sensory data can be processed in an EDMS in a distributed manner and the edge servers can handle the sensory data in a distributed manner, reducing the workload of the cloud. In this article, we define a query processing problem in an EDMS, which aims to derive a distributed query plan with the minimum query response latency. We prove that this problem is NP-Hard and propose a corresponding approximation algorithm. The performance of the proposed algorithm is bounded. Furthermore, we evaluate the performance of the proposed algorithm through extensive simulations.

Recently, the surge in popularity of the Internet of Things (IoT), mobile devices, social media, etc., has opened up a large source for graph data. Graph embedding has been proved extremely useful to learn low-dimensional feature representations from graph-structured data. These feature representations can be used for a variety of prediction tasks from node classification to link prediction. However, the existing graph embedding methods do not consider users' privacy to prevent inference attacks. That is, adversaries can infer users' sensitive information by analyzing node representations learned from graph embedding algorithms. In this article, we propose adversarial privacy graph embedding (APGE), a graph adversarial training framework that integrates the disentangling and purging mechanisms to remove users' private information from learned node representations. The proposed method preserves the structural information and utility attributes of a graph while concealing users' private attributes from inference attacks. Extensive experiments on real-world graph data sets demonstrate the superior performance of APGE compared to the state-of-the-arts. Our source code can be found at https://github.com/KaiyangLi1992/Privacy-Preserving-Social-Network-Embedding.

The ubiquity of devices in Internet of Things (IoT) has opened up a large source for IoT data. Machine learning (ML) models with big IoT data is beneficial to our daily life in monitoring air condition, pollution, climate change, etc. However, centralized conventional ML models rely on all clients' data at a central server, which seriously threatens user privacy. Federated learning (FL) emerges as a promising solution aiming to protect user privacy by enabling model training on a large corpus of decentralized data. The recent studies indicate FL suffers from the heterogeneity issue as it treats all clients' data equally, that is, FL might sacrifice the performance of the majority of clients to accommodate the performance of the minority of clients with low usability data. In order to overcome this issue, a reinforcement learning (RL)-based intelligent central server with the capability of recognizing heterogeneity is implemented, which can help lead the trend toward better performance for majority of clients. To be specific, an FL central server analyses the benefits of different collaboration by capturing the intricate patterns in heterogeneous clients based on rating feedback and then updates clients' weights iteratively, until it establishes a coalition of clients with quasioptimal performance. The experimental results on three real data sets under various heterogeneity levels demonstrate the superior performance of the proposed solution.

Computer vision and deep neural networks have been significantly promoting the development of visual perception in these years. Particularly, for autonomous vehicles, real-time image/video data is captured by onboard cameras and analyzed by computer vision techniques in many real applications. In the captured camera data, some contents can be used as auxiliary information to infer individuals' locations and trajectories, which leads to severe privacy leakage but has been rarely studied. Thus, the goal of this article is to protect individuals' location privacy by hiding side-channel information in the captured data while preserving the data utility for downstream applications. To this end, the technology of generative adversarial networks (GAN) is utilized to design two novel models, named ADGAN-I and ADGAN-II, both of which can take the original camera data as inputs and generate privacy-preserving outputs according to predefined sensitive object class. Thus, the processed camera data can defend location inference attack from adversaries in offline applications. Moreover, in ADGAN-I and ADGAN-II, the tradeoff between location privacy and data utility can be effectively balanced. Finally, the results of extensive real-data experiments validate the superiority of our proposed models over the state of the arts in utility preservation and privacy protection for autonomous vehicles' images and videos.

To promote development of Mobile Crowdsensing Systems (MCSs), numerous auction schemes have been proposed to motivate mobile users' participation. But, task diversity of MCSs has not been fully explored by most existing works. To further exploit task diversity and improve performance of MCSs, in this paper, we investigate the joint problem of sensing task assignment and schedule with considering multi-dimensional task diversity, including partial fulfillment, bilaterally-multi-schedule, attribute diversity, and price diversity. First, task owner-centric auction model is formulated and two distributed auction schemes (CPAS and TPAS) are proposed such that each task owner can locally process auction procedure. Then, mobile user-centric auction model is established and two distributed auction schemes (VPAS and DPAS) are developed to facilitate local auction implementation. These four auction schemes differ in their approaches to determine winners and compute payments. We further rigorously prove that all the four auction schemes (CPAS, TPAS, VPAS, and DPAS) are computationally-efficient, individually-rational, and incentive-compatible and that both CPAS and TPAS are budget-feasible. Finally, we comprehensively evaluate the effectiveness of CPAS, TPAS, VPAS, and DPAS via comparing with the state-of-the-art in real-data experiments.

Data aggregation is a fundamental yet popular operation in wireless networks where the sink needs to obtain the combined information of the whole network. However, the problem of minimum latency aggregation scheduling (MLAS) is not well studied in cognitive radio networks. Few studies have addressed this issue and most previous aggregation methods all assume that a fixed-structure based aggregation tree is constructed in advance, which may result in the selection of a node with limited spectrum opportunities as the parent by many nodes and by extension results in a large latency. Thus, the MLAS problem in cognitive radio networks (MLAS-CR) without the above limitation is investigated in this paper. First, the MLAS-CR problem with primary social behaviors where the activity of primary users can be predicted is studied. To make full use of the limited spectrum opportunities, we integrate the construction of the aggregation tree, and the computation of a conflict-free schedule simultaneously, without any predetermined structures. Second, the MLAS-CR problem without the above assumption is also investigated. To reduce the latency, a two-way aggregation scheduling method is proposed to adaptively choose the parent with only current channel information. To further reduce the latency, we also introduce a new data aggregation mode for CRN, i.e., Data Aggregation Scheduling in The Dark, to utilize the spectrum opportunities of scheduled nodes. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

The emerging energy-harvesting technology enables charging sensor batteries with renewable energy sources, which has been effectively integrated into Wireless Sensor Networks (EH-WSNs). Due to the limited energy-harvesting capacities of tiny sensors, the captured energy remains scarce and differs greatly among nodes, which makes the data aggregation scheduling problem more challenging than that in energy-abundant WSNs. In this article, we investigate the Minimum Latency Aggregation Scheduling (MLAS) problem in EH-WSNs. First, we identify a new kind of collision in EH-WSNs, named as energy-collision, and design several special structures to avoid it during data aggregation. To reduce the latency, we try to choose the parent adaptively according to nodes’ transmission tasks and energy-harvesting ability, under the consideration of collisions avoidance. By considering transmitting time, residual energy, and energy-collision, three scheduling algorithms are proposed under protocol interference model. Under physical interference model, several approximate algorithms are also designed by taking account of the interference from the nodes several hops away. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

The energy limitation of wireless sensors limits the lifetime of the traditional wireless sensor networks. The Battery-Free Sensor Network (BF-WSN) is a new network architecture proposed in recent years to address the limitation of wireless sensor networks. In a BF-WSN, the battery-free node can harvest energy from the ambient environment, and thus the lifetime of a BF-WSN is unlimited in terms of energy. The coverage quality is an important measurement of BF-WSNs. Considering the specific features of BF-WSNs, we propose a new deployment concept for BF-WSNs, named Joint Deployment. It aims to determine the locations and working schedules of sensor nodes to maximize network coverage quality. Based on the joint deployment concept, we propose a new deployment problem of battery-free sensor nodes. We prove that this problem is at least NP-Hard. We also analyze the upper bound of this problem. Furthermore, we propose an approximated algorithm to solve this problem and analyze the time complexity and the ratio bound of the algorithm. Extensive simulations are carried out to examine the performance of the proposed algorithm. The simulation results show that the algorithm is efficient and effective.

Battery-Free Wireless Sensor Networks (BF-WSNs) have been attracting increasing interests in the recent years. To reduce the latency in BF-WSNs, the Minimum Latency Aggregation Scheduling (MLAS) problem with coverage requirement q is proposed recently, which tries to choose q percent of nodes for communication and aggregation. In the existing method, the authors try to select nodes adaptively according to their energy status and schedule these nodes to achieve the minimum latency. Unfortunately, it cannot guarantee the distribution of the aggregated nodes and may result in these nodes being squeezed in a small area and a poor aggregation quality. Thus, we re-investigate the q-coverage MLAS problem in this article, which can guarantee that the aggregated nodes are distributed evenly. Firstly, the 1-coverage MLAS problem, in which each node can be covered by at least one aggregated node, is studied. To reduce the latency, we intertwine the selection of aggregated nodes and the computation of a collision-free communication schedule simultaneously. Two algorithms are proposed by scheduling the communication tasks in the bottom-up and top-down manner respectively. Secondly, to satisfy the arbitrary coverage requirement q, three algorithms are proposed to guarantee the aggregated nodes are evenly distributed in the network with a low latency. Additionally, the method to extend the proposed algorithms for the BF-WSNs with multiple channels is also studied. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Recently, the concept of a decentralized data marketplace is getting much attention to exchange user data. Multi‐authority attribute‐based encryption (ABE), which can provide flexibility and user‐centric access control, is previously widely used in decentralized data sharing applications and also becoming a foundation to build decentralized data trading applications. It is known that users in a multi‐authority ABE system can collude by sharing their secret information for malicious purposes. To address this issue, the collusion‐resistant multi‐authority ABE model was introduced in which a unique global identifier (GID) is issued by the central authority (CA) to each user. Unfortunately, such approach cannot be used directly to build a decentralized data marketplace as (a) such intervention of the CA is directly against the main motivation of the decentralized trading platform and, mostly importantly, (b) the CA can exploit its full knowledge on users’ GID to launch various attacks against users. Motivated by these observations, this paper introduces a novel user collusion‐resistant decentralized multi‐authority ABE scheme for privacy preserving data trading systems. In the existing multi‐authority ABE systems, users utilize his/her GID that is solely assigned by the CA to generate his/her secret keys throughout the collaboration with authorities and a user can compute multi‐authority keys by combining the secret keys (stem from the same GID) in various ways. In the proposed system, the CA only has a partial knowledge of users’ GIDs, and thus, users’ privacy can be protected. On the other hand, we set the user’s own partial GID as a secret which can be used to withdraw his/her deposit to discourage any possible collusion among users.

In recent years, a series of researches have revealed that the Deep Neural Network (DNN) is vulnerable to adversarial attack, and a number of attack methods have been proposed. Among those methods, an extremely sly type of attack named the one‐pixel attack can mislead DNNs to misclassify an image via only modifying one pixel of the image, leading to severe security threats to DNN‐based information systems. Currently, no method can really detect the one‐pixel attack, for which the blank will be filled by this paper. This paper proposes two detection methods, including trigger detection and candidate detection. The trigger detection method analyzes the vulnerability of DNN models and gives the most suspected pixel that is modified by the one‐pixel attack. The candidate detection method identifies a set of most suspected pixels using a differential evolution‐based heuristic algorithm. The real‐data experiments show that the trigger detection method has a detection success rate of 9.1%, and the candidate detection method achieves a detection success rate of 30.1%, which can validate the effectiveness of our methods.

The huge amount of graph data are published and shared for research and business purposes, which brings great benefit for our society. However, user privacy is badly undermined even though user identity can be anonymized. Graph de-anonymization to identify nodes from an anonymized graph is widely adopted to evaluate users' privacy risks. Most existing de-anonymization methods which are heavily reliant on side information (e.g., seeds, user profiles, community labels) are unrealistic due to the difficulty of collecting this side information. A few graph de-anonymization methods only using structural information, called seed-free methods, have been proposed recently, which mainly take advantage of the local and manual features of nodes while overlooking the global structural information of the graph for de-anonymization.

Broadcasting is a fundamental networking service where the source node tries to disseminate the message to the whole network. The problem of Minimum Latency Broadcast Scheduling (MLBS) which seeks a fast and collision-free broad-cast schedule has been well studied when nodes are energy-abundant. However, in battery-free wireless networks, node can only receive or transmit packets after it has harvested enough energy. In such networks, it is of great importance to exploit the harvested energy smartly to reduce broadcast latency. Un-fortunately, the existing works rely on predetermined structures may greatly increase the latency by choosing a node with large charging latency as the backbone node. In addition, they assume each node can only transmit once which may result in much waiting latency. To address the above issues, we investigate the MLBS problem in battery-free wireless networks without predetermined structures in this paper. Firstly, to make use of the harvested energy smartly, we intertwine the construction of broadcast tree and the computation of an energy-satisfied and collision-free schedule. Secondly, a Delayed Broadcasting technique is proposed for each node to tradeoff between the number of transmissions and its waiting latency. By considering residual energy and transmitting time, two latency and energy aware scheduling algorithms are proposed, in which the broadcast tree can be constructed adaptively according to nodes' energy status. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of broadcast latency.

In a Device-to-Device (D2D) mobile edge computing system, the mobile devices can share computation and communication resources with each other. A mobile device can offload its computation task to a nearby mobile device. In order to release the computation workload of mobile edge servers and enhance the mobile edge service coverage quality, a set of mobile devices can be selected as sub-servers and provide mobile edge service to nearby devices. Based on the crowdsourcing technique, we have proposed a framework, CROSS, to select sub-servers in a D2D enhanced mobile edge computing system. Two major problems in CROSS framework, the sub-server selection problem and the payoff allocation problem, have been formulated. The first problem is proved to be NP-Hard and solved by an approximation algorithm. The second problem is solved by an auction mechanism. The performance of the CROSS framework is evaluated by experiments. The experimental results show that the CROSS framework is efficient and effective.

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are envisioned as two promising techniques to satisfy the increasing energy and computation requirements of latency-sensitive and computation-intensive applications installed on mobile devices. The integration of MEC and WPT introduces a novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC). In WP-MEC networks, edge devices located at the edge of radio access networks, such as access points and base stations, transmit radio frequency signals to power mobile devices and mobile devices can offload their intensive computation workloads to edge devices. In this paper, we study the Computation Completion Ratio Maximization Scheduling problem for WP-MEC networks with multiple edge devices, which is proved to be NP-hard. We jointly optimize the WPT time allocation and computation scheduling for mobile devices in a WP-MEC network to maximize the computation completion ratio of the WP-MEC network and propose approximation algorithms. The approximation ratio and computation complexity of the proposed algorithms are theoretically analyzed. Extensive simulations are conducted to verify the performance of the proposed algorithms.

The collection of high-confidence data has been one prominent step for many services in smart city systems. However, the privacy issues have been thwarting the seamless publication of data, especially as the data from different aspects of daily life may provide unprecedented coverage of contributors. Current solutions have been carefully designed to perturb or suppress the data before publication, so as to balance the privacy and utilities. However, they cannot fit the practice in smart cities, where multiple service providers request information on heterogeneous domains and regions of the city. Therefore, this article proposes a novel framework for data publication of workers in smart city systems. The framework allows workers and requestors to own and request various types of contents in different regions. The objective is to maximize the number of service providers receiving qualified utilities under privacy constraints. Furthermore, differential privacy is applied to guarantee that workers will not disclose personal information to requestors. In the technical part, the problem is proved to be NP-complete. Then two algorithms and strategies are proposed toward different cases: 1) workers apply identical privacy budgets for all published data and 2) workers are flexible on privacy settings. Both algorithms are theoretically analyzed on their performance of the released results. Finally, the evaluation of data sets of local businesses reveals that proposed algorithms can outperform baseline methods.

The effective physical data sharing has been facilitating the functionality of Industrial IoTs, which is believed to be one primary basis for Industry 4.0. These physical data, while providing pivotal information for multiple components of a production system, also bring in severe privacy issues for both workers and manufacturers, thus aggravating the challenges for data sharing. Current designs tend to simplify the behaviors of participants for better theoretical analysis, and they cannot properly handle the challenges in IIoTs where the behaviors are more complicated and correlated. Therefore, this paper proposes a privacy-preserved data sharing framework for IIoTs, where multiple competing data consumers exist in different stages of the system. The framework allows data contributors to share their contents upon requests. The uploaded contents will be perturbed to preserve the sensitive status of contributors. The differential privacy is adopted in the perturbation to guarantee the privacy preservation. Then the data collector will process and relay contents with subsequent data consumers. This data collector will gain both its own data utility and extra profits in data relay. Two algorithms are proposed for data sharing in different scenarios, based on whether the service provider will further process the contents to retain its exclusive utility. This work also provides for both algorithms a comprehensive consideration on privacy, data utility, bandwidth efficiency, payment, and rationality for data sharing. Finally, the evaluation on real-world datasets demonstrates the effectiveness of proposed methods, together with clues for data sharing towards Industry 4.0.

In spatiotemporal crowdsourcing applications, sensing data uploaded by participants usually contain spatiotemporal sensitive data. If application servers publish the unprocessed sensing data directly, it is easy to expose the privacy of participants. In addition, application servers usually adopt the static publishing mechanism, which is easy to produce problems such as poor timeliness and large information loss for spatiotemporal crowdsourcing applications. Therefore, this paper proposes a spatiotemporal privacy protection (STPP) method based on dynamic clustering methods to solve the privacy protection problem for crowd participants in spatiotemporal crowdsourcing systems. Firstly, the working principles of a dynamic privacy protection mechanism are introduced. Then, based on k-anonymity and l-diversity, the spatiotemporal sensitive data are anonymized. In addition, this paper designs the dynamic k-anonymity algorithm based on the previous anonymous results. Through extensive performance evaluation on real-world data, compared with existing methods, the proposed STPP algorithm could effectively solve the problem of poor timeliness and improve the privacy protection level while reducing the information loss of sensing data.

Cloud computing offers a cost-effective and elastic computing paradigm that facilitates large scale data storage and analytics. By deploying virtualization technologies in the datacenter, cloud enables efficient resource management and isolation for various big data applications. Since the hotspots (i.e., overloaded machines) can degrade the performance of these applications, virtual machine migration has been utilized to perform load balancing in the datacenters to eliminate hotspots and guarantee Service Level Agreements (SLAs). However, the previous load balancing schemes make migration decisions based on deterministic resource demand estimation and workload characterization, without considering their stochastic properties. By studying real world traces, we show that the resource demand and workload of virtual machines are highly dynamic and bursty, which can cause these schemes to make inefficient migrations for load balancing. To address this problem, in this paper we propose a stochastic load balancing scheme which aims to provide probabilistic guarantee against the resource overloading with virtual machine migration, while minimizing the total migration overhead. Our scheme effectively addresses the prediction of the distribution of resource demand and the multidimensional resource requirements with stochastic characterization. Moreover, as opposed to the previous works that measure the migration cost without considering the network topology, our scheme explicitly takes into account the distance between the source physical machine and the destination physical machine for a virtual machine migration. The trace-driven experiments show that our scheme outperforms the previous schemes in terms of SLA violation and the migration cost.

Resilience problem is defined on a database d, given a boolean query q where q(d) is initially true, and an integer k, it is to find the tuple set d′ of smallest size such that the query result q(d∖d′) becomes false. As a potential explanation of a specified query, resilience plays a fundamental and important role in query explanation, database debugging and error tracing. Complexity results of resilience decision on relational algebraic queries have been studied previously. In this paper, we investigate the resilience decision problem on aggregation queries. New results on the hardness are provided. We show that, this problem is polynomially intractable on nested COUNT and SUM query both under data complexity and parametric complexity, and even it is NP-hard to approximate it within a constant ratio under the active domain constraint.

This article studies the batch processing of truss maintenance in large graphs. Trussness is a widely used index in graph analytics for cohesive subgraph mining. It is defined on edges to reflect the closeness of vertices connected by the edges. The trussness maintenance problem, i.e., updating trussness after edge insertions/deletions and avoiding recomputation, was proposed by Cohen (2008) with the assumption that real graphs are continuously evolving in nature and their dynamicity usually only affects few edges. Different from the existing work that mainly focuses on simple single edge/vertex insertion/deletion, we propose batch processing algorithms for truss maintenance with multiedge insertions/deletions. By presenting an edge structure called triangle disjoint set, whose insertion/deletion can make each edge change its trussness by at most 1, we tackle the difficulty in quantifying the trussness changes of the edges in batch processing. More specifically, we propose two indices, namely sustain support and pivotal support, to help measure whether the edges have the potential to change their trussness, such that the search range of the potential edges is greatly reduced. The extensive experiments on real-world graphs illustrate that compared with the single-edge processing approach, our batch processing algorithms can significantly improve the processing efficiency, and the improvement becomes more obvious when more edges are inserted/deleted.

With the rapid development of Industry 5.0 and mobile devices, the research of mobile crowdsensing networks has become an important research focus. Task allocation is an important research content that can inspire crowd workers to participate in crowd tasks and provide truthful sensed data in mobile crowdsourcing systems. However, how to inspire crowd workers to participate in crowd tasks and provide truthful sensed data still has many challenges. In this article, based on the Markov model and collaborative filtering model, the similarities, trajectory prediction, dwell time, and trust degree are considered to propose the Markov and Collaborative filtering-based Task Recommendation (MCTR) model. Then, based on the Walrasian equilibrium, the optimum solution is researched to maximize the social welfare of mobile crowdsourcing systems. Finally, the comparison experiments are carried out to evaluate the performance of the proposed multiobjective optimization and the Markov-based task allocation with other methods. Through comparison experiments, the efficiency and adaptation of mobile crowdsourcing systems could be improved by the proposed task allocation.

Enabled by the industrial Internet, intelligent transportation has made remarkable achievements such as autonomous vehicles by carnegie mellon university (CMU) Navlab, Google Cars, Tesla, etc. Autonomous vehicles benefit, in various aspects, from the cooperation of the industrial Internet and cyber-physical systems. In this process, users in autonomous vehicles submit query contents, such as service interests or user locations, to service providers. However, privacy concerns arise since the query contents are exposed when the users are enjoying the services queried. Existing works on privacy preservation of query contents rely on location perturbation or k-anonymity, and they suffer from insufficient protection of privacy or low query utility incurred by processing multiple queries for a single query content. To achieve sufficient privacy preservation and satisfactory query utility for autonomous vehicles querying services in cyber-physical systems, this article proposes a novel privacy notion of client-based personalized k-anonymity (CPkA). To measure the performance of CPkA, we present a privacy metric and a utility metric, based on which, we formulate two problems to achieve the optimal CPkA in term of privacy and utility. An approach, including two modules, to establish mechanisms which achieve the optimal CPkA is presented. The first module is to build in-group mechanisms for achieving the optimal privacy within each content group. The second module includes linear programming-based methods to compute the optimal grouping strategies. The in-group mechanisms and the grouping strategies are combined to establish optimal CPkA mechanisms, which achieve the optimal privacy or the optimal utility. We employ real-life datasets and synthetic prior distributions to evaluate the CPkA mechanisms established by our approach. The evaluation results illustrate the effectiveness and efficiency of the established mechanisms.

Blockchain, a promising decentralized para-digm, can be exploited not only to overcome the shortcomings of the traditional crowdsourcing systems, but also to bring technical innovations, such as decentralization and accountability. Nevertheless, some critical inherent limitations of blockchain have been rarely addressed in the literature when it is incorporated into crowdsourcing, which may yield the performance bottleneck in the crowdsourcing systems. To further leverage the superiority of combining blockchain and crowdsourcing, in this article, we propose an innovative hybrid blockchain crowdsourcing platform, named zkCrowd. Our zkCrowd integrates with a hybrid blockchain structure, smart contract, dual ledgers, and dual consensus protocols to secure communications, verify transactions, and preserve privacy. Both the theoretical analysis and experiments are performed to evaluate the advantages of zkCrowd over the state of the art.

The skyline query is important in the database community. In recent years, the researches on incomplete data have been increasingly considered, especially for the skyline query. However, the existing skyline definition on incomplete data cannot provide users with valuable references. In this paper, we propose a novel skyline definition utilizing probabilistic model on incomplete data where each point has a probability to be in the skyline. In particular, it returns K points with the highest skyline probabilities. In addition, we propose incomplete models and estimate probability density functions of missing values on independent, correlated, and anti-correlated distributions, respectively. Meanwhile, it is a big challenge to compute probabilistic skyline on incomplete data. We propose three efficient algorithms SPISkyline, SPCSkyline, and SPASkyline for probabilistic skyline computation on incomplete data complying with independent, correlated, and anti-correlated distributions, respectively. They employ pruning strategy, optimization of the process of probability computation, and sorting technique to improve the efficiency of probabilistic skyline computation on incomplete data. Our experimental results demonstrate that our proposed concept of probabilistic skyline is an effective method to tackle skyline query on incomplete data and our algorithms are tens of times faster than the naive algorithm on both synthetic and real datasets.

This article studies the core maintenance problem for dynamic graphs which requires to update each vertex's core number with the insertion/deletion of vertices/edges. Previous algorithms can either process one edge associated with a vertex in each iteration or can only process one superior edge associated with the vertex (an edge 〈u; v〉 is a superior edge of vertex u if v' core number is no less than u's core number) in each iteration. Thus for high superior-degree vertices (the vertices associated with many superior edges) insertions/deletions, previous algorithms become very inefficient. In this article, we discovered a new structure called joint edge set whose insertions/deletions make each vertex's core number change at most one. The joint edge set mainly contains all the superior edges associated with the high superior-degree vertices as long as these vertices are 3+-hop independent. Based on this discovery, faster parallel algorithms are devised to solve the core maintenance problems. In our algorithms, we can process all edges in the joint edge set in one iteration and thus can greatly increase the parallelism and reduce the processing time. The results of extensive experiments conducted on various types of real-world, temporal, and synthetic graphs illustrate that the proposed algorithms achieve good efficiency, stability and scalability. Specifically, the new algorithms can outperform the single-edge processing algorithms by up to four orders of magnitude. Compared with the matching based algorithm and the superior edge based algorithm, our algorithms show a significant speedup up to 60× in the processing time.

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k -anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

Broadcasting is a fundamental networking service where the source node disseminates the message to all the other nodes. Unfortunately, the problem of Minimum Latency Broadcast Scheduling (MLBS) in duty-cycled wireless networks is not well studied. In the existing works, the construction of broadcast tree and the scheduling of transmissions are conducted separately, which may result in a bad-structured broadcast tree and then a large latency is obtained even using the optimal scheduling method. Thus, the MLBS problem in duty-cycled wireless networks without above limitation is investigated in this paper. Firstly, a Two-Step Scheduling algorithm is proposed to construct the broadcast tree and compute a collision-free schedule simultaneously. The proposed method can generate a latency-aware broadcast tree adaptively to reduce the broadcast latency. To the best of our knowledge, this is the first work that can integrate these two kinds of operations together. Additionally, a novel transmission mode, i.e., concurrent broadcasting, is first introduced in wireless networks and several techniques are designed to further improve the broadcast latency. Finally, the theoretical analysis and experimental results demonstrate the efficiency of the proposed algorithms in term of latency.

Data privacy arises as one of the most important concerns, facing the pervasive commoditization of big data statistic analysis in Internet of Things (IoT). Current solutions are incapable to thoroughly solve the privacy issues on data pricing and guarantee the utility of statistic outputs. Therefore, this paper studies the problem of trading private statistic results for IoT data, by considering three factors. Specifically, a novel framework for trading range counting results is proposed. The framework applies a sampling-based method to generate approximated counting results, which are further perturbed for privacy concerns and then released. The results are theoretically proved to achieve unbiasedness, bounded variance, and strengthened privacy guarantee under differential privacy. Moreover, a pricing approach is proposed for the traded results, which is proved to be immune against arbitrage attacks. The framework is evaluated by estimating the air pollution levels with different ranges on 2014 CityPulse Smart City datasets. The analysis and evaluation results demonstrate that our framework greatly reduces the error of range counting approximation; and the optimal perturbation approach enables that the private counting satisfies the specified approximation degree while providing strong privacy guarantee.

Motivating the mobile users to participate in sensing services for efficient data generation and collection is one of the most critical issues in Mobile Crowdsensing Systems (MCSs). Auction based mechanisms are seen to be promising and effective solutions to incentivize mobile users. However, price is not the unique factor dominating participants' contribution in MCSs. Participant's preference for different sensing tasks is also a pivotal factor which should be considered in the auction mechanisms as assigning the least favorite tasks discourages them to participate in future sensing tasks. Unfortunately, participant's preference has been overlooked by most existing works, which motivates us to fill this gap in this paper. We first propose a new concept "mutual preference degree" to capture participant's preference and then design a preference-based auction mechanism (PreAM) to simultaneously guarantee individual rationality, budget feasibility, preference truthfulness, and price truthfulness. Finally, both the theoretical analysis and simulation results demonstrate the effectiveness of PreAM.

Battery-free sensor network is a new network architecture in which battery-free nodes can harvest energy from the ambient environment and the network lifetime is unlimited in terms of energy. However, the unbalanced ambient energy distribution results in a low data coverage quality of the battery-free sensor network. In this paper, we want to deploy artificial power stations in the monitored region, improve the energy distribution and achieve the energy-data dual coverage. We have investigated two energy-data dual coverage problems in this paper to meet different user requirements. We have proved that these two problems are at least NP-Hard. Two approximated algorithms are proposed accordingly to solve these two problems. The ratio bound and time complexity of these two algorithms are analyzed. Furthermore, extensive simulations are carried out to evaluate the performance of the algorithms. The experimental results show that the algorithms are effective and efficient.

This paper studies the deletion propagation problem in terms of minimizing view side-effect. It is a problem funda-mental to data lineage and quality management which could be a key step in analyzing view propagation and repairing data. The investigated problem is a variant of the standard deletion propagation problem, where given a source database D, a set of key preserving conjunctive queries Q, and the set of views V obtained by the queries in Q, we try to identify a set T of tuples from D whose elimination prevents all the tuples in a given set of deletions on views △V while preserving any other results. The complexity of this problem has been well studied for the case with only a single query. Dichotomies, even trichotomies, for different settings are developed. However, no results on multiple queries are given which is a more realistic case. We study the complexity and approximations of optimizing the side-effect on the views, i.e., find T to minimize the additional damage on V after removing all the tuples of △V. We focus on the class of key-preserving conjunctive queries which is a dichotomy for the single query case. It is surprising to find that except the single query case, this problem is NP-hard to approximate within any constant even for a non-trivial set of multiple project-free conjunctive queries in terms of view side-effect. The proposed algorithm shows that it can be approximated within a bound depending on the number of tuples of both V and △V. We identify a class of polynomial tractable inputs, and provide a dynamic programming algorithm to solve the problem. Besides data lineage, study on this problem could also provide important foundations for the computational issues in data repairing. Furthermore, we introduce some related applications of this problem, especially for query feedback based data cleaning.

The autonomous driving (auto-driving) technology has been promoted significantly by the rapid advances in computer vision and deep neural networks. Auto-driving vehicles, nowadays, are fully equipped with numerous sensors such as cameras, geo-sensors, and radar sensors, to capture real-time data inside the vehicles and outside surroundings. Meanwhile, the captured data contains lots of private information about vehicles, drivers and passengers and thus faces a high risk of privacy breaches. Especially, side-channel information can be mined from camera data to identify vehicles' locations and even trajectories, raising serious privacy issues. Unfortunately, the issue, how to resist location-inference attack for camera data in auto-driving, has never been addressed in literature. In this paper, we intend to fill this blank by developing a GAN-based image-toimage translation method named Auto-Driving GAN (ADGAN). Through performance comparisons between ADGAN and the state-of-the-art, the superiority of ADGAN can be validated - offering an effective tradeoff between recognition utility and privacy protection for camera data.

Thanks to the recent advances in energy-harvesting devices, nodes equipped with such devices are produced and enable Wireless Sensor Networks (WSNs) to be energy self-sustainable. Such networks are named as Battery-Free WSNs (BF-WSNs). Data aggregation is an essential operation in WSNs, and the Minimum Latency Aggregation Scheduling (MLAS) problem which seeks a collision-free aggregation scheduling with the minimum latency has been well studied in Battery-Powered WSNs (BP-WSN). In BP-WSNs, latency is mainly caused by the time overhead in collision-avoiding. However, the time-consumption for node recharging is the main cause of latency in BF-WSNs. Moreover, the collisions are time-independent while the recharge rate is time-varying. Therefore, the previous algorithms are not suitable for BF-WSNs. In addition, if aggregating data from all nodes, the latency would be determined by the node with the lowest recharge rate. Thus, we propose to aggregate a subset of nodes which can meet the given coverage quality requirement. Meanwhile, the aggregation tree and scheduling strategy should be adaptive to the current energy condition. We formulate this problem and propose a distributed algorithm which can select nodes adaptively according to their energy condition and schedule these nodes to achieve the minimum latency, simultaneously. To the best of our knowledge, it is the first distributed algorithm to solve the MLAS problem with coverage guarantee in BF-WSNs. The simulation results verify that our algorithm can reduce aggregation latency effectively, especially in bad energy condition.

In the age of emerging applications, such as Internet of Things (IoT), big data, and data mining, our life becomes more convenient through customized services that utilize a huge amount of personal data generated and collected by various IoT devices. To fully exploit the data value as well as enhance the data utilization, more and more data are being traded in online data markets. While enjoying the benefit from data trading, data sellers are also suffering from severe risk of privacy leakage. In this paper, our objective is to maximize data seller's received utility via balancing the tradeoff between data trading benefit and data privacy cost. To achieve this, contract theory is utilized to design optimal contract trading mechanisms for both complete and incomplete information markets. From our thorough theoretical analysis, comprehensive simulations, and real-data experiments, the effectiveness of our proposed optimal contract mechanisms can be validated, i.e., the maximum utility can be obtained at the seller side, the individual rationality and incentive compatibility can be guaranteed at the buyer side, and the advantages of our mechanism over the single contract mechanisms can be confirmed.

With dramatic developments of blockchain technology, a number of blockchain-based applications emerge rapidly, among which the incorporation of blockchain into Internet of Things is one of the most valued research direction. Such powerful incorporation is a double-sided sword, i.e., it can benefit both individuals and society but has the vulnerability to coin hopping attack that is a new type of pool mining attack and hard to happen in traditional blockchain networks. In this paper, we theoretically prove the feasibility of coin hopping attack, deeply analyze the conditions of attack implementation, and comprehensively investigate the impacts of coin hopping attack. Moreover, some defense strategies are addressed. To our best knowledge, this paper is the first work targeting coin hopping attack.

Mobile edge computing (MEC) is a new computing approach in which computation tasks carried by mobile devices (MDs) can be offloaded to MEC servers or computed locally. Since the MDs are always battery limited and computation tasks have strict deadlines, how to schedule the execution of each task energy effectively is important. Comparing with existing works, we consider a much more complexed scenario, in which multiple moving MDs sharing multiple heterogeneous MEC servers, and a problem named as minimum energy consumption problem in deadline-aware MEC system is formulated. Such problem is proved to be NP-hard, and two approximation algorithms are proposed focusing on single and multiple MD scenarios, respectively. The performances of these algorithms are varied by theoretical analysis and simulations.

Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person's daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider the data privacy on OSNs at the network level rather than the user level. This network-level privacy helps us to prevent information leakage to third-party users, such as advertisers. We propose a novel scheme that combines the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We use an enhanced equi-cardinal clustering (ECC) as a way to achieve k-anonymity. We further improve k-anonymity with l-diversity. Our proposed enhanced ECC ensures that there are at least “k” users in any given network as well as the attributes in each cluster has at least l-distinct values. We further provide proofs on how the proposed ECC ensures k-anonymity and the maximum information loss. We consider a weighted directed social network graph as an input to our method to consider the existing complexities in a social network. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.

In social networks, several influential individuals can promote an idea or a product to numerous individuals. Thus, it is valuable to solve the influence maximization (IM) problem, which asks for finding the most influential set of individuals in a social network. To estimate the influence of individuals, the existing independent cascade (IC) model simulates the influence diffusion only considering the influences from direct in-neighbors to nodes. This consideration does not hold in real life. In many cases, people are likely influenced by information depending on where it comes from, instead of who gives it. To simulate the influence diffusion more accurate, this paper proposes the second-order IC model, which takes the previous influence into consideration. In addition, we design an approximate algorithm and its distributed extension for IM under the second-order IC model. Experimental results show that our second-order IC model outperforms the IC model in terms of simulating influence diffusions. The proposed algorithms are efficient, and the obtained node sets are influential.

Mobile crowdsourcing is an emerging crowdsourcing paradigm, which generates large-scale sensing tasks and sensing data. One of the major issues in mobile crowdsourcing is how to maximize social welfare through selecting appropriate sensing tasks for crowd workers and selecting appropriate workers for sensing tasks such that it can improve the effectiveness and efficiency of mobile crowdsourcing. This paper proposes an incentive mechanism to maximize social welfare for mobile crowdsourcing and, respectively, investigates worker-centric task selection and platform-centric worker selection. This paper applies an optimization algorithm in task selection for mobile crowdsourcing systems. A discrete particle swarm optimization (DPSO) algorithm for worker-centric task selection is designed to maximize the utilities of workers. In addition, a platform-centric worker selection method, which integrates multiattribute auction and two-stage auction, is proposed to maximize the utility of the platform. The performance of the proposed incentive mechanism is evaluated through experiments. The experimental results show that the proposed incentive mechanism can improve the efficiency and truthfulness of mobile crowdsourcing effectively.

Due to the prominent development of public transportation systems, the taxi flows could nowadays work as a reasonable reference to the trend of urban population. Being aware of this knowledge will significantly benefit regular individuals, city planners, and the taxi companies themselves. However, to mindlessly publish such contents will severely threaten the private information of taxi companies. Both their own market ratios and the sensitive information of passengers and drivers will be revealed. Consequently, we propose in this paper a novel framework for privacy-preserved traffic sharing among taxi companies, which jointly considers the privacy, profits, and fairness for participants. The framework allows companies to share scales of their taxi flows, and common knowledge will be derived from these statistics. Two algorithms are proposed for the derivation of sharing schemes in different scenarios, depending on whether the common knowledge can be accessed by third parties like individuals and governments. The differential privacy is utilized in both cases to preserve the sensitive information for taxi companies. Finally, both algorithms are validated on real-world data traces under multiple market distributions.

Transmission is crucial for big data analysis and learning in industrial Internet of Things (IoT) systems. To transmit data with limited energy is a challenge. This paper studies the problem of data transmission in energy harvesting systems with capacitor to supply energy where the energy receiving rate varies over time. The energy receiving rate is slower when the capacitor receives more energy. Based on this characteristic, we study the problem of how to transmit more data when the energy receiving time is not continuous. Given many packets that arrive at different time instances, there is a tradeoff between transmitting the packet right now or saving the energy to transmit the future arriving packets. We formalize two types of problems. The first one is how to minimize the total completion time when there is enough energy to transmit all the packets. The second one is how to transmit as many packets as possible when the energy is not enough to transmit all the packets. For the first problem, we give a 1 + α approximation off line algorithm when all the information of the packets and the energy receiving periods is known in advance, and a max{2, β} competitive ratio online algorithm where the information is not known in advance. For the second problem, we study three cases and give a 6 + [h/(b/R)] approximation off line algorithm for the general situation. We also prove that there does not exit a constant competitive ratio online algorithm.

A new network architecture, named as RF-based battery-free sensor network, was proposed in recent years to overcome the lifetime limitation of traditional wireless sensor networks. In an RF-based battery-free sensor network, the battery-free nodes equip no battery and can be recharged by RF-signals. The Dominating Set (DS) is a key method to maintain the coverage of traditional WSNs, and it can be also adopted in the RF-based battery-free sensor networks. However, considering the specific features of RF-based battery-free sensor networks, the DS construction is totally different from that in traditional WSNs. Thus, the problem of constructing DS in a battery-free sensor network is deeply investigated in this article. The NP-Hardness of such problem is proved. Four approximation algorithms are proposed to deal with the snapshot and continuous DS construction requirements, respectively. The approximation ratios of these four algorithms have been analyzed, and the theoretical results show that all these four algorithms are effective. Furthermore, the electromagnetic interference problem in the RF-based battery-free sensor network is considered and defined. An approximated algorithm is proposed to solve such problem. Finally, extensive simulations are carried out. The experimental results verify that the proposed algorithms have high performance in terms of accuracy and efficiency.

Crowdsourcing is the perfect show of collective intelligence, and the key of finishing perfectly the crowdsourcing task is to allocate the appropriate task to the appropriate worker. Now the most of crowdsourcing platforms select tasks through tasks search, but it is short of individual recommendation of tasks. Tag-semantic task recommendation model based on deep learning is proposed in the paper. In this paper, the similarity of word vectors is computed, and the semantic tags similar matrix database is established based on the Word2vec deep learning. The task recommending model is established based on semantic tags to achieve the individual recommendation of crowdsourcing tasks. Through computing the similarity of tags, the relevance between task and worker is obtained, which improves the robustness of task recommendation. Through conducting comparison experiments on Tianpeng web dataset, the effectiveness and applicability of the proposed model are verified.

The emerging energy harvesting technology enables charging sensor batteries with renewable energy sources, which has been effectively integrated into Wireless Sensor Networks (EH-WSNs). Meanwhile, data aggregation is an essential operation in a WSN. The problem of Minimum Latency Aggregation Scheduling (MLAS) which seeks a fast and collision-free aggregation schedule has been well studied when nodes are energy-abundant. However, due to the limited energy harvesting capacities of tiny sensors, the captured energy remains scarce and differs greatly among nodes. Thus, all of the previous algorithms for MLAS are not suitable in EH-WSNs. In this paper, we investigate the MLAS problem in EH-WSNs. To make use of the harvested energy smartly, we construct an aggregation tree adaptively according to the residual battery level at each node. Furthermore, we identify a new kind of collision, named as energy -collision, and design a special structure to assist in avoiding it. By considering transmitting time, residual energy, and energy-collision, we propose three scheduling algorithms for MLAS problem in EH-WSNs. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of aggregation latency compared with the baseline methods.

Battery-Free Wireless Sensor Network (BF-WSN) is a newly proposed network architecture to address the limitation of traditional Wireless Sensor Networks (WSNs). The special features of BF-WSNs make the coverage problem quite different and even more challenging from and than that in traditional WSNs. This paper defines a new coverage problem in BF-WSNs which aims at maximizing coverage quality rather than prolonging network lifetime. The newly defined coverage problem is proved to be at least NP-Hard. Two sufficient conditions, under which the optimal solution of the problem can be derived in polynomial time, are given in this paper. Furthermore, two approximate algorithms are proposed to derive nearly optimal coverage when the sufficient conditions are unsatisfied. The time complexity and approximate ratio of the two algorithms are analyzed. Extensive simulations are carried out to examine the performance of the proposed algorithms. The simulation results show that these algorithms are efficient and effective.

Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person's daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider data privacy on online social networks at the network level rather than user level. This network level privacy helps us to prevent information leakage to third-party users like advertisers. We propose a novel scheme that combines both node and edge privacy of the OSN by clustering the similar users. We further improve traditional clustering by utilizing the concept of k-anonymity. Our enhanced equicardinal clustering ensures that there are at least k users in any given network. We further provide proofs on how the proposed equicardinal clustering ensures k-anonymity and the maximum information loss. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.

The recommender system is mainly used in the e-commerce platform. With the development of the Internet, social networks and e-commerce networks have broken each other’s boundaries. Users also post information about their favorite movies or books on social networks. With the enhancement of people’s privacy awareness, the personal information of many users released publicly is limited. In the absence of items rating and knowing some user information, we propose a novel recommendation method. This method provides a list of recommendations for target attributes based on community detection and known user attributes and links. Considering the recommendation list and published user information that may be exploited by the attacker to infer other sensitive information of users and threaten users’ privacy, we propose the CDAI (Infer Attributes based on Community Detection) method, which finds a balance between utility and privacy and provides users with safer recommendations.

Sensor-embedded smartphones have become ubiquitous nowadays, further leveraging the popularity of mobile crowdsensing. A mobile crowdsensing platform gathers sensory data from smartphone users and makes payments to them in return. Due to the spatial correlation of sensory data in various applications, users close to each other in geographical positions usually provide similar sensory data, and it is quite an economic waste for a mobile sensing platform to buy duplicated sensory data with multiple payments to geographically close users. Unfortunately, the existing works do not take this matter into consideration. To prevent waste, our paper considers geographical position conflicting mobile crowdsensing systems in which any two users within a limited geographical distance cannot obtain payments simultaneously while participating in crowdsensing tasks. Two algorithms are proposed to select appropriate mobile crowdsensing participants and calculate the payments to them. Solid theoretical proofs are presented to demonstrate the beneficial properties of our proposed algorithms. The extensive experiment results based on real-world datasets indicate that our proposed algorithms are efficient while providing beneficial properties.

Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, sensitive information may be predicted out of released data through data mining techniques. Therefore, sanitizing network data prior to release is necessary. In this paper, we explore how to launch an inference attack exploiting social networks with a mixture of non-sensitive attributes and social relationships. We map this issue to a collective classification problem and propose a collective inference model. In our model, an attacker utilizes user profile and social relationships in a collective manner to predict sensitive information of related victims in a released social network dataset. To protect against such attacks, we propose a data sanitization method collectively manipulating user profile and friendship relations. Besides sanitizing friendship relations, the proposed method can take advantages of various data-manipulating methods. We show that we can easily reduce adversary's prediction accuracy on sensitive information, while resulting in less accuracy decrease on non-sensitive information towards three social network datasets. This is the first work to employ collective methods involving various data-manipulating methods and social relationships to protect against inference attacks in social networks.

The view propagation problem is a class of view update problem in relational databases [7], involving deletion and insertion propagations. Given source database D, conjunctive query Q, view V generated by query Q(D) and a deletion (insertion) on view ΔV, deletion (insertion) propagation is to find a side effect free update ΔD on D such that the deletion (insertion) of ΔD from (into) D will delete (insert) the intentional ones ΔV without resulting in the deletion (insertion) of additional tuples from (into) the view. Generally, such a deletion (insertion) is side effect free. The related data management applications include query result explanation, data debugging, and anonymizing datasets, which rely on understanding how interventions in a database affect the output of a query. View propagation is a natural and typical way to define such interventions, which seems to be well-studied. However, in general, the candidate update on a source database is picked up aimlessly in advance, making the updated database to be very distant from the original one no matter whether it is the maximum one. In this paper, we formally define the bounded view propagation problem, where candidate update ΔD is bounded as a subset of potential C which is a fixed small tuple set of D. We study the complexity of this problem for conjunctive queries, and make contributions to the previous results of the problems of side-effect free deletion propagation. Specifically, our bounded view propagation problem decreases computational complexity regardless of conjunctive query structure. We show the fixed potential is actually a dichotomy for both deletion and insertion propagations, and figure out the results on combined complexity which is neglected previously. Based on our results, for view propagation, we map out a complete picture of the computational complexity hierarchy for conjunctive queries on both data and combined complexities. Moreover, this bounded version is an update forbidden case of view propagation, and our results can be applied to it.

In the cloud, multiple tenants share the resource of datacenters and their applications compete with each other for scarce network bandwidth. Current studies have shown that the lack of bandwidth guarantee causes unpredictable network performance, leading to poor application performance. To address this issue, several virtual network abstractions have been proposed which allow the tenants to reserve virtual clusters with specified bandwidth between the Virtual Machines (VMs) in the datacenters. However, all these existing proposals require the tenants to deterministically characterize the bandwidth demands in the abstractions, which can be difficult and result in inefficient bandwidth reservation due to the demand uncertainty. In this paper, we explore a virtual cluster abstraction with stochastic bandwidth characterization to address the bandwidth demand uncertainty. We propose Stochastic Virtual Cluster (SVC), which models the bandwidth demand between VMs in a probabilistic way. Based on SVC, we develop a stochastic framework for virtual cluster allocation, in which the admitted virtual cluster's bandwidth demands are satisfied with a high probability. Efficient VM allocation algorithms are proposed to implement the framework while reducing the possibility of link congestion through minimizing the maximum bandwidth occupancy of a virtual cluster on physical links. Using simulations, we show that SVC achieves the trade-off between the job concurrency and the average job running time, and demonstrate its effectiveness for accommodating cloud application workloads with highly volatile bandwidth demands and its improvement to work-conserving bandwidth enforcement.

The skyline query is important in database community. In recent years, the researches on incomplete data have been increasingly considered, especially for the skyline query. However, the existing skyline definition on incomplete data cannot provide users with valuable references. In this paper, we propose a novel skyline definition utilizing probabilistic model on incomplete data where each point has a probability to be in the skyline. In particular, it returnsK points with the highest skyline probabilities. Meanwhile, it is a big challenge to compute probabilistic skyline on incomplete data. We propose an efficient algorithm PISkyline, which utilizes two pruning strategies to reduce the number of points and adopts two optimizations to accelerate probability computation for each point. Nevertheless, PISkyline is susceptible to the order of input data and there is still a great deal of room for optimization. We develop a point-level sorting technique by adjusting the order of accessing points to further improve the efficiency of PISkyline. Our experimental results demonstrate that our algorithms are tens of times faster than the naive algorithm on both synthetic and real datasets.

With the emergence of Mobile Crowdsensing Systems (MCSs), many auction schemes have been proposed to incentivize mobile users to participate in sensing activities. However, in most of the existing work, the heterogeneity of MCSs has not been fully exploited. To tackle this issue, in this paper, we study the joint problem of sensing task assignment and scheduling while considering partial fulfillment, attribute diversity, and price diversity. We first elaborately model the problem as a reverse auction and design a distributed auction framework. Then, based on this framework, we propose two distributed auction schemes, cost-preferred auction scheme (CPAS) and time schedule-preferred auction scheme (TPAS), which differ on the methods of task scheduling, winner determination, and payment computation. We further rigorously prove that both CPAS and TPAS can achieve computational-efficiency, individual-rationality, budget-balance, and truthfulness. Finally, the simulation results validate the effectiveness of both CPAS and TPAS in terms of sensing task's allocation efficiency, mobile user's working time utilization and utility, and truthfulness.

Data aggregation is an essential operation for the sink to obtain summary information in a Wireless Sensor Network (WSN). The problem of Minimum Latency Aggregation Schedule (MLAS) which seeks a fastest and collision-free aggregation schedule has been well studied when nodes are always awake. However, in duty-cycle WSNs, nodes can only receive data in active state. In such networks, it is of great importance to exploit the limited active time slots to reduce aggregation latency. Unfortunately, few studies have addressed this issue and most previous aggregation methods rely on fixed structures which greatly limit the exploitation of the active time slots from other neighbors. In this paper, we investigate the MLAS problem in duty-cycle WSNs without considering structures. We propose the first distributed aggregation algorithm for duty-cycle WSNs, in which the aggregation tree and a conflict-free schedule are generated simultaneously. Compared with the previous centralized and distributed methods, the aggregation latency and the utilization ratio of available time slots are greatly improved. The theoretical analysis and simulation results verify that the proposed algorithm has high performance in terms of latency and communication cost.

Currently, the limitation of battery has become a serious obstacle for the development of Internet of things (IoTs). Therefore, a new network architecture, named as battery-free network, were proposed. In a typical battery-free network, the battery-free nodes are equipped with any battery and can only gain energy from the environment. Such network extremely expands the scope of the IoT applications, however, it also brings many troubles for some network operations, e.g. data collection, since the energy of each node is quite small. Considering that the Connected Dominating Sets (CDSs) are commonly used to support data collection and network communication in wireless networks, and thus we will also investigate the CDS construction problem in battery-free networks. In this paper, the problem of constructing CDS in a battery-free network is formally defined, and we prove that it is NP-Complete. Thus, four approximation algorithms were proposed to deal with the snapshot, continuous and time-window based CDS construction requirements, respectively. Finally, the extensive experiments were carried out and the results verify that the proposed algorithms have high performance in term of accuracy and efficiency.

Local business service systems (LBSS), such as Yelp and Dianping, play an essential role in making decisions like choosing a restaurant for our daily life. These systems heavily rely on individuals' voluntarily submitted reviews to build the reputation for nearby businesses. Unfortunately, the reviews expose users' private information such as visited places to the public and adversaries. Even worse, such location information is always public as it is the basic information of businesses, and adversaries could be anyone ranging from advertisement spammer to physical stalker. This paper formalizes the privacy preserving problem in local business service systems and propose a novel location privacy preserving framework. The framework can preserve users' location privacy in arbitrary local area and can maintain a good utility for both the system and every user. We evaluate our framework thoroughly towards real-world data traces. The results validate that the framework can achieve a good performance.

With the fast development of wireless technologies, wireless applications have invaded various areas in people's lives with a wide range of capabilities. Guaranteeing quality-of-service (QoS) is the key to the success of those applications. One of the QoS requirements, service frequency, is very important for tasks including multimedia transmission in the Internet of Things. A service frequency constraint denotes the length of the time period during which a link can transmit at least once. Unfortunately, it has not been well addressed yet. Therefore, this paper proposes a new framework to schedule multitransmitting flows in wireless networks considering service frequency constraint for each link. In our model, the constraints for flows are heterogeneous due to the diversity of users' behaviors. We first introduce a new definition for network stability with service frequency constraints and demonstrate that the novel scheduling policy is throughput-optimal in one fundamental category of network models. After that, we discuss the performance of a wireless network with service frequency constraints from the views of capacity region and total queue length. Finally, a series of evaluations indicate the proposed scheduling policy can guarantee service frequency and achieve a good performance on the aspect of queue length of each flow.

Due to the close correlation with individual's physical features and status, the adoption of cyber-physical social systems (CPSSs) has been inevitably hindered by users' privacy concerns. Such concerns keep growing as our bile devices have more embedded sensors, while the existing countermeasures only provide incapable and limited privacy preservation for sensitive physical information. Therefore, we propose a novel privacy preservation framework for CPSSs. We formulate both the privacy concerns and user expectations in CPSSs based on real-world knowledge. We also design a corresponding data publishing mechanism for users. It regulates the publishing behaviors to hide sensitive physical profiles. Meanwhile, the published data retain comprehensive social profiles for users. Our analysis demonstrates that the mechanism achieves a local maximized performance on the aspect published data size. The experiment results toward real datasets reveals that the performance is comparable to the global optimal one.

Mobile devices bring benefits as well as the risk of exposing users’ location information, as some embedded sensors can be accessed without users’ permission and awareness. In this paper, we show that, only by using the data collected from the embedded sensors in mobile devices instead of GPS data, we can infer a user’s location information with high accuracy. Three issues are addressed which are route identification, user localization in a specific route, and user localization in a bounded area. The Dynamic Time Warping based technique is designed and we develop a Hidden Markov Model to solve the localization problem. Real experiments are performed to evaluate our proposed methods.

Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries’ arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via Jenson-Shannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose ϵ -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the ϵ -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.

Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unraveling complex relationships between genotypes and phenotypes. Current multi-locus-based methods are insufficient to detect interactions with diverse genetic effects on multifarious diseases. Also, statistic tests for high-order epistasis ( ≥ 2 SNPs) raise huge computational and analytical challenges because the computation increases exponentially as the growth of the cardinality of SNPs combinations. In this paper, we provide a simple, fast and powerful method, named DAM, using Bayesian inference to detect genome-wide multi-locus epistatic interactions in multiple diseases. Experimental results on simulated data demonstrate that our method is powerful and efficient. We also apply DAM on two GWAS datasets from WTCCC, i.e., Rheumatoid Arthritis and Type 1 Diabetes, and identify some novel findings. Therefore, we believe that our method is suitable and efficient for the full-scale analysis of multi-disease-related interactions in GWASs.

The huge volume of contents generated from mobile ends has dramatically contributed to big data. Unfortunately, current packet scheduling policies in wireless networks for the underlying big data delivery greatly hinder the utilization of the contents. Specifically, the delivery of real-time big data requires running-time interactions with users, as well as variable bandwidth consumptions to maintain fine user experience. There is no previous work designed for this kind of traffic. To mitigate this gap, a thorough-designed scheduling policy to assign and organize detailed packet transmissions for real-time big data is desired. This paper takes video delivery as a case study, which dominates the current real-time traffic and can be easily extended to other scenarios. We propose a novel scheduling policy, which assigns a proper number of video requests to servers and allocates bandwidth to these requests in a relatively small time scale. It helps with serving more users without compromising user experience of the current ones. We also prove that the scheduling policy has a guaranteed performance on the total number of served requests. Finally, the simulation results demonstrate that our scheduling policy outperforms other state-of-art methods significantly.

The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks (WSNs). The scale of sensory data in many applications has already exceeded several petabytes annually, which is beyond the computation and transmission capabilities of conventional WSNs. On the other hand, the information carried by big sensory data has high redundancy because of strong correlation among sensory data. In this paper, we introduce the novel concept of ϵ-Kernel Dataset, which is only a small data subset and can represent the vast information carried by big sensory data with the information loss rate being less than ϵ, where ϵ can be arbitrarily small. We prove that drawing the minimum ϵ-Kernel Dataset is polynomial time solvable and provide a centralized algorithm with O(n3) time complexity. Furthermore, a distributed algorithm with constant complexity O(1) is designed. It is shown that the result returned by the distributed algorithm can satisfy the ϵ requirement with a near optimal size. Furthermore, two distributed algorithms of maintaining the correlation coefficients among sensor nodes are developed. Finally, the extensive real experiment results and simulation results are presented. The results indicate that all the proposed algorithms have high performance in terms of accuracy and energy efficiency.

The past decade witnessed the dramatic evolution from Quality of Service (QoS) to Quality of Experience (QoE) in the design of wireless networks, especially on the aspect of link scheduling. In many applications, end users are concerned more about transmission quality of an individual task rather than the quality of a link, where a task may refer to a piece of music, video, etc. and may include many packets. This paper proposes a new network model aiming at improving user experience by pushing the scheduling problem to the task layer. A novel QoE requirement is designed to generalize the QoS requirements of a task, which is the ratio requirement. Following this design, a corresponding scheduling policy is proposed to capture it for each task and then reach an application-aware transmission allocation. We theoretically analyze the performance of the scheduling policy, and discuss the design of an optimal solution and the impact of the QoE requirements. Finally, the simulation results indicate that our scheduling policy can significantly improve QoE.

Multicast is an important way to diffuse data in duty-cycled wireless sensor networks (WSNs), where nodes can receive data only in active state. The communication delay can be extremely large if inappropriate schedules are adopted. Unfortunately, most previous methods do not consider controlling multicast delay energy-efficiently. This paper studies the minimum active time slot augmentation for delay-bounded multicast (MAADM) problem in duty-cycled WSNs. The MAADM problem is proved to be NP-hard even under the node-exclusive interference model. An optimal algorithm is proposed for the MAADM problem when K = 2 and a heuristic latency bounding algorithm is proposed for source-to-all communications, where K denotes the number of the destination nodes. When K > 2, two (K-1)-approximation algorithms are designed for the MAADM problem. In addition, a low computation-complexity distributed algorithm is proposed. To the best of our knowledge, this is the first work that develops a series of efficient centralized and distributed algorithms for the MAADM problem in dutycycled WSNs. The theoretical analysis and experimental results verify that all the proposed algorithms have high performance in terms of delivery delay and energy consumption.

Duty-cycle scheduling is an effective way to balance energy consumptions and prolong network lifetime of wireless sensor networks (WSNs), which usually requires a connected dominating set (CDS) to guarantee network connectivity and coverage. Therefore, the problem of finding the largest number of CDSs is important for WSNs. The previous works always assume all the nodes are non-rechargeable. However, WSNs are now taking advantages of rechargeable nodes to become energy harvest networks (EHNs). To find the largest number of CDSs then becomes completely different. This is the first work to investigate, how to identify the largest number of CDSs in EHNs to prolong network lifetime. The investigated novel problems are proved to be NP-Complete and we propose four approximate algorithms, accordingly. Both the solid theoretical analysis and the extensive simulations are performed to evaluate our algorithms.

Holistic aggregations are popular queries for users to obtain detailed summary information from Wireless Sensor Networks. An aggregation operation is holistic if there is no constant bound on the size of the storage needed to describe a sub-aggregation. Since holistic aggregation cannot be distributable, it requires that all the sensory data should be sent to the sink in order to obtain the exact holistic aggregation results, which costs lots of energy. However, in most applications, exact holistic aggregation results are not necessary; instead, approximate results are acceptable. To save energy as much as possible, we study the approximated holistic aggregation algorithms based on uniform sampling. In this article, four holistic aggregation operations, frequency, distinct-count, rank, and quantile, are investigated. The mathematical methods to construct their estimators and determine optional sample size are proposed, and the correctness of these methods are proved. Four corresponding distributed holistic algorithms to derive (ϵ, δ)-approximate aggregation results are given. The solid theoretical analysis and extensive simulation results show that all the proposed algorithms have high performance on the aspects of accuracy and energy consumption.

The pay-as-you-go service model impels cloud customers to reduce the usage cost of bandwidth. Traffic Redundancy Elimination (TRE) has been shown to be an effective solution for reducing bandwidth costs, and thus has recently captured significant attention in the cloud environment. By studying the TRE techniques in a trace driven approach, we found that both short-term (time span of seconds) and long-term (time span of hours or days) data redundancy can concurrently appear in the traffic, and solely using either sender-based TRE or receiver-based TRE cannot simultaneously capture both types of traffic redundancy. Also, the efficiency of existing receiver-based TRE solution is susceptible to the data changes compared to the historical data in the cache. In this paper, we propose a Cooperative end-to-end TRE solution (CoRE) that can detect and remove both short-term and long-term redundancy through a two-layer TRE design with cooperative operations between layers. An adaptive prediction algorithm is further proposed to improve TRE efficiency through dynamically adjusting the prediction window size based on the hit ratio of historical predictions. Besides, we enhance CoRE to adapt to different traffic redundancy characteristics of cloud applications to improve its operation cost. Extensive evaluation with several real traces show that CoRE is capable of effectively identifying both short-term and long-term redundancy with low additional cost while ensuring TRE efficiency from data changes.

Transitional Region Phenomenon leads to the existence of lossy links in wireless networks, which results in a transmission between two users who are theoretically connected under the Deterministic Network Model cannot be guaranteed. Therefore, we focus on a more practical network model - Probabilistic Network Model (PNM) which can better characterize the lossy links in wireless networks. To be specific, we focus on the investigation of accelerating data aggregation process in probabilistic wireless networks with the cognitive radio technology. By involving cognitive radio technology, users in the wireless networks can seek extra transmission opportunity if other spectrum resource is available. Otherwise, the data aggregation process still can be done on the default working spectrum. Particularly, we are interested in the time efficient data aggregation scheduling problem. In this work, a two phase scheduling algorithm is proposed. The first phase is finding an efficient routing structure considering the speciality of the network model under investigation. In the second phase, a dynamic scheduling algorithm is introduced. Theoretical analysis is provided to estimate the lower latency bound for the scheduling algorithm, followed by the experimental simulation verification.

Online social networks have gained significant popularity recently. The problem of influence maximization in online social networks has been extensively studied. However, in prior works, influence propagation in the physical world, which is also an indispensable factor, is not considered. The Location-Based Social Networks (LBSNs) are a special kind of online social networks in which people can share location-embedded information. In this paper, we make use of mobile crowdsourced data obtained from location-based social network services to study influence maximization in LBSNs. A novel network model and an influence propagation model taking influence propagation in both online social networks and the physical world into consideration are proposed. An event activation position selection problem is formalized and a corresponding solution is provided. The experimental results indicate that the proposed influence propagation model is meaningful and the activation position selection algorithm has high performance.

A Wireless Sensor Network consists of a number of sensors. The energy of each sensor is limited which limits network lifetime. There are many existing energy efficiency algorithms to prolong network lifetime. Basically, there are two kinds of methods. One is energy-efficiency management, such as duty-cycling using virtual-backbones. The other one is energy provision, such as energy harvest from the environment. In this paper, we introduce a new problem, CDSEH, to combine these two methods together. We also propose a new standard to define the network lifetime of a WSN. We prove that the CDSEH problem is NP-Complete and propose two approximate algorithms accordingly. Extensive simulation results are shown to validate the performance of our algorithms.

Network virtualization with bandwidth guarantee is essential for the performance predictability of cloud applications because of the shared multi-tenant nature of the cloud. Several virtual network abstractions have been proposed for the tenants to specify and reserve their virtual clusters with bandwidth guarantee. However, they require pre-determined fixed cluster size and bandwidth, and do not support the scaling of the cluster in size and bandwidth requirements. On the other hand, the existing works on virtual cluster scaling focus on dynamically adjusting the cluster size without considering any bandwidth guarantee targeted by current network abstractions. To fill the gap, this paper considers the problem of scaling up a virtual network abstraction with bandwidth guarantee. Efficient algorithms are proposed to find the valid allocation for the scaled cluster abstraction with optimization on the VM locality of the cluster. We also point out the case that a virtual cluster cannot be scaled without changing its original VM placement, and propose an optimal allocation algorithm that exploits the VM migration to address this issue while minimizing the total migration cost for the virtual cluster scaling. Extensive simulations demonstrate the effectiveness and efficiency of our algorithms.

The advances in sensing capabilities of smartphones give rise to a variety of mobile participatory sensing applications that collect users' personal data. Because of the existence of both sensitive, private personal data, and untrusted aggregator, serious privacy concerns on users arise. Currently, existing privacy preserving data collection methods either require bidirectional communications between an untrusted aggregator and mobile users in every aggregation period, or have high computation or communication overhead. To address these problems, we propose an efficient data aggregation approach by which an untrusted aggregator in mobile sensing can collect the statistics over the data contributed by multiple mobile users, while supporting privacy preservation of each user and data integrity verification. In this approach, information hiding and homomorphic encryption are applied to guarantee the data privacy of mobile users. In detail, a breadth-first search tree is first constructed at the initial phase among the mobile users, and then the original datum of each user is perturbed among its neighbors in ciphertext space by using information hiding and homomorphic encryption. The evaluations of our approach show that our protocol requires lower communication and computation overhead and thus more feasible for the computation constrained mobile devices. Copyright © 2016 John Wiley & Sons, Ltd.

Genome-wide association studies (GWASs), which assay more than a million single nucleotide polymorphisms (SNPs) in thousands of individuals, have been widely used to identify genetic risk variants for complex diseases. However, most of the variants that have been identified contribute relatively small increments of risk and only explain a small portion of the genetic variation in complex diseases. This is the so-called missing heritability problem. Evidence has indicated that many complex diseases are genetically related, meaning these diseases share common genetic risk variants. Therefore, exploring the genetic correlations across multiple related studies could be a promising strategy for removing spurious associations and identifying underlying genetic risk variants, and thereby uncovering the mystery of missing heritability in complex diseases. We present a general and robust method to identify genetic patterns from multiple large-scale genomic datasets. We treat the summary statistics as a matrix and demonstrate that genetic patterns will form a low-rank matrix plus a sparse component. Hence, we formulate the problem as a matrix recovering problem, where we aim to discover risk variants shared by multiple diseases/traits and those for each individual disease/trait. We propose a convex formulation for matrix recovery and an efficient algorithm to solve the problem. We demonstrate the advantages of our method using both synthesized datasets and real datasets. The experimental results show that our method can successfully reconstruct both the shared and the individual genetic patterns from summary statistics and achieve comparable performances compared with alternative methods under a wide range of scenarios. The MATLAB code is available at: http://www.comp.hkbu.edu.hk/~xwan/iga.zip.

Routing is one of the most important and fundamental issues in cognitive radio networks (CRNs). However, most of the existing routing algorithms for CRNs either cannot fully take account of the spectrum dynamics or are resource aided which might introduce too much cost. Therefore, in this paper, we study to design an effective routing scheme with respect to induced latency and energy consumption for CRNs. Our main contributions are threefold. First, we propose a spectrum-aware semi-structure routing (SSR) framework which also incorporates power control for CRNs. By employing forwarding zones and routing zones, SSR can utilize the local real-time spectrum dynamics effectively and meanwhile guarantee the global routing performance. In addition, without sacrificing spectrum opportunities, SSR achieves energy efficiency by completing each data transmission with the lowest allowed working power. Second, aiming at closing the gap of lacking of analytical models for routing protocol performance [4] in CRNs, we propose a mathematical framework for SSR which includes a latency analytical model and an energy consumption analytical model. Under the dense scaling network distribution model, we demonstrate (i) the upper bound of the induced latency and scalability of SSR; and (ii) the optimality of SSR with respect to energy consumption, which is approximately optimal. Finally, extensive simulations are conducted to validate the performance of SSR. Simulation results indicate that SSR can utilize spectrum dynamics effectively and has better performance than state-of-the-art methods.

Users can enjoy personalized services provided by various context-aware applications that collect users' contexts through sensor-equipped smartphones. Meanwhile, serious privacy concerns arise due to the lack of privacy preservation mechanisms. Currently, most mechanisms apply passive defense policies in which the released contexts from a privacy preservation system are always real, leading to a great probability with which an adversary infers the hidden sensitive contexts about the users. In this paper, we apply a deception policy for privacy preservation and present a novel technique, FakeMask, in which fake contexts may be released to provably preserve users' privacy. The output sequence of contexts by FakeMask can be accessed by the untrusted context-aware applications or be used to answer queries from those applications. Since the output contexts may be different from the original contexts, an adversary has greater difficulty in inferring the real contexts. Therefore, FakeMask limits what adversaries can learn from the output sequence of contexts about the user being in sensitive contexts, even if the adversaries are powerful enough to have the knowledge about the system and the temporal correlations among the contexts. The essence of FakeMask is a privacy checking algorithm which decides whether to release a fake context for the current context of the user. We present a novel privacy checking algorithm and an efficient one to accelerate the privacy checking process. Extensive evaluation experiments on real smartphone context traces of users demonstrate the improved performance of FakeMask over other approaches.

Vehicular ad hoc networks (VANETs) which are deployed along roads make traffic systems safer and efficient. Existing theoretical results on capacity scaling laws provide insights and guidance for the design and deployment of VANETs. In this paper, we propose a novel fundamental framework RVWNM (Real Vehicular Wireless Network Model), which enables a more realistic capacity analysis in VANETs. We first introduce a Euclidean planar graph which can be constructed from any real map of urban area, and represents the practical geometry structure of the urban area. Then, an interference relationship graph is abstracted from the Euclidean planar graph which considers the transmission interference relations among the nodes in the network. Finally, we analyze theoretically the interference relationships in the interference relationship graph. As far as we know, we are the first to use a practical geometry structure to calculate the asymptotic capacity of VANETs. To verify the feasibility of RVWNM, we calculate the asymptotic capacity of urban area VANETs with the consideration of social- proximity based mobility of vehicles.

The spread of rumors in Online Social Networks (OSNs) poses great challenges to the social peace and public order. It is imperative to model propagation dynamics of rumors and develop corresponding countermeasures. Most of the existing works either overlook the heterogeneity of social networks or do not consider the cost of countermeasures. Motivated by these issues, this paper proposes a heterogeneous network based epidemic model that incorporates both the network heterogeneity and various countermeasures. Through analyzing the existence and stability of equilibrium solutions of the proposed ODE (Ordinary Differential Equation) system, the critical conditions that determine whether a rumor continuously propagates or becomes extinct are derived. Moreover, we concern about the cost of the main two types of countermeasures, i.e., Blocking rumors at influential users and spreading truth to clarify rumors. Employing the Pontryagin's maximum principle, we obtain the optimized countermeasures that ensures a rumor can become extinct at the end of an expected time period with lowest cost. Both the critical conditions and the optimized countermeasures provide a real-time decision reference to restrain the rumor spreading. Experiments based on Digg2009 dataset are conducted to evaluate the effectiveness of the proposed dynamic model and the efficiency of the optimized countermeasures.

Holistic aggregation results are important for users to obtain summary information from Wireless Sensor Networks (WSNs). Holistic aggregation requires all the sensory data to be sent to the sink, which costs a huge amount of energy. Fortunately, in most applications, approximate results are acceptable. We study the approximated holistic aggregation algorithms based on uniform sampling. In this paper, four holistic aggregation operations are investigated. The mathematical methods to construct their estimators and determine the optional sample size are proposed, and the correctness of these methods is proved. Four corresponding distributed holistic algorithms are presented. The theoretical analysis and simulation results show that the algorithms have high performance.

The pervasiveness of mobile applications stimulates more eager demand for Quality of Experience (QoE) than Quality of Service (QoS), especially on the aspect of link scheduling in wireless networks. In many applications, end users concern more about transmission quality of an individual task rather than an individual packet. A task may correspond to a piece of video, music, etc. And may include many packets. This paper proposes a new network model aiming at improving users' experience that pushes the scheduling problem to the task layer. We first introduce a QoE requirement that can generalize the QoS requirement in link scheduling, the partial result requirement. Subsequently, a novel scheduling policy is proposed which can capture this requirement for each task, and then performs an application-aware scheduling. We theoretically analyze the performance of the novel scheduling policy, and discuss the impact of the QoE requirements and network settings. Finally, the simulation results indicate that our scheduling policy can significantly improve QoE.

The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks. The scale of the sensory data in many applications has already exceeds several petabytes annually, which is beyond the computation and transmission capabilities of the conventional WSNs. On the other hand, the information carried by big sensory data has high redundancy because of strong correlation among sensory data. In this paper, we define the concept of e-dominant dataset, which is only a small data set and can represent the vast information carried by big sensory data with the information loss rate being less than e, where e can be arbitrarily small. We prove that drawing the minimum e-dominant dataset is polynomial time solvable and provide a centralized algorithm with 0(n3) time complexity. Furthermore, a distributed algorithm with constant complexity (O(l)) is also designed. It is shown that the result returned by the distributed algorithm can satisfy the e requirement with a near optimal size. Finally, the extensive real experiment results and simulation results are carried out. The results indicate that all the proposed algorithms have high performance in terms of accuracy and energy efficiency.

Event monitoring is a popular task carried out by Wireless Sensor Networks (WSNs). A composite event involves multiple properties requiring different types of sensors to monitor. Considering the costs of different deployment of heterogeneous sensors and the total budget for a monitored region, this paper investigates the composite event coverage problem with the purpose of optimizing coverage quality subjecting to the constraint of not exceeding the total budget. This is a novel coverage problem which is different from the traditional ones where deployment costs of sensors, total budget and composite events are not considered. Two exact algorithms are proposed whose time complexities are O(nk) and O(nk−1) respectively in the worst case, and a (1 − e−1)-approximate algorithm are designed. The simulation results indicate the efficiency and effectiveness of the proposed algorithms.

Myopia becomes a more and more serious worldwide problem as the number of myopic people (especially young people) grows rapidly. Efficient methods are required to monitoring the deterioration of nearsightness so as to take further treatment. This demo realizes a noval nearsightness monitoring system, called iSee, which utilizes the widely used smartphones to detect the deterioration of nearsightness by monitoring and analysing the the distance between the eyes and the smartphone screen. A prototype of iSee has been developed to evaluated the effectiveness under different environmental conditions.

As an objective reality, the social behavior pattern of Primary Users (PUs) has significant impacts on the design and management of the secondary network. However, most of the existing works overlook this fact by simplifying the spectrum whitespace assumption. In this paper, we study the joint routing and time-domain scheduling problem for Cognitive Radio Networks (CRNs) by considering the social behaviors of PUs. Our main contributions consist of four aspects. First, we analyze the social pattern of PUs based on two practical data traces. According to the obtained social pattern, the available spectrum whitespace is derived for SUs. Subsequently, in terms of previous analysis, we propose a centralized joint routing and time-domain scheduling framework with global provable ε-optimality (ε ∊ [0,1]) by employing the branch-and-bound technique, where ε indicates the expected closeness of our solution to the optimum solution. The solution of this centralized algorithm can serve as a theoretical benchmark for developing future routing and scheduling algorithms for CRNs. Third, we design a distributed primary behavior-aware routing and scheduling algorithm with local performance guarantee, where the routing and scheduling fairness, the available bandwidth, the potential interference, etc. are taken into account. Finally, simulation results confirm our assertion that primary behaviors have significant impacts on the spectrum whitespace, and demonstrate that primary-behavior-aware joint routing and scheduling design can utilize spectrum whitespace efficiently.

The existing works on analyzing/utilizing spectrum whitespace in Cognitive Radio Networks (CRNs) are either empirical studies lacking of theoretical guarantee, or local primary network information based inducing inaccurate analysis and estimation, or overlooking the spectrum whitespace details. Therefore, we propose to systematically analyze the spectrum whitespace in CRNs from a social network perspective. Our main contributions include four parts. First, we propose a novel metric named centrality score to measure the active weights of Primary Users (PUs) by considering each PU's topological importance in the primary network and the global primary network running and traffic information. Subsequently, based on the centrality scores of PUs, we derive the whitespace in CRNs under different social patterns of primary activities. Since we consider both the primary network topological structure and the global network running and traffic information, our whitespace analysis is more accurate compared with the existing works. Third, according to our whitespace analysis, we design a Virtual Backbone (VB) construction algorithm, which aims to improve the spectrum utilization efficiency in CRNs. Finally, we conduct extensive simulations to validate our whitespace analysis and the VB construction algorithm. The simulation results demonstrate that our social attributes based whitespace analysis can accurately characterize the whitespace in CRNs and the VB construction algorithm significantly improves the performance of the existing VB-based CRN protocols.

Social networks are important mediums for communication,information dissemination, and influence spreading. Most of existing works focus on understanding the characteristics of social networks or spreading information through the 'word of mouth' effect of social networks. However, motivated by applications of alleviating social problems, such as drinking, smoking, addicting to gaming, and influence spreading problems, such as promoting new products, we propose a new optimization problem named the Minimum-sized Influential Node Set (MINS) selection problem, which is to identify the minimum-sized set of influential nodes, such that every node in the network could be influenced by these selected nodes no less than a threshold. Our contributions are threefold. First, we prove that, under the independent cascade model, MINS is NP-hard. Subsequently, we present a greedy approximation algorithm to address the MINS selection problem. Moreover, the performance ratio of the greedy algorithm is analyzed. Finally, to validate the proposed greedy algorithm, extensive experiments and simulations are conducted both on real world coauthor data sets and random graphs.

A thorough understanding of capacity of wireless networks can help with effective design and efficient employment of wireless networks. Much effort has been spent on investigating capacity of multicast which is a popular communication model and generalization of unicast and broadcast. However, most previous works assume homogeneous traffic patterns, which is not meaningful for practical applications. This paper analyzes the capacity of wireless networks with multiple types of multicast sessions without the assumption of homogeneous traffic patterns. A new network model is proposed accommodating practical traffic patterns and the capacity is analyzed accordingly. A theoretical upper bound is derived, and a feasible transmission scheme with capacity lower bound is presented. Two bounds are asymptotically tight, that is, in the order of Θ(a2ns/∑i=1nsmin√ki⋅ Ri⋅ r,Ri2⋅ W), where a is the side length of the deployed region, r is the transmission range, ns is the number of multicast sessions, and ki and Ri are parameters of multicast session i. Furthermore, the variation of capacity towards different numbers of and distributions of destinations is illustrated.

Complicated collisions and spectrum uncertainty constrain the usage of Cognitive Radio Networks (CRNs) on heavy transmission and time sensitive applications. On the other hand, data aggregation has been considered as an essential operation in wireless networks. A large amount of effort has been dedicated to the investigation of CRNs and data aggregation in wireless networks. However, the existing literatures rarely concentrate on how to use cognitive radio technique to promote the performance of data aggregation in conventional wireless networks. In this paper, we investigate the Minimum Latency Data Aggregation Scheduling in wireless networks with Cognitive Radio capability (MLDAS-CR) problem. As the first try, an approximation scheduling algorithm based on Integer Linear Programming (ILP) and Linear Programming (LP) is proposed. According to the simulation results, this method performances great, however, it is difficult to theoretically evaluate the solution. Therefore, a heuristic scheduling algorithm with guaranteed latency bound is presented in our further investigation. The performance of the proposed solutions are evaluated through extensive simulations.

was held at the University of North Carolina at Charlotte (Charlotte, NC, USA) on May 20-22, 2013.For 9 years, the ISBRA symposium has been a forum for exchange of diverse ideas and research results broadly organized under the umbrella disciplines of bioinformatics and computational biology.The five keynote addresses at ISBRA 2013 included talks on RNA structure (Dr.Steve Harvey), computational behavioral ecology (Dr.Tanya Berger-Wolf), peptide identification from mass spectrometry (Dr.Bin Ma), gene regulation (Dr.Martha Bulyk), and biological network analysis (Dr.Luonan Chen).The research presented by conference participants was equally diverse.Reflecting current trends in the field, a substantial number of presentations were focused on genomics research, so we have chosen to present two linked supplements in BMC Bioinformatics, and one in BMC Genomics.ISBRA 2013 was attended by 115 participants from the US, Canada, China, southeast Asia, and several European nations.

The articles in this special section were presented at the Ninth International Symposium on Bioinformatics Research and Applications (ISBRA 2013), which was held at the University of North Carolina at Charlotte, NC.

Data collection is a common operation of Wireless Sensor Networks (WSNs), of which the performance can be measured by its achievable network capacity. Most existing works studying the network capacity issue are based on the unpractical model called deterministic network model. In this paper, a more reasonable model, probabilistic network model, is considered. For snapshot data collection, we propose a novel Cell-based Path Scheduling (CPS) algorithm that achieves capacity of $(\Omega ({1/ 5\omega \ln n} \cdot W))$ in the sense of the worst case and order-optimal capacity in the sense of expectation, where $(n)$ is the number of sensor nodes, $(\omega)$ is a constant, and $(W)$ is the data transmitting rate. For continuous data collection, we propose a Zone-based Pipeline Scheduling (ZPS) algorithm. ZPS significantly speeds up the continuous data collection process by forming a data transmission pipeline, and achieves a capacity gain of $(N \sqrt{n}/ \sqrt{\log n} \ln n)$ or $(n/ \log n \ln n)$ times better than the optimal capacity of the snapshot data collection scenario in order in the sense of the worst case, where $(N)$ is the number of snapshots in a continuous data collection task. The simulation results also validate that the proposed algorithms significantly improve network capacity compared with the existing works.

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. We study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects a snapshot of data to the base station in a distributed manner without the time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Furthermore, we extend ADDC to deal with the continuous data collection issue, and analyze the delay and capacity performances of ADDC for continuous data collection, which are also proven to be order-optimal. Finally, extensive simulation results indicate that ADDC can effectively accomplish a data collection task and significantly reduce data collection delay.

To observe the complicated physical world, the sensors in a network sense and sample the data from the physical world. Currently, most existing works use the Equi-Frequency Sampling (EFS) methods or EFS based methods for data acquisition. However, the accuracy of EFS and EFS based methods cannot be guaranteed in practice since the physical world keeps changing continuously, and these methods do not effectively support reconstruction of the monitored physical world. To overcome the shortages of EFS and EFS based methods, this paper focuses on designing physical-world-aware data acquisition algorithms to support O(ε)-approximation to the physical world for any ε ≥ 0. Two physical-world-aware data acquisition algorithms are proposed. Both algorithms can adjust the sensing frequency automatically based on the changing trend of the physical world and the given ε. The thorough analysis on the performances of the algorithms are also provided. It is proven that the error bounds of the algorithms are O(ε) and the complexities of the algorithms are O(1/(ε1/4)). Based on the new data acquisition algorithms, an algorithm for reconstructing the physical world is proposed and analyzed. The theoretical analysis and experimental results show that the proposed algorithms have high performances on the aspects of accuracy and energy consumption.

Cognitive Radio Networks (CRNs) have been proposed as a promising technology to alleviate the spectrum shortage and under-utilization problem. A large amount of research effort has been put on CRNs. However, little attention has been paid to data aggregation problem, one of the most important communication protocols in wireless networks due to its efficiency in both energy conservation and latency reduction. In this paper, we investigate the minimum latency data aggregation scheduling problem in CRNs. Considering the difficulty of this problem, two practical distributed algorithms under the Unit Disk Graph interference model and the Physical Interference Model are proposed. Extensive simulation results indicate that the proposed algorithms have good latency performance in various network scenarios.

Capacity scaling laws of wireless networks have attracted a lot of attention. In this paper, we study the multicast capacity of bus-assistant VANETs (vehicular ad hoc networks) with two-hop relay scheme, which has not been addressed before. Assume that n ordinary vehicles and nbbuses are deployed in a grid-like road framework while the number of roads increase linearly with n. All the ordinary vehicles obey the restricted mobility model. Thus, the spatial stationary distribution decays as power law with the distance from the centre spot (home-point) of a restrict region of each vehicle. All the buses deployed in all roads as intermediate nodes. They are used to forward packets for ordinary vehicles. Each ordinary vehicle randomly chooses k - 1 vehicles from the other ordinary vehicles as receivers. The packets could be transmitted directly from source to destination or be transmitted to an intermediate vehicle or bus, then be forwarded to the destination. We found that the social-proximity urban bus-assisted VANET has three routing methods. For each routing method, we derive the matching asymptotic upper and lower bounds of multicast capacity of bus-assisted VANET.

To observe the complicate physical world by a WSN, the sensors in the WSN senses and samples the data from the physical world. Currently, most of the existing work use equi-frequency sampling methods (EFS) or EFS based sampling methods for data acquisition in sensor networks. However, the accuracies of EFS and EFS based sampling methods cannot be guaranteed in practice since the physical world usually varies continuously, and these methods does not support reconstructing of the monitored physical world. To overcome the shortages of EFS and EFS based sampling methods, this paper focuses on designing physical-world-aware data acquisition algorithms to support O(ϵ)-approximation to the physical world for any ϵ ≥ 0. Two physical-world-aware data acquisition algorithms based on Hermit and Spline interpolation are proposed in the paper. Both algorithms can adjust the sensing frequency automatically based on the changing trend of the physical world and given c. The thorough analysis on the performance of the algorithms are also provided, including the accuracies, the smooth of the outputted curves, the error bounds for computing first and second derivatives, the number of the sampling times and complexities of the algorithms. It is proven that the error bounds of the algorithms are O(ϵ) and the complexities of the algorithms are O(1/ϵ1/4). Based on the new data acquisition algorithms, an algorithm for reconstructing physical world is also proposed and analyzed. The theoretical analysis and experimental results show that all the proposed algorithms have high performance in items of accuracy and energy consumption.

Routing is one of the most important and fundamental issues in Cognitive Radio Networks (CRNs). In this paper, we propose an effective routing scheme. Our main contributions are threefold. First, we propose a spectrum-aware Semi-Structure Routing (SSR) framework which incorporates power control. By employing forwarding zones and routing zones, SSR can effectively utilize the local real-time spectrum dynamics and meanwhile guarantee the global routing performance. Second, considering the lack of analytical models for routing protocol performance [1] in CRNs, we analyze the upper bound of the induced latency and scalability of SSR. Finally, extensive simulation results are presented to validate the performance of SSR.

Cognitive Radio Networks (CRNs) introduce a new communication paradigm which enable unlicensed users to opportunistically access spectrum bands assigned to licensed users. Interestingly, the broadcast problem, which is one of the most fundamental operations in CRNs, has not been well studied. Existing works for the broadcast issue in CRNs are either heuristic solutions without performance guarantee or with performance far from the optimal solution. In this paper, we study the Minimum-Latency Broadcast Scheduling (MLBS) issue for CRNs. Our contributions are threefold. Firstly, we propose a Mixed Broadcasting Scheduling (MBS) algorithm under the Unit Disk Graph (UDG) model, denoted by MBS-UDG. MBSUDG finishes a broadcast task by employing mixed unicast and broadcast communication modes in two phases. We show that the latency performance of MBS-UDG is O(ħ+ΔT) when ΔT≤ 1/p, or O(ħ+log1-p1/(pΔT)) when ΔT> 1/p, where ħ and ΔTare the height and the maximum number of leaf nodes connected by a SU of the broadcasting tree, respectively, and p is the spectrum opportunity for a secondary communication. Furthermore, the redundancy performance of MBS-UDG is analyzed. Secondly, we extend MBS-UDG to a more general MBS algorithm under the protocol interference model and analyze its latency and redundancy performance. Finally, simulations are conducted to validate MBS, which indicate that MBS significantly improves existing algorithms with respect to both latency and redundancy.

Wireless sensor networks (WSNs) are more likely to be d-pistributed asynchronous systems. In this paper, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs. Our main contributions include five aspects. First, to avoid data transmission interference, we derive an ℜ0-proper carrier-sensing range (ℜ0-PCR) under the generalized physical interference model, where ℜ0is the satisfied threshold of data receiving rate. Taking ℜ0-PCR as its carrier-sensing range, any sensor node can initiate a data transmission with a guaranteed data receiving rate. Second, based on ℜ0-PCR, we propose a Distributed Data Collection (DDC) algorithm with fairness consideration. Theoretical analysis of DDC surprisingly shows that its achievable network capacity is order-optimal and independent of network size. Thus, DDC is scalable. Third, we discuss how to apply ℜ0-PCR to the distributed data aggregation problem and propose a Distributed Data Aggregation (DDA) algorithm. The delay performance of DDA is also analyzed. Fourth, to be more general, we study the delay and capacity of DDC and DDA under the Poisson node distribution model. The analysis demonstrates that DDC is also scalable and order-optimal under the Poisson distribution model. Finally, we conduct extensive simulations to validate the performance of DDC and DDA.

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. First, we study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects data of a snapshot to the base station in a distributed manner without any time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Finally, extensive simulation results indicate that ADDC can effectively finish a data collection task and significantly reduce data collection delay.

Data collection is a common operation of Wireless Sensor Networks (WSNs). The performance of data collection can be measured by its achievable network capacity. Most of the current works on the network capacity issue are based on the deterministic network model, which is not practical for real applications due to the “transitional region phenomenon” [22]. The probabilistic network model is actually a more practical one. In this paper, we investigate the achievable Snapshot/Continuous Data Collection (SDC/CDC) capacity for WSNs under the probabilistic network model. For SDC, we propose a novel Cell-based Multi-Path Scheduling (CMPS) algorithm, whose achievable network capacity is Ω(po/3ω · W) in the worst case and Ω(po/ω · W) in the average case, where pois the promising transmission threshold probability, ω is a constant, and W is the data transmitting rate over a wireless channel, i.e. the channel bandwidth, which are both order-optimal. For CDC, we propose a Zone-based Pipeline Scheduling (ZPS) algorithm. ZPS significantly speeds up the data collection process and achieves surprising network capacities for both the worst case and the average case. The simulation results also validate that the proposed algorithms significantly improve network capacity compared with the existing works.

Most of the existing works studying the data collection capacity issue have an ideal assumption that the network time is slotted and the entire network is strictly synchronized explicitly or implicitly. Such an assumption is mainly for centralized synchronous WSNs. However, WSNs are more likely to be distributed asynchronous systems. Thus, in this paper, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs. To the best of our knowledge, this is the first work to study the data collection capacity issue for distributed asynchronous WSNs. Our main contributions are threefold. First, to avoid data transmission collisions/interference, we derive an R0-Proper Carrier-sensing Range (R0-PCR) under the generalized physical interference model for the nodes in a data collection WSN, where R0is the satisfied threshold data receiving rate. Taking R0-PCR as its carrier-sensing range, any node can initiate a data transmission with a guaranteed data receiving rate. Second, based on the obtained R0-PCR, we propose a Distributed Data Collection (DDC) algorithm with fairness consideration for asynchronous WSNs. Theoretical analysis of DDC surprisingly shows that its asymptotic achievable network capacity is ℂ = Ω(1/(26βκ+1)·W), where βκ+1is a constant value depends on R0and W is the bandwidth of a wireless communication channel, which is order optimal and independent of network size. Thus, DDC is scalable. Finally, we conduct extensive simulations to validate the performance of DDC. Simulation results demonstrate that DDC can achieve comparable data collection capacity as that of the most recently published centralized and synchronized data collection algorithm.

The performance of data collection in Wireless Sensor Networks (WSNs) can be measured by network capacity. However, few existing works dedicatedly consider the Continuous Data Collection (CDC) capacity for WSNs under the protocol interference model. In this paper, we propose a multipath scheduling algorithm for SDC in single-radio multichannel WSNs and derive its network capacity which is a tighter lower bound compared with the previously best result [CHECK END OF SENTENCE]. We also propose a novel CDC method for dual-radio multichannel WSNs. It significantly speeds up the data collection process, and achieves a capacity of (nW/12M⌈(3.63ρ2+c3ρ+c4)/H⌉) when Δe≤ 12 or (nW/MΔe⌈(3.63ρ2+c3ρ+c4)/H⌉) when Δe>;12, where n is the number of the sensors, M is a constant value and usually M ≪ n, Δeis the maximum number of the leaf nodes having a same parent in the data collection tree, W is the channel bandwidth, H is the number of available orthogonal channels, \rho is the ratio of the interference radius over the transmission radius, c3= (8π/√(3)) + π + 2, and c4= (8π/√(3)) + 2π + 6. Extensive simulation results indicate that the proposed algorithms improve network capacity significantly compared with existing works.

Network wide broadcast is a fundamental and widely-used operation in multi-hop wireless networks. Through one-to-all broadcast, a source node disseminates a message to all the other nodes in the network. In duty-cycle scenarios, the nodes alternate between active and sleep states, which results in the increasing of broadcast latency. In this paper, we investigate the Minimum Latency Broadcast Scheduling problem in Duty-Cycle (MLBSDC problem) multi-hop wireless networks. Our goal is to provide a generalized framework for the MLBSDC problem. We mathematically remodel this problem and propose a vector iteration algorithm, in which a greedy strategy is employed. We also conduct extensive simulations to show the efficiency of the broadcast schedule returned by our algorithm under various network configurations. Compared with the results of the current best algorithm, our greedy algorithm achieves significant improvement both in broadcast latency and the total number of transmissions.

Modern biological applications usually involve the similarity comparison between two objects, which is often computationally very expensive, such as whole genome pairwise alignment and protein 3D structure alignment. Nevertheless, being able to quickly identify the closest neighboring objects from very large databases for a newly obtained sequence or structure can provide timely hints to its functions and more. This paper presents a substantial speedup technique for the well-studied k-nearest neighbor (k-nn) search, based on novel concepts of virtual pivots and partial pivots, such that a significant number of the expensive distance computations can be avoided. The new method is able to dynamically locate virtual pivots, according to the query, with increasing pruning ability. Using the same or less amount of database preprocessing effort, the new method outperformed the second best method by using no more than 40 percent distance computations per query, on a database of 10,000 gene sequences, compared to several best known k-nn search methods including M-Tree, OMNI, SA-Tree, and LAESA. We demonstrated the use of this method on two biological sequence data sets, one of which is for HIV-1 viral strain computational genotyping.

Data mining in wireless sensor networks (WSNs) is a new emerging research area. This paper investigates the problem of real time clustering of sensory data in WSNs. The objective is to cluster the data collected by sensor nodes in real time according to data similarity in a d-dimensional sensory data space. To perform in-network data clustering efficiently, a Hilbert Curves based mapping algorithm, HilbertMap, is proposed to convert a d-dimensional sensory data space into a two-dimensional area covered by a sensor network. Based on this mapping, a distributed algorithm for clustering sensory data, H-Cluster, is proposed. It guarantees that the communications for sensory data clustering mostly occur among geographically nearby sensor nodes and sensory data clustering is accomplished in in-network manner. Extensive simulation experiments were conducted using both real-world datasets and synthetic datasets to evaluate the algorithms. H-Cluster consistently achieves the lowest data loss rate, the highest energy efficiency, and the best clustering quality.

Area query processing is significant for various applications of wireless sensor networks. No previous study has specifically addressed this issue. We can adopt a naive method, which is to send all data to base station for centralized processing. However, this method wastes a large amount of energy for reporting useless data. This motivates us to propose an energy-efficient in-network area query processing scheme. In our scheme, the whole monitored area is partitioned into grids, and a gray code is used to represent a grid ID (GID), which is a smart way to describe an area. Furthermore, a reporting tree is constructed to process merging areas and aggregations. Based on the properties of GIDs, useless data can be dropped and areas can be merged as early as possible. Incremental update is used to continuously generate query results. In essence, all of these strategies are pivots to conserve energy consumption. With a thorough simulation study, it is shown that our scheme is energy-efficient.

BACKGROUND: The "common disease--common variant" hypothesis and genome-wide association studies have achieved numerous successes in the last three years, particularly in genetic mapping in human diseases. Nevertheless, the power of the association study methods are still low, in particular on quantitative traits, and the description of the full allelic spectrum is deemed still far from reach. Given increasing density of single nucleotide polymorphisms available and suggested by the block-like structure of the human genome, a popular and prosperous strategy is to use haplotypes to try to capture the correlation structure of SNPs in regions of little recombination. The key to the success of this strategy is thus the ability to unambiguously determine the haplotype allele sharing status among the members. The association studies based on haplotype sharing status would have significantly reduced degrees of freedom and be able to capture the combined effects of tightly linked causal variants. RESULTS: For pedigree genotype datasets of medium density of SNPs, we present two methods for haplotype allele sharing status determination among the pedigree members. Extensive simulation study showed that both methods performed nearly perfectly on breakpoint discovery, mutation haplotype allele discovery, and shared chromosomal region discovery. CONCLUSION: For pedigree genotype datasets, the haplotype allele sharing status among the members can be deterministically, efficiently, and accurately determined, even for very small pedigrees. Given their excellent performance, the presented haplotype allele sharing status determination programs can be useful in many downstream applications including haplotype based association studies.

BACKGROUND: With the increasing availability of whole genome sequences, it is becoming more and more important to use complete genome sequences for inferring species phylogenies. We developed a new tool ComPhy, 'Composite Distance Phylogeny', based on a composite distance matrix calculated from the comparison of complete gene sets between genome pairs to produce a prokaryotic phylogeny. RESULTS: The composite distance between two genomes is defined by three components: Gene Dispersion Distance (GDD), Genome Breakpoint Distance (GBD) and Gene Content Distance (GCD). GDD quantifies the dispersion of orthologous genes along the genomic coordinates from one genome to another; GBD measures the shared breakpoints between two genomes; GCD measures the level of shared orthologs between two genomes. The phylogenetic tree is constructed from the composite distance matrix using a neighbor joining method. We tested our method on 9 datasets from 398 completely sequenced prokaryotic genomes. We have achieved above 90% agreement in quartet topologies between the tree created by our method and the tree from the Bergey's taxonomy. In comparison to several other phylogenetic analysis methods, our method showed consistently better performance. CONCLUSION: ComPhy is a fast and robust tool for genome-wide inference of evolutionary relationship among genomes. It can be downloaded from http://digbio.missouri.edu/ComPhy.

BACKGROUND: Serotypes of the Foot-and-Mouth disease viruses (FMDVs) were generally determined by biological experiments. The computational genotyping is not well studied even with the availability of whole viral genomes, due to uneven evolution among genes as well as frequent genetic recombination. Naively using sequence comparison for genotyping is only able to achieve a limited extent of success. RESULTS: We used 129 FMDV strains with known serotype as training strains to select as many as 140 most serotype-specific nucleotide strings. We then constructed a linear-kernel Support Vector Machine classifier using these 140 strings. Under the leave-one-out cross validation scheme, this classifier was able to assign correct serotype to 127 of these 129 strains, achieving 98.45% accuracy. It also assigned serotype correctly to an independent test set of 83 other FMDV strains downloaded separately from NCBI GenBank. CONCLUSION: Computational genotyping is much faster and much cheaper than the wet-lab based biological experiments, upon the availability of the detailed molecular sequences. The high accuracy of our proposed method suggests the potential of utilizing a few signature nucleotide strings instead of whole genomes to determine the serotypes of novel FMDV strains.

MOTIVATION: The availability of the whole genomic sequences of HIV-1 viruses provides an excellent resource for studying the HIV-1 phylogenies using all the genetic materials. However, such huge volumes of data create computational challenges in both memory consumption and CPU usage. RESULTS: We propose the complete composition vector representation for an HIV-1 strain, and a string scoring method to extract the nucleotide composition strings that contain the richest evolutionary information for phylogenetic analysis. In this way, a large-scale whole genome phylogenetic analysis for thousands of strains can be done both efficiently and effectively. By using 42 carefully curated strains as references, we apply our method to subtype 1156 HIV-1 strains (10.5 million nucleotides in total), which include 825 pure subtype strains and 331 recombinants. Our results show that our nucleotide composition string selection scheme is computationally efficient, and is able to define both pure subtypes and recombinant forms for HIV-1 strains using the 5000 top ranked nucleotide strings. AVAILABILITY: The Java executable and the HIV-1 datasets are accessible through 'http://www.cs.ualberta.ca/~ghlin/src/WebTools/hiv.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker identification, which are expected to be closely related to the disease. A most important task of these identified genes is that they can be used to construct a classifier which can effectively diagnose disease and even recognize the disease subtypes. Binary classification, for example, diseased or healthy, in microarray data analysis has been successful, while multi-class classification, such as cancer subtyping, remains challenging. RESULTS: We target on the challenging multi-class classification in microarray data analysis, especially on the cancer subtyping using gene expression microarray. We present a novel class discrimination strength vector to represent individual genes and introduce a new measurement to quantify the class discrimination strength difference between two genes. Such a new distance measure is employed in gene clustering, and subsequently the gene cluster information is exploited to select a set of genes which can be used to construct a sample classifier. We tested our method on four real cancer microarray datasets each contains multiple subtypes of cancer patients. The experimental results show that the constructed classifiers all achieved a higher classification accuracy than the previously best classification results obtained on these four datasets. Additional tests show that the selected genes by our method are less correlated and they all contribute statistically significantly to the more accurate cancer subtyping. CONCLUSION: The proposed novel class discrimination strength vector is a better representation than the gene expression vector, in the sense that it can be used to effectively eliminate highly correlated but redundant genes for classifier construction. Such a method can build a classifier to achieve a higher classification accuracy, which is demonstrated via cancer subtyping.

A single DNA microarray measures thousands to tens of thousands of gene expression levels, but experimental datasets normally consist of much fewer such arrays, typically in tens to hundreds, taken over a selection of tissue samples. The biological interpretation of these data relies on identifying subsets of induced or repressed genes that can be used to discriminate various categories of tissue, to provide experimental evidence for connections between a subset of genes and the tissue pathology. A variety of methods can be used to identify discriminatory gene subsets, which can be ranked by classification accuracy. But the high dimensionality of the gene expression space, coupled with relatively fewer tissue samples, creates the dimensionality problem: gene subsets that are too large to provide convincing evidence for any plausible causal connection between that gene subset and the tissue pathology. We propose a new gene selection method, clustered gene selection (CGS) which, when coupled with existing methods, can identify gene subsets that overcome the dimensionality problem and improve classification accuracy. Experiments on eight real datasets showed that CGS can identify many more cancer related genes and clearly improve classification accuracy, compared with three other non-CGS based gene selection methods

Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most significantly differentially expressed genes under different categories of conditions, is both computationally and biologically interesting, and has become a central research focus in all studies that use gene expression microarray technology. Despite many existing efforts, better gene selection methods that can effectively identify biologically significant biomarkers, yet computationally efficient, are still in need. In this paper, a model-free greedy (MFG) gene selection method is proposed, which implements several intuitive heuristics but doesn't assume any statistical distribution on the expression data. The experimental results on three real microarray datasets showed that the MFG method combined with a support vector machine (SVM) classifier or a k-nearest neighbor (KNN) classifier is efficient and robust in identifying discriminatory genes

BACKGROUND: Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot. RESULTS: Two novel gene selection methods are proposed in this paper, which are not affected by the unbalanced sample class sizes and do not assume any explicit statistical model on the gene expression values. They were evaluated on eight publicly available microarray datasets, using leave-one-out cross-validation and 5-fold cross-validation. The performance is measured by the classification accuracies using the top ranked genes based on the training datasets. CONCLUSION: The experimental results showed that the proposed gene selection methods are efficient, effective, and robust in identifying differentially expressed genes. Adopting the existing SVM-based and KNN-based classifiers, the selected genes by our proposed methods in general give more accurate classification results, typically when the sample class sizes in the training dataset are unbalanced.

Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Detecting the most significantly differentially expressed genes under different conditions, or gene selection, has been a central focus for researchers. The gene selection problem becomes more difficult when the numbers of samples under different conditions vary significantly, or are unbalanced. A novel model-free and stable gene selection method is proposed in this paper, i.e., the method does not assume any statistical model on the gene expression data and it is not affected by the unbalanced samples. The method has been evaluated on two publicly available datasets, the leukemia dataset and the small round blue cell tumor dataset, where the experimental results showed that the proposed method is efficient and robust in identifying differentially expressed genes.