Automated radiology report generation (R2Gen) has advanced significantly, yet evaluation remains challenging due to the complexity of assessing report quality. Traditional metrics often misalign with human judgments, failing to identify specific deficiencies. To address this, we introduce ReFINE, a framework for training an Evaluation Model using a novel margin-based reward enforcement loss. This approach decomposes report quality into fine-grained sub-scores across user-defined criteria, improving interpretability. Leveraging GPT-4, we generate diverse training data with paired accepted and rejected reports to train our model under a reward-based system. The trained ReFINE Score provides both granular sub-scores and an aggregated quality assessment, enabling criterion-specific evaluation. Experimental results demonstrate ReFINE's superior alignment with human judgments, outperforming traditional metrics in model selection. Its robustness is validated across three expert-annotated datasets—including chest X-rays and multimodal reports covering 9 imaging modalities—and under two distinct scoring systems.

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.

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%.

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.

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.

Lung cancer remains the leading cause of cancer-related deaths worldwide, mainly due to late diagnosis and limited availability of expert pathologists. Although Artificial Intelligence (AI) and DT technologies offer promising avenues for early detection, their adoption in clinical settings introduces significant concerns around data security, system vulnerabilities, and model trust. In response, this paper proposes a novel, trusted medical AI framework that combines blockchain-based device authentication, explainable artificial intelligence (XAI), and DT technologies to enhance diagnostic accuracy, data integrity, and system resilience. The proposed system leverages ResNet for lung condition classification from CT scans, augmented by Grad-CAM for visual explainability, enabling clinicians to interpret AI-driven decisions confidently. A blockchain-based whitelist mechanism authenticates medical devices before data contribution, mitigating risks of tampered or unverified input. Furthermore, the framework integrates explainable digital twin visualization to simulate patient-specific predictions and embeds a vulnerability detection layer to identify and mitigate software flaws in medical IoT and DT components. This comprehensive solution addresses key challenges in real-world healthcare: ensuring data authenticity, interpretability, and cyber resilience, paving the way for secure, transparent, and trustworthy AI-driven diagnostics.

The increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis. Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare systems. Traditional centralized methods of medical image analysis pose significant risks to patient privacy and data security, as they require the aggregation of sensitive information in a single location. Furthermore, these methods often suffer from limitations related to data diversity and scalability, hindering the development of universally robust diagnostic models. Recent advancements in machine learning, particularly deep learning, have shown promise in enhancing medical image analysis. However, the need to access large and diverse datasets for training these models introduces challenges in maintaining patient confidentiality and adhering to strict data protection regulations. This paper introduces FedViTBloc, a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning (FL) combined with Vision Transformers (ViT) and blockchain technology. The proposed system ensures patient data privacy and security through fully homomorphic encryption and differential privacy techniques. By employing a decentralized FL approach, multiple medical institutions can collaboratively train a robust deep-learning model without sharing raw data. Blockchain integration further enhances the security and trustworthiness of the FL process by managing client registration and ensuring secure onboarding of participants. Experimental results demonstrate the effectiveness of FedViTBloc in medical image analysis while maintaining stringent privacy standards, achieving 67% accuracy and reducing loss below 2 across 10 clients, ensuring scalability and robustness.

In recent research on the Digital Twin-based Vehicular Ad hoc Network (DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy. However, Federated learning struggles to adequately train a global model when confronted with data heterogeneity and data sparsity among vehicles, which ensure suboptimal accuracy in making precise predictions for different vehicle types. To address these challenges, this paper combines Federated Transfer Learning (FTL) to conduct vehicle clustering related to types of vehicles and proposes a novel Hierarchical Federated Transfer Learning (HFTL). We construct a framework for DT-VANET, along with two algorithms designed for cloud server model updates and intra-cluster federated transfer learning, to improve the accuracy of the global model. In addition, we developed a data quality score-based mechanism to prevent the global model from being affected by malicious vehicles. Lastly, detailed experiments on real-world datasets are conducted, considering different performance metrics that verify the effectiveness and efficiency of our algorithm.

In the advancement of collaborative intelligence within the Internet of Things (IoT), federated learning (FL) enables clients to collaboratively train a global model without centralizing raw data. However, the non-independent and identically distributed (non-IID) nature of data among clients often leads to divergent local training objectives, deteriorating the performance of the aggregated global model. To address this challenge, we propose AP-CFL, a novel clustered FL algorithm that incorporates affinity propagation to dynamically discover the clustering structure of clients without the need to predefine the number of clusters. Specifically, AP-CFL calculates the mean of absolute differences of pairwise cosine similarity to effectively cluster clients based on similarities in their data distributions. Knowledge sharing is enhanced by decoupling each cluster model into a globally shared encoder and a cluster-specific classifier, and the local training objectives are modified to improve the generalization capacity of the shared encoder. Additionally, a robust strategy is introduced to manage partial client participation by employing a time and data importance index, which mitigates the adverse effects of model staleness and maintains the integrity of the clustering structure. Extensive experiments on diverse real-world datasets demonstrate that AP-CFL outperforms existing FL baselines in non-IID settings, effectively improving model quality and convergence stability.

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.

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.

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.

Despite considerable advancements in specialized hardware, the majority of IoT edge devices still rely on CPUs. The burgeoning number of IoT users amplifies the challenges associated with performing multiple Deep Neural Network inferences on these resource-limited, CPU-only edge devices. Existing strategies, including model compression, hardware acceleration, and model partitioning, often involve a trade-off in inference accuracy, are unsuitable due to hardware specificity, or lead to inefficient resource utilization. In response to these challenges, this paper introduces L-PIC (Latency Minimized Parallel Inference on CPU)—a framework expressly devised to optimize resource allocation, decrease inference latency, and maintain result accuracy on CPU-only edge devices. A series of comprehensive experiments have verified the superior efficiency and effectiveness of the L-PIC framework in comparison to the state-of-the-art method. Remarkably, compared to the state-of-the-art method, L-PIC can reduce the inference latency of multi-DNN by an average of approximately 30% across all tested scenarios.

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.

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.

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.

As one of the most promising machine learning frameworks emerging in recent years, Federated learning (FL) has received lots of attention. The main idea of centralized FL is to train a global model by aggregating local model parameters and maintain the private data of users locally. However, recent studies have shown that traditional centralized federated learning is vulnerable to various attacks, such as gradient attacks, where a malicious server collects local model gradients and uses them to recover the private data stored on the client. In this paper, we propose a DEcentralized FEderated learning Against aTtacks (DEFEAT) framework and use it to defend the gradient attack. The decentralized structure adopted by this paper uses a peer-to-peer network to transmit, aggregate, and update local models. In DEFEAT, the participating clients only need to communicate with their single-hop neighbors to learn the global model, in which the model accuracy and communication cost during the training process of DEFEAT are well balanced. Through a series of experiments and detailed case studies on real datasets, we evauate the excellent model performance of DEFEAT and the privacy preservation capability against gradient attacks.

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.

COVID-19 vaccine distribution route directly impacts the community's mortality and infection rate. Therefore, optimal vaccination dissemination would appreciably lower the death and infection rates. This paper proposes the Epidemic Vulnerability Index (EVI) that quantitatively evaluates the subject's potential risk. Our primary aim for the suggested index is to diminish both infection rate and death rate efficiently. EVI was accordingly designed with clinical factors determining the mortality and social factors incorporating the infection rate. Through statistical COVID-19 patient dataset analysis and social network analysis with an agent-based model that is analogous to a real-world system, we define and experimentally validate the capability of EVI. Our experiments consist of nine vaccination distribution scenarios, including existing indexes which estimate the risk and stochastically proliferate the contagion and vaccine in a 300,000 agent-based graph network. We compared the outcome and variation of the three metrics in the experiments: infection case, death case, and death rate. Through this assessment, vaccination by the descending order of EVI has shown to have a significant outcome with an average of 5.0% lower infection cases, 9.4% lower death cases, and 3.5% lower death rate than other vaccine distribution routes.

With the development of mobile networks and intelligent equipment, as a new intelligent data sensing paradigm in large-scale sensor applications such as the industrial Internet of Things, mobile crowd sensing (MCS) assigns industrial sensing tasks to workers for data collection and sharing, which has created a bright future for building a strong industrial system and improving industrial services. How to design an effective worker selection mechanism to maximize the utility of crowdsourcing is the research hotspot of mobile sensing technologies. This article studies the problem of least workers selection to make large MCS system perform sensing tasks more effective and achieve certain coverage with certain constraints being meeting. A many-objective worker selection method is proposed to achieve the desired tradeoff and an optimization mechanism is designed based on the enhanced differential evolution algorithm to ensure data integrity and search solution optimality. The effectiveness of the proposed method is verified through a large scale of experimental evaluation datasets collected from real world.

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.

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.

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.

The increasing number of products is the trend of current industry. However, the product development process is significantly limited by budget and testing risk. Recently, digital twin (DT) has emerged as a promising industrial paradigm that provides an integrated and cohesive view of the product design process. In this article, we propose a product design framework for Internet of Things (IoT) platforms, namely, DT-aided IoT platform design (DTIPD). This framework considers a large number of IoT devices performing different tasks with machine learning (ML) technologies. Each IoT device constructs a particular ML-based model that deals with its task automatically by feeding related data with labels. The challenges of large-scale network management and ground-truth shortage at the initial stage of product iteration are addressed. We propose a two-level hierarchical learning process using the real-time model status stored at DT servers (DTS), aiming to improve product quality while shortening the development lifecycle. The comprehensive experimental results for both the single-DTS and multiple-DTS scenarios demonstrate the applicability of our framework.

To break through the limitation of battery-powered wireless sensor networks, a novel kind of network, named battery-free wireless sensor network (BF-WSN), is proposed. Battery-free sensor nodes in BF-WSNs harvest energy from power sources in their ambient environment, such as solar power, wind power and radio frequency (RF) signal power,etc., instead of batteries. Therefore, the energy consumption of battery-free sensor nodes are not limited by the battery capacity anymore. However, they still have limited energy harvesting rates and energy capacities. Data aggregation is a fundamental operation in sensor networks where the sensory data gathered by the relay nodes can be merged by in-network computation, such as taking the maximum, average, or sum, etc., of them. Due to the energy features of BF-WSNs, the data aggregation scheduling problem in BF-WSNs is more complicated and the previous aggregation scheduling algorithms designed for battery-powered WSNs are no longer applicable. This paper investigates the Minimum-Latency Aggregation Scheduling problem in BF-WSNs, which is proved to be NP-hard. Then, we propose the Data Aggregation Scheduling algorithm to solve the problem. Finally, the theoretical analysis and extensive simulation results are provided to verify the performance of the proposed algorithm.

With the advent of the Internet of Things (IoT) era, various application requirements have put forward higher requirements for data transmission bandwidth and real‐time data processing. Mobile edge computing (MEC) can greatly alleviate the pressure on network bandwidth and improve the response speed by effectively using the device resources of mobile edge. Research on mobile crowdsourcing in edge computing has become a hot spot. Hence, we studied resource utilization issues between edge mobile devices, namely, crowdsourcing scenarios in mobile edge computing. We aimed to design an incentive mechanism to ensure the long‐term participation of users and high quality of tasks. This paper designs a long‐term incentive mechanism based on game theory. The long‐term incentive mechanism is to encourage participants to provide long‐term and continuous quality data for mobile crowdsourcing systems. The multistrategy repeated game‐based incentive mechanism (MSRG incentive mechanism) is proposed to guide participants to provide long‐term participation and high‐quality data. The proposed mechanism regards the interaction between the worker and the requester as a repeated game and obtains a long‐term incentive based on the historical information and discount factor. In addition, the evolutionary game theory and the Wright‐Fisher model in biology are used to analyze the evolution of participants’ strategies. The optimal discount factor is found within the range of discount factors based on repeated games. Finally, simulation experiments verify the existing crowdsourcing dilemma and the effectiveness of the incentive mechanism. The results show that the proposed MSRG incentive mechanism has a long‐term incentive effect for participants in mobile crowdsourcing systems.

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.

In the participatory sensing framework, privacy protection of the Internet of Things (IoT) is very important. In this article, cryptography-based methods are utilized to protect participants' privacy information in unsecured network channels for dynamic and real-time sensing tasks. The edge computing paradigm is introduced in the traditional participatory sensing framework to reduce network latency. Then, the Rivest Cipher 4 stream cipher and logistic mapping are combined to deal with the problems of participants' limited resources and untruthful third-party platforms. Finally, the product algebra and logistic mapping are combined to deal with the problems of large numbers of participants' access and poor randomness of keystream. Through extensive performance evaluation and comparison experiments on the real-world data, the effectiveness and adaptation of the proposed privacy protection based on stream cipher are verified. It could effectively solve the problem of poor network latency and improve the privacy protection level of IoT.

The rapid growth of DNA-sequencing technologies motivates more personalized and predictive genetic-oriented services, which further attract individuals to increasingly release their genome information to learn about personalized medicines, disease predispositions, genetic compatibilities, etc. Individual genome information is notoriously privacy-sensitive and highly associated with relatives. In this paper, we present an inference attack algorithm to predict target genotypes and phenotypes based on belief propagation in factor graphs. With this algorithm, an attacker can effectively predict the target genotypes and phenotypes of target individuals based on genome information shared by individuals or their relatives, and genotype and phenotype association from genome-wide association study (GWAS). To address the privacy threats resulted from such inference attacks, we elaborate the metrics to evaluate data utility and privacy and then present a data sanitization method. We evaluate our inference attack algorithm and data sanitization method on real GWAS dataset: Age-related macular degeneration (AMD) case/control dataset. The evaluation results show that our work can effectively defense against genome threats while guaranteeing data utility.

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.

Beaconing is a fundamental networking service where each node broadcasts a packet to all its neighbors locally. Unfortunately, the problem Minimum Latency Beaconing Schedule (MLBS) in duty-cycled scenarios is not well studied. Existing works always have rigid assumption that each node is only active once per working cycle. Aiming at making the work more practical and general, MLBS problem in duty-cycled network where each node is allowed to active multiple times in each working cycle (MLBSDCA for short) is investigated in this paper. First, a novel kind of coloring problem, named as label coloring problem, is identified and analyzed. Second, an edge-based scheduling framework is designed and the MLBSDCA under protocol interference model is transformed to such coloring problem. Based on label coloring, a group first-fit scheduling algorithm is designed for MLBSDCA under protocol interference model. After that, a (ρ + 1)2|W|-approximation algorithm is proposed to further reduce the beaconing latency, where p denotes the interference radius, and |W| is the maximum number of active time slots per working cycle. When p and |W| is equal to 1, the approximation ratio is only 4, which is better than the one (i.e., 10) in existing works. Furthermore, two approximation algorithms for MLBSDCA under physical interference model are also investigated. The theoretical analysis and experimental results demonstrate the efficiency of the proposed algorithms in term of latency.

The lifetime of battery-powered Wireless Sensor Networks (WSNs) are limited by the batteries equipped in sensors. The appearance of Battery-free Wireless Sensor Networks (BF-WSNs) breaks through this limitation, in which battery-free sensors harvest energy from sustainable but uncontrollable energy sources in ambient environment, such as solar power, wind power, radio frequency signal power, and so on. The energy characteristics of BF-WSNs make it more challenging for data collection scheduling in BF-WSNs. Latency of data collection is a crucial measurement to evaluate the performance of data collection schedules. In this article, we study the problem of generating data collection schedules with minimum latency for BF-WSNs and propose latency-efficient data collection scheduling algorithms for line BF-WSNs and general BF-WSNs, respectively. Theoretical analysis and extensive simulations are conducted to verify the efficiency and effectiveness of the proposed algorithms.

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.

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.

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.

Battery-Free Wireless Sensor Networks (BF-WSNs) are newly emerging Wireless Sensor Networks (WSNs) to break through the energy limitations of traditional WSNs. In BF-WSNs, the broadcast scheduling problem is more challenging than that in traditional WSNs. This article investigates the broadcast scheduling problem in BF-WSNs with the purpose of minimizing broadcast latency. The Minimum-Latency Broadcast Scheduling problem in BF-WSNs (MLBS-BF) is formally defined and its NP-hardness is proved. Three approximation algorithms for solving the MLBS-BF problem are proposed. The broadcast latency of the broadcast schedules produced by the proposed algorithms is analyzed. The correctness and approximation ratio of the proposed algorithms are also proved. Finally, extensive simulations are conducted to evaluate the performances of the proposed algorithms. The simulation results show that the proposed algorithms have high performance.

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.

Over years, many efforts are made for the problem of constructing quality fault-tolerant virtual backbones in wireless network. In case that a wireless network consists of physically equivalent nodes, e.g., with the same communication range, unit disk graph (UDG) is widely used to abstract the wireless network and the problem is formulated as the minimum k-connected m-dominating set problem on the UDG. So far, most results are focused on designing a constant factor approximation algorithm for this NP-hard problem under two positive integers k and m satisfying m ≥ k ≥ 1 and k ≤ 3. This paper introduces an approximation algorithm for the problem with m ≥ k ≥ 1. This algorithm is simple to implement; it connects the components by adding a bounded number of paths, which first computes a 1-connected m-dominating set D and repeats the following steps: (a) search the separators arbitrarily in (i - 1, m)-CDS with i = 2, 3, ⋯ , k, (b) add a bounded number of paths connecting the components separated by separators in (i-1, m)-CDS to improve the connectivity of (i-1, m)-CDS, until it becomes k-connected, and (c) remove redundant paths if there exist at every iteration. We provide a rigorous theoretical analysis to prove that the proposed algorithm is correct and its approximation ratio is a constant, for any fixed k.

This paper studies a novel approach to processing massive uncertain graph data. In this approach, we propose a new framework to simultaneously process a query on a set of randomly sampled possible worlds of an uncertain graph. Based on this framework, we develop a series of algorithms to analyze massive uncertain graphs, including breadth-first search, shortest distance queries, triangle counting, and core decomposition. We implement this approach based on GraphLab, one of the stateof-the-art graph processing frameworks. By sharing fine-grained internal processing steps on common substructures of sampled possible worlds, the new approach achieves tens to hundreds of times speedup in execution time on a cluster of 20 servers.

Beaconing is a fundamental networking service where each node broadcasts a packet to all its neighbors locally. Unfortunately, the problem Minimum Latency Beaconing Schedule (MLBS) in duty-cycled scenarios is not well studied. Existing works always have rigid assumption that each node is only active once per working cycle. Aiming at making the work more practical and general, MLBS problem in duty-cycled network where each node is allowed to active multiple times in each working cycle (MLBSDCA for short) is investigated in this paper. Firstly, a modified first-fit coloring based algorithm is proposed for MLBSDCA under protocol interference model. After that, a (ρ + 1)2*|W|-approximation algorithm is proposed to further reduce the beaconing latency, where ρ denotes the interference radius, and |W| is the maximum number of active time slots per working cycle. When ρ and |W| is equal to 1, the approximation ratio is only 4, which is better than the one (i.e., 10) in existing works. Furthermore, two approximation algorithms for MLBSDCA under physical interference model are also investigated. The theoretical analysis and experimental results demonstrate the efficiency of the proposed algorithms in term of latency.

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.

With the proliferation of smartphones and the usage of the smartphone apps, privacy preservation has become an important issue. The existing privacy preservation approaches for smartphones usually have less efficiency due to the absent consideration of the active defense policies and temporal correlations between contexts related to users. In this paper, through modeling the temporal correlations among contexts, we formalize the privacy preservation problem to an optimization problem and prove its correctness and the optimality through theoretical analysis. To further speed up the running time, we transform the original optimization problem to an approximate optimal problem, a linear programming problem. By resolving the linear programming problem, an efficient context-aware privacy preserving algorithm (CAPP) is designed, which adopts active defense policy and decides how to release the current context of a user to maximize the level of quality of service (QoS) of context-aware apps with privacy preservation. The conducted extensive simulations on real dataset demonstrate the improved performance of CAPP over other traditional approaches.

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.

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.

Over years, many efforts are made for the problem of constructing quality fault-tolerant virtual backbones in wireless network. In case that a wireless network consists of physically equivalent nodes, e.g. with the same communication range, unit disk graph (UDG) is widely used to abstract the wireless network and the problem is formulated as the minimum k-connected mdominating set problem on the UDG. So far, most results are focused on designing a constant factor approximation algorithm for this NP-hard problem under two positive integers k and m satisfying m ≥ k ≥ 1 and k ≤ 3. Very recently, Shi et. al. and Fukunaga separately introduced constant factor approximation algorithms for the problem with m ≥ k ≥ 1. However, we found the structures of the algorithms are extremely complicated, and thus it would be difficult to implement and use them in practice. Motivated by such observation, this paper introduces a novel approximation algorithm for the problem with m ≥ k ≥ 1. This algorithm is based on our new technique which first computes a 1-connected m-dominating set D and repeatedly (a) decomposes D into an i-connected block tree, with i = 2, 3, ··· , k, and (b) use this graph structure to improve the connectivity of D, until D becomes k-connected. We provide a rigorous theoretical analysis to prove that the proposed algorithm is correct and its approximation ratio is a constant. We compare the structure of our algorithm against the existing ones and show our algorithm is much simpler to understand and implement.

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.

This paper studies social influence from the perspective of users' characteristics. The importance of users' characteristics in word-of-mouth applications has been emphasized in economics and marketing fields. We model a category of users called mavens where their unique characteristics nominate them to be the preferable seeds in viral marketing applications. In addition, we developed and verified methods to learn their characteristics from a real dataset. Also, we illustrated ways to maximize information flow through mavens in social networks. Our experiments show that our model successfully detected mavens as well as fulfilled significant roles in maximizing the information flow in a social network comparing to the spread that was a result of traditional influencer users in influence maximization problem. These results showed the compatibility of our model with real marketing approaches.

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.

Data sharing for data collection among multiple applications is an efficient way to reduce communication cost for Wireless Sensor Networks (WSNs). This paper is the first work to introduce the interval data sharing problem which is to investigate how to transmit as less data as possible over the network, and meanwhile the transmitted data satisfies the requirements of all the applications. Different from current studies where each application requires a single data sampling during each task, we study the problem where each application requires a continuous interval of data sampling in each task. The proposed problem is a nonlinear nonconvex optimization problem. In order to lower the high complexity for solving a nonlinear nonconvex optimization problem in resource restricted WSNs, a 2-factor approximation algorithm whose time complexity is$O(n^{2})$and memory complexity is$O(n)$is provided. A special instance of this problem is also analyzed. This special instance can be solved with a dynamic programming algorithm in polynomial time, which gives an optimal result in$O(n^{2})$time complexity and$O(n)$memory complexity. Three online algorithms are provided to process the continually coming tasks. Both the theoretical analysis and simulation results demonstrate the effectiveness of the proposed algorithms.

COUNT is a typical aggregation operation in Wireless Sensor Networks (WSNs). In such an operation, the total number of the items which are of the same kind is obtained and only one numerical value is returned as the result. This paper identifies the multiple count problem which counts items belonging to multiple categories. For each category, the total number of the items belonging to this category is calculated. Therefore, the returned result is a set of values instead of a single value. The multiple count problem is more challenging than the traditional count problem as the former incurs more communication overhead. This paper proposes a distributed approximate multiple count algorithm which can derive an error bounded result under a specified communication cost constraint for each node. The error of the derived result is hN/L, where h is the depth of the routing tree, N is the total number of all the items belonging to all the categories, and L is a representation of the communication cost constraint for each node. Furthermore, the weighted multiple count problem is investigated where different kinds of items to be counted have different weights. The proposed algorithms are evaluated through TOSSIM, a widely used simulation tool for WSNs. The theoretical analysis and simulation results both demonstrate the correctness and effectiveness of the proposed algorithms.

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.

Data Gathering is a fundamental task in Wireless Sensor Networks (WSNs). Data gathering trees capable of performing aggregation operations are also referred to as Data Aggregation Trees (DATs). Currently, most of the existing works focus on constructing DATs according to different user requirements under the Deterministic Network Model (DNM). However, due to the existence of many probabilistic lossy links in WSNs, it is more practical to obtain a DAT under the realistic Probabilistic Network Model (PNM). Moreover, the load-balance factor is neglected when constructing DATs in current literatures. Therefore, in this paper, we focus on constructing a Load-Balanced Data Aggregation Tree (LBDAT) under the PNM. More specifically, three problems are investigated, namely, the Load-Balanced Maximal Independent Set (LBMIS) problem, the Connected Maximal Independent Set (CMIS) problem, and the LBDAT construction problem. LBMIS and CMIS are well-known NP-hard problems and LBDAT is an NP-complete problem. Consequently, approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented in the paper. Finally, our simulation results show that the proposed algorithms outperform the existing state-of-the-art approaches significantly.

ABSTRACT Inspired by the backbone concept in wired networks, a virtual backbone is expected to bring substantial benefits to routing in wireless sensor networks (WSNs). A connected dominating set (CDS) is used as a virtual backbone for efficient routing and broadcasting in WSNs. Most existing works focus on constructing a minimum CDS, ak‐connectm‐dominating CDS, a minimum routing cost CDS, or a bounded‐diameter CDS. However, theload‐balancefactor is not considered for CDSs in WSNs. In this paper, a greedy‐based approximation algorithm is proposed to construct load‐balanced CDS in a WSN. More importantly, we propose a new problem: the Load‐balanced Allocate Dominatee problem. Consequently, we propose an optimal centralized algorithm and an efficient probability‐based distributed algorithm to solve the Load‐balanced Allocate Dominatee problem. For a given CDS, the upper and lower bounds of the performance ratio of the distributed algorithm are analyzed in the paper. Through extensive simulations, we demonstrate that our proposed methods extend network lifetime by up to 80% compared with the most recently published CDS construction algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Query scheduling as one of the most important technologies used in query processing has been widely studied recently. In this paper, we investigate the Minimum Latency Multi‐Regional Query Scheduling (ML‐MRQS) problem in wireless Sensor Networks (WSNs), which aims to generate a scheduling plan with minimum latency under a more practical query model called Multi‐Regional Query (MRQ). An MRQ targets at interested data from multiple regions of a WSN, where each region is a subarea. Because the ML‐MRQS problem is NP‐hard, we propose a heuristic scheduling algorithm Multi‐Regional Query Scheduling Algorithm (MRQSA) to solve this problem. Theoretical analysis shows that the latency of MRQSA is upper bounded by 23A + B + Cfor an MRQ withmquery regions , where is the maximum latency for non‐overlapped regions, is the maximum latency for overlapped regions, and is the accumulated latency for data transmission from the accessing nodes to the sink. Simulation results show that MRQSA reduces latency by 42.7%to 51.63%with respect to different number of query regions, network density, region size, and interference/transmission range compared with C‐DCQS, while guaranteeing energy efficiency. Copyright © 2012 John Wiley & Sons, Ltd.

A Connected Dominating Set (CDS) is used as a Virtual Backbone (VB) for efficient routing and broadcasting in Wireless Sensor Networks (WSNs). Currently, almost all existing works focus on constructing Minimum-sized CDS under the Deterministic Network Model (DNM). However, due to the existence of many probabilistic lossy links in WSNs, it is more practical to obtain a VB under the realistic Probabilistic Network Model (PNM). Moreover, load-balance factor cannot be neglected when constructing a VB to prolong network lifetime. Hence, in this paper, we propose a Multi-Objective Genetic Algorithm (MOGA) to construct a Load-Balanced Virtual Backbone under PNM (LBVBP). Through simulations, we demonstrate that our proposed methods extend network lifetime by 65% on average compared with the existing state-of-the-art approaches.

Data sharing for data collection among multiple applications is an efficient way to reduce the communication cost of Wireless Sensor Networks (WSNs). This paper is the first work to introduce the interval data sharing problem which is to investigate how to transmit as less data as possible over the network, and meanwhile the transmitted data satisfies the requirements of all the applications. Different from current studies where each application requires a single data sampling during each task, we study the problem where each application requires a continuous interval of data sampling in each task instead. The proposed problem is a nonlinear nonconvex optimization problem. In order to lower the high complexity for solving a nonlinear nonconvex optimization problem in resource restricted sensor nodes, a 2-factor approximation algorithm whose time complexity is O(n2) and memory complexity is O(n) is provided. A special instance of this problem is also analyzed. This special instance can be solved with a dynamic programming algorithm in polynomial time, which gives an optimal result in O(n2) time complexity and O(n) memory complexity. We evaluate the proposed algorithms with TOSSIM, a widely used simulation tool in WSNs. Theoretical analysis and simulation results both demonstrate the effectiveness of the proposed algorithms.

Routing in Disruption Tolerant Networks (DTNs) is a challenging problem due to the intermittent connectivity between the nodes. Researchers have proposed many routing protocols that adapt to the temporary connections of DTNs. One classification of routing protocols makes use of historical information to predict future contact patterns for any pair of nodes. However, most existing protocols focus on the probability of a path from the source to the destination without considering the information in a packet which includes the source, destination, size, TTL (Time-To-Live) and limited resources such as available buffer size and bandwidth. In this paper, we propose a new prediction-based routing algorithm that takes into account packet information under the conditions of limited transmission opportunities. The goal of this protocol is to increase the overall delivery ratio through scheduling packets at each node. Meanwhile, this protocol may sacrifice some messages' delivery delay time to some extent. Extensive simulation results with real traces show that our protocol with packet scheduling has better performance than the pure probabilistic routing algorithms in term of delivery ratio. Our protocol's performance advantage is more obvious for nodes with higher packet intensity and shorter TTL in packets.

Data collection is a common operation of wireless sensor networks (WSNs). The performance of data collection can be measured by its achievable network capacity. However, most existing works focus on the network capacity of unicast, multicast or/and broadcast. In this article, we study the snapshot/continuous data collection (SDC/CDC) problem under the physical interference model for randomly deployed dense WSNs. For SDC, we propose a Cell-Based Path Scheduling (CBPS) algorithm based on network partitioning. Theoretical analysis shows that its achievable network capacity is order-optimal. For CDC, a novel Segment-Based Pipeline Scheduling (SBPS) algorithm is proposed which combines the pipeline technique and the compressive data gathering technique. Theoretical analysis shows that SBPS significantly speeds up the CDC process and achieves a high network capacity.

Wireless Sensor Networks (WSNs) are deployed for monitoring in a range of critical domains (e.g., health care, military, critical infrastructure). Accordingly, these WSNs should be resilient to attacks. The current approach to defending against malicious threats is to develop and deploy a specific defense mechanism for a specific attack. However, the problem with this traditional approach to defending sensor networks is that the solution for the Jamming attack does not defend against other attacks (e.g., Sybil and Selective Forwarding). In reality, one cannot know a priori what type of attack an adversary will launch. This work addresses the challenges with the traditional approach to securing sensor networks and presents a comprehensive framework, Di-Sec, that can defend against all known and forthcoming attacks. At the heart of Di-Sec lies the monitoring core (M-Core), which is an extensible and lightweight layer that gathers statistics relevant for the defense mechanisms. The M-Core allows for the monitoring of both internal and external threats and supports the execution of multiple detection and defense mechanisms (DDMs) against different threats in parallel. Along with Di-Sec, a new user-friendly domain-specific language was developed, the M-Core Control Language (MCL). Using the MCL, a user can implement new defense mechanisms without the overhead of learning the details of the underlying software architecture (i.e., TinyOS, Di-Sec). Hence, the MCL expedites the development of sensor defense mechanisms by significantly simplifying the coding process for developers. The Di-Sec framework has been implemented and tested on real sensors to evaluate its feasibility and performance. Our evaluation of memory, communication, and sensing components shows that Di-Sec is feasible on today's resource-limited sensors and has a nominal overhead. Furthermore, we illustrate the basic functionality of Di-Sec by implementing and simultaneously executing DDMs for attacks at various layers of the communication stack (i.e., Jamming, Selective Forwarding, Sybil, and Internal attacks).

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.

Abstract For extremely sensitive applications, it may be advantageous for users to transmit certain types of data covertly over the network. This provides an additional layer of security to that provided by the different layers of the protocol stack. In this paper we present a covert side channel that uses the 802.11 MAC rate switching protocol. The covert channel provides a general method to hide communications within currently deployed 802.11 LANs. The technique uses a one‐time password (OTP) algorithm to ensure high‐entropy randomness of the covert messages. We investigate how the covert side channel affects network throughput under various rate‐switching conditions with UDP‐based and TCP‐based application traffic. We also investigate the covertness of the covert side channel using standardized entropy. The theoretical analysis shows that the maximum covert channel bandwidth is 60 bps. The simulation results show that the impact on network throughput is minimal and increases slightly as the covert channel bandwidth increases. We further show that the channel has 100% accuracy with minimal impact on rate switching entropy for scenarios where rate switching normally occurs. Finally, we present two applications for the covert channel: covert authentication and covert WiFi botnets. Copyright © 2010 John Wiley & Sons, Ltd.

Wireless sensors are attached to all kinds of mobile devices/entities such as mobile phones, PDAs, vehicles, robots and animals. This generates Mobile Wireless Sensor Networks (MWSNs) with very dynamic topologies and loose connectivity that depend on mobility of the mobile devices. Data collection from these mobile sensors has become a great challenge considering volatile topologies, loose connectivity and limited buffer storage. This paper proposes a stochastic compressive data collection protocol for MWSNs named SMITE. SMITE consists of three parts: random collector election, stochastic direct transmission from common nodes to collectors when common nodes are in the collectors' transmission range, and angle transmission from collectors to the mobile sink when collectors gather enough data using a predictive method. The collectors use bloom filters to compress the received data. The protocol's performance is theoretically analyzed. The analytic results show that data from the common nodes can be gathered to the collectors with a high probability and gathered data on the collectors can also be forwarded to the mobile sink with a high probability. Simulations are carried out for performance evaluation. The simulation results show that SMITE significantly outperforms the state-of-the-art solutions such as DFT-MSN, SCAR and Sidewinder on the aspects of delivery ratio, transmission overhead, and time delay.

Data collection is an important operation of wireless sensor networks (WSNs). The performance of data collection can be measured by its achievable network capacity. Most existing works focus on the capacity of unicast, multicast or snapshot data collection in single-radio single-channel wireless networks, and no dedicated works consider the continuous data collection capacity for WSNs in detail under the protocol interference model. In this paper, we first propose a multi-path scheduling algorithm for the snapshot data collection in single-radio multi-channel WSNs and prove that its achievable network capacity is at least W/[(3.63/H)ρ2+o(ρ)], which is a tighter lower bound compared with the previously best result in which is W/(8ρ2), where W is the bandwidth over a channel, H is the number of the available orthogonal channels, ρ is the ratio of the interference radius over the transmission radius of a sensor and o(ρ) is a linear equation of ρ. For the continuous data collection problem, although the authors in claim that data collection can be pipelined with existing works, we find that such an idea cannot actually improve network capacity. We explain the reason for this and propose a novel continuous data collection method for dual-radio multi-channel WSNs. This method significantly speeds up the data collection process, and achieves a capacity of nW/[12M((3.63/H)ρ2+o(ρ))] when Δe≤ 12, or nW/[MΔc((3.63/H)ρ2+o(ρ))] when Δe>; 12, where n is the number of sensors, M is a constant value and usually Meis the maximum number of leaf nodes having a same parent node in the routing tree (i.e. data collection tree). The simulation results also indicate that the proposed algorithms significantly improve network capacity compared with the existing works.

In this paper, we propose a novel compressive sensing (CS) based approach for sparse target counting and positioning in wireless sensor networks. While this is not the first work on applying CS to count and localize targets, it is the first to rigorously justify the validity of the problem formulation. Moreover, we propose a novel greedy matching pursuit algorithm (GMP) that complements the well-known signal recovery algorithms in CS theory and prove that GMP can accurately recover a sparse signal with a high probability. We also propose a framework for counting and positioning targets from multiple categories, a novel problem that has never been addressed before. Finally, we perform a comprehensive set of simulations whose results demonstrate the superiority of our approach over the existing CS and non-CS based techniques.

Query scheduling as one of the most important technologies used in query processing has been widely studied recently. Unfortunately, to the best of our knowledge, no previous work focuses on the Minimum Latency Multi-Regional Query Scheduling (ML-MRQS) problem. In this paper, we investigate the ML-MRQS problem in Wireless Sensor Networks (WSNs), which aims to generate a scheduling plan with minimum latency for a more practical query model called Multi-Regional Query (MRQ). A MRQ targets at user interested data from multiple region-sofa WSN, where each region is a subarea of the WSN. We claim that the ML-MRQS problem is NP-hard. Therefore, we propose a heuristic scheduling algorithm Multi-Regional Query Scheduling Algorithm (MRQSA) to solve this problem. Theoretical analysis shows that the latency of MRQSA is upper bounded by 23A + B + C for a MRQ with m query regions R1, R2…, Rm, where A = maxi=1mDileft, B = maxi=1m{(23Di+5Δ+21)ki}, C = Σi=1mHi+5Δ−m+17, m is the number of regions, Δ is the maximum node degree in the WSN, A is the diameter of Ri, kiis the maximum overlapped degree of sensor nodes in TU, Hi represents the distance of Riwith respect to the sink, and Dileftis the diameter of the non-overlapped part of Ri. Extensive simulations are conducted to verify the performance of our algorithm, which show that MRQSA significantly reduces the query latency when compared with the most recently published multi-query scheduling algorithm.

Data collection is a common operation of Wireless Sensor Networks (WSNs). The performance of data collection can be measured by its achievable network capacity. However, most existing works focus on the network capacity of unicast, multicast or/and broadcast, which are different communication modes from data collection, especially continuous data collection. In this paper, we study the Snapshot/Continuous Data Collection (SDC/CDC) problem under the Physical Interference Model (PhIM) for randomly deployed dense WSNs. For SDC, we propose a Cell-Based Path Scheduling (CBPS) algorithm based on network partitioning. Theoretical analysis shows that its achievable network capacity is Ω(W) (W is the data transmitting rate, i.e. bandwidth, over a channel), which is order-optimal. For CDC, we propose a novel Segment-Based Pipeline Scheduling (SBPS) algorithm that significantly speeds up the CDC process, and achieves a surprising network capacity, which is at least √(n/ log n) or n/log n times better than the current best result.

The complete area coverage problem in Wireless Sensor Networks (WSNs) has been extensively studied in the literature. However, many applications do not require complete coverage all the time. For such applications, one effective method to save energy and prolong network lifetime is to partially cover the area. This method for prolonging network lifetime recently attracts much attention. However, due to the hardness of verifying the coverage ratio, all the existing centralized or distributed but nonparallel algorithms for partial coverage have very high time complexities. In this work, we propose a framework which can transform almost any existing complete coverage algorithm to a partial coverage one with any coverage ratio by running a complete coverage algorithm to find full coverage sets with virtual radii and converting the coverage sets to partial coverage sets via adjusting sensing radii. Our framework can preserve the characteristics of the original algorithms and the conversion process has low time complexity. The framework also guarantees some degree of uniform partial coverage of the monitored area.

Data aggregation is an essential yet time-consuming task in wireless sensor networks (WSNs). This paper studies the well-known Minimum-Latency Aggregation Schedule (MLAS) problem and proposes an energy-efficient distributed scheduling algorithm named Clu-DDAS based on a novel cluster-based aggregation tree. Our approach differs from all the previous schemes where Connected Dominating Sets or Maximal Independent Sets are employed. We prove that Clu-DDAS has a latency bound of 4R' + 2Delta - 2, where Δ is the maximum degree and R' is the inferior network radius which is smaller than the network radius R. Clu-DDAS has comparable latency as the previously best centralized algorithm E-PAS, while Clu-DDAS consumes 78% less energy as shown by the simulation results. Clu-DDAS outperforms the previously best distributed algorithm DAS whose latency bound is 16R' + Δ - 14 on both latency and energy consumption. On average, Clu-DDAS transmits 67% fewer total messages than DAS does. We also propose an adaptive strategy for updating the schedule to accommodate dynamic network topology.

In this paper, a duty cycle based multi-channel MAC protocol with multiple channel reservation, called M-cube, is proposed to tackle the triple hidden terminal problems. M-cube can make nodes to choose one actually idle channel from all the expected idle channels. Therefore, M-cube can avoid data packet collisions resulted by the triple hidden terminal problems. By minimizing the lower bound of the average number of times of channel switching in M-cube, the optimal duty cycle is obtained through theoretical analysis. To validate the effectiveness of multiple channel reservation and dynamic optimal duty cycling, extensive simulations and real test bed experiments were conducted. Both the simulation and experiment results show that when the number of channels is large or network loads are heavy, M-cube improves energy efficiency and throughput significantly compared with other works in the literature.

This paper proposes ARM, an receiver-initiated MAC protocol with duty cycling to tackle control channel saturation, triple hidden terminal and low broadcast reliability problems in asynchronous multi-channel WSNs. By adopting a receiver-initiated transmission scheme and probability-based random channel selection, ARM effectively solves control channel saturation and triple hidden terminal problems. Further, ARM employs a receiver-adjusted broadcast scheme to guarantee broadcast reliability for broadcast-intensive applications. Via the theoretical analysis, two factors that assist ARM to handle these problems are derived. The simulation and real testbed experimental results show that via solving these three problems ARM achieves significant improvement in energy efficiency and throughput. Moreover, ARM exhibits a prominent ability to enhance its broadcast reliability.

Designing cost-efficient, secure network protocols for Wireless Sensor Networks (WSNs) is a challenging problem because sensors are resource-limited wireless devices. Since the communication cost is the most dominant factor in a sensor's energy consumption, we introduce an energy-efficient Virtual Energy-Based Encryption and Keying (VEBEK) scheme for WSNs that significantly reduces the number of transmissions needed for rekeying to avoid stale keys. In addition to the goal of saving energy, minimal transmission is imperative for some military applications of WSNs where an adversary could be monitoring the wireless spectrum. VEBEK is a secure communication framework where sensed data is encoded using a scheme based on a permutation code generated via the RC4 encryption mechanism. The key to the RC4 encryption mechanism dynamically changes as a function of the residual virtual energy of the sensor. Thus, a one-time dynamic key is employed for one packet only and different keys are used for the successive packets of the stream. The intermediate nodes along the path to the sink are able to verify the authenticity and integrity of the incoming packets using a predicted value of the key generated by the sender's virtual energy, thus requiring no need for specific rekeying messages. VEBEK is able to efficiently detect and filter false data injected into the network by malicious outsiders. The VEBEK framework consists of two operational modes (VEBEK-I and VEBEK-II), each of which is optimal for different scenarios. In VEBEK-I, each node monitors its one-hop neighbors where VEBEK-II statistically monitors downstream nodes. We have evaluated VEBEK's feasibility and performance analytically and through simulations. Our results show that VEBEK, without incurring transmission overhead (increasing packet size or sending control messages for rekeying), is able to eliminate malicious data from the network in an energy-efficient manner. We also show that our framework performs better than other comparable schemes in the literature with an overall 60-100 percent improvement in energy savings without the assumption of a reliable medium access control layer.

Wireless sensor networks can be used for event warning applications. Till date, in most of the proposed schemes, the raw or aggregated sensed data are periodically sent to a data consuming center. However, with those schemes, the occurrence of an emergency event such as a fire is hardly reported timely, which is a strict requirement for event warning applications. In wireless sensor networks, it is also highly desired to conserve energy so that network lifetime can be maximized. Furthermore, to ensure the quality of surveillance, some applications require that if an event occurs, it needs to be detected by at least k sensors, where k is a user-defined parameter. In this work, we examine the Timely Energy-efficient k-Watching Event Monitoring (TEKWEM) problem and propose a scheme, which involves an event detection model and a warning delivery model, for monitoring composite events and delivering warnings to users. Theoretical analysis and simulation results are shown to validate the proposed scheme.

This paper proposes a novel and efficient distributed flow control scheme for multirate multicast (MR-M), based on the well-known Proportional Integral and Derivative (PID) controllers. The PID controller at each router computes its expected incoming rate and feed backs this rate to its upstream router, such that the local buffer occupancy can be stabilized at an appropriate value. We give the theoretical analysis of the proposed PID controller in terms of system stability. The proposed MR-M controller achieves the fairness in two aspects: 1) The intrasession fairness, i.e., the receivers from the same source within the same multicast session can receive data at different rates, if they subscribe networks with different capacities; 2) The intersession fairness, i.e., the link bandwidth is fairly shared among multiple multicast sessions from different sources. Extensive simulations have been conducted and the results have demonstrated a superior performance of the proposed scheme in terms of system stability, high link utilization, and high throughput.

Many data dissemination techniques have been proposed for wireless sensor networks to facilitate data dissemination and query processing. However, these techniques may not work well in a large scale sensor network where a huge amount of sensing data are generated. In this paper, we propose an integrated distributed Connected dominating set Based Indexing (CBI) data dissemination scheme to support scalable handling of large amount of sensing data in large scale wireless sensor networks. Our CBI can minimize the use of limited network and computational resources while providing timely responses to queries. Moreover, our data dissemination framework ensures scalability and load balance. Analysis and simulations are conducted to evaluate the performance of our CBI scheme. The results show that the CBI scheme outperforms the external storage-based scheme, local storage-based scheme and the data-centric storage-based scheme in overall performance.

Data aggregation is an essential operation in wireless sensor network applications. This paper focuses on the data aggregation scheduling problem. Based on maximal independent sets, a distributed algorithm to generate a collision-free schedule for data aggregation in wireless sensor networks is proposed. The time latency of the aggregation schedule generated by the proposed algorithm is minimized using a greedy strategy. The latency bound of the schedule is 24D + 6 Delta + 16, where D is the network diameter and Delta is the maximum node degree. The previous data aggregation algorithm with least latency has the latency bound (Delta- Delta 1)R, where R is the network radius. Thus in our algorithm Delta contributes to an additive factor instead of a multiplicative factor, which is a significant improvement. To the best of our knowledge, the proposed algorithm is the first distributed algorithm for data aggregation scheduling. This paper also proposes an adaptive strategy for updating the schedule when nodes fail or new nodes join in a network. The analysis and simulation results show that the proposed algorithm outperforms other aggregation scheduling algorithms.

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.

The complete area coverage problem in wireless sensor networks (WSNs) where every point inside an area is covered by an active sensor has been extensively studied in the literature. However, there are many applications that do not always require complete coverage. For such applications, an effective method to save energy and prolong network lifetime is to partially cover the area. However, due to the hardness to verify the ratio of the covered area over the entire monitored area (coverage ratio), all the existing algorithms for partial coverage have very high time complexities (either centralized algorithms or distributed but non-parallel algorithms). Besides, all the existing algorithms are intentionally designed for partial coverage, thus they do not utilize the various exiting methods for the complete coverage problem. In this work, we propose a framework that can convert almost any existing algorithm for complete coverage to a one for partial coverage with any coverage ratio. Our framework can preserve the characteristics of the original algorithms and the conversion process has low time complexity. The framework also guarantees some degree of uniform partial coverage of the monitored area.

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.

Failure detection (FD) is an important issue for supporting dependability in distributed healthcare systems to guarantee continuous, safe, secure, and dependable operation, and often is an important performance bottleneck in the event of node failure. FD can be used to manage the health status of communication for delivering telemedicine services, and then to help distributed healthcare system reduce fatal accident rate and increase the reliability and safety of systems. Ensuring acceptable quality of service (QoS) is made difficult by the relative unpredictability of the network environment. In this paper, first, we compare QoS metrics of several adaptive FDs, discuss their properties and their relation, and then propose one optimization over the existing methods, called tuning adaptive margin failure detector (TAM FD), which significantly improves QoS, especially in the aggressive range and when the network is unstable. Second, we address the problem of most adaptive schemes, namely their need for a large window of samples. So we also analyze the impact of memory size on the performance of FDs, and then prove that the presented scheme is designed to use a fixed and very limited amount of memory for the distributed system. Our experimental results over several kinds of networks (Cluster, WiFi, LAN, Intercontinental WAN) show that the properties of the existing adaptive failure detectors, and demonstrate that the optimization is reasonable and acceptable. Furthermore, the extensive experimental results show what is the effect of memory size on the overall QoS of each adaptive failure detector. For our TAM FD, the effect of window size on their QoS is very small and can be negligible.

Connected Dominating Sets (CDSs) can serve as virtual backbones for wireless networks. A smaller virtual backbone incurs less maintenance overhead. Unfortunately, computing a minimum size CDS is NP-hard, and thus most researchers in this area concentrate on how to construct smaller CDSs. However, people neglected other important metrics of network, such as diameter and average hop distances between two communication parties. In this paper, we investigate the problem of constructing quality CDS in terms of size, diameter, and Average Backbone Path Length (ABPL). We present two centralized algorithms having constant performance ratios for its size and diameter of the constructed CDS. Especially, the size of CDS computed by the second algorithm is no more than 6.906 times of its optimal solution. Furthermore, we give its distributed version, which not only can be implemented in real situation easily but also considers energy to extend network lifetime. In our simulation, we show that in average the distributed algorithm not only generates a CDS with smaller diameter and ABPL than related work but also suppresses its size well. We also show that it is more energy efficient than others in prolonging network lifetime.

The recently proposed active queue management (AQM) is an effective method used in Internet routers for congestion control, and to achieve a tradeoff between link utilization and delay. The de facto standard, the random early detection (RED) AQM scheme, and most of its variants use average queue length as a congestion indicator to trigger packet dropping. In this paper, we propose a novel proportional and differential RED algorithm, called NPDRED, as an extension of RED. NPD-RED is based on a self-tuning proportional and differential controller, which not only considers the instantaneous queue length at the current time point, but also takes into consideration the ratio of the current differential error signal to the buffer size. Furthermore, we give theoretical analysis of the system stability and give guidelines for the selection of feedback gains for the TCP/RED system to stabilize the instantaneous queue length at a desirable level. Extensive simulations have been conducted with ns2. The simulation results have demonstrated that the proposed NPD-RED algorithm outperforms the existing AQM schemes in terms of average queue length,average throughput, and stability.

With the ever-increasing number of multicast data applications recently, considerable efforts have been focused on the design of flow control schemes for multicast services. The main difficulties in designing a flow controller for multicast service are caused by heterogeneous multicast receivers, especially those with large propagation delays, since the feedback arriving at the source is somewhat outdated, and can be harmful to the control operations. To attack the above problem, the present paper describes a novel multicast flow control scheme, the so-called proportional, integrative, derivative plus neural network (PIDNN) predictive technique, which consists of two components: the proportional integrative plus derivative (PID) controller and the back propagation BP neural network (BPNN). This network-assisted property is different from the existing control schemes, in that the PIDNN controller can release the irresponsiveness of a multicast flow caused by those long propagation delays from the receivers. By using BPNN for the receivers with longer propagation delay, this active scheme makes the control more responsive to network status. Thus the rate adaptation can be performed in a timely manner, for the sender to respond to network congestion quickly. We analyze the theoretical aspects of the proposed algorithm, show how the control mechanism can be used to design a controller to support multi-rate multicast transmission based on feedback of explicit rates, and verify this matching using simulations. Simulation results demonstrate the efficiency of our scheme in terms of high link utilization, quick response, scalability, high unitary throughput, intra-session fairness and inter-session fairness.

We investigate the p-percent coverage problem in this paper and propose two algorithms to prolong network lifetime based on the fact that for some applications full coverage is not necessary and different subareas of the monitored area may have different coverage requirements. The first algorithm, CPCA, is a centralized algorithm which selects the least number of nodes to monitor p-percent of the monitored area. The second algorithm, DPCP, is a distributed algorithm which can determine a set of nodes in a distributed manner to cover p-percent of the monitored area. Both of the algorithms guarantee network connectivity. The simulation results show that our algorithms can remarkably prolong network lifetime, have less than 5% unrequired coverage for large networks and employ nodes fairly for most cases.

Wireless sensor networks (WSNs) are employed in many applications in order to collect data. One key challenge is to minimize energy consumption to prolong network lifetime. A scheme of making some nodes asleep and estimating their values according to the other active nodespsila readings has been proved energy-efficient. For the purpose of improving the precision of estimation, we propose two powerful estimation models, data estimation using physical model (DEPM) and data estimation using statistical model (DESM). DEPM estimates the values of sleeping nodes by the physical characteristics of sensed attributes, while DESM estimates the values through the spatial and temporal correlations of the nodes. Experimental results on real sensor networks show that the proposed techniques provide accurate estimations and conserve energy efficiently.

Data centric storage is an effective algorithm to organize data in the sensor networks. Even though such kind of storage can save more energy than other algorithms, such as flooding based algorithm, it also wastes energy in some cases. For example, the event frequency is much more than the query frequency. To overcome this problem, we propose an index based query processing algorithm. We analyze the energy consumption of the algorithm and present conditions for the index based algorithm to save more energy than the traditional data centric storage. Finally a time-based index management algorithm is proposed to adopt the proper algorithm in different cases. Extensive experiments showed that our time-based index management algorithm can save more energy than the traditional data centric algorithm.

The control and coordination of mobile robots in groups that can freely cooperate and move on a plane is a widely studied topic in distributed robotics. In this paper, we focus on the flocking problem: there are two kinds of robots: the leader robot and the follower robots. The follower robots are required to follow the leader robot wherever it goes (following), while keeping a formation they are given in input (flocking). A novel scheme is proposed based on the relative motion theory. Extensive theoretical analysis and simulation results demonstrate that this scheme provides the follower robots an efficient method to follow the leader as soon as possible with the shortest path. Furthermore, this scheme is scalable, and the processing load for every robot is not increased with the addition of more robots.

A Connected Dominating Set (CDS) working as a virtual backbone is an effective way to decrease the overhead of routing in a wireless sensor network. Furthermore, a k-Connected m-Dominating Set (kmCDS) is necessary for fault tolerance and routing flexibility. Some approximation algorithms have been proposed to construct a kmCDS. However, most of them only consider some special cases where k = 1,2 or k ≤ m, or are not easy to implement, or have high message complexity. In this paper, we propose a novel distributed algorithm LDA with low message complexity to construct a kmCDS for general k and m whose size is guaranteed to be within a small constant factor of the optimal solution when the maximum node degree is a constant. We also propose one centralized algorithm ICGA with a constant performance ratio to construct a kmCDS. Theoretical analysis as well as simulation results are shown to evaluate the proposed algorithms.

This paper introduces the problem of fault tolerant topology control for all-to-one and one-to-all communication in static wireless networks with asymmetric wireless links. This problem is important in both theoretical and practical aspects. We investigate two approaches, namely minimum weight based approach and nearest neighbor augmentation approach, to address this problem. Furthermore, we give theoretical analysis for the proposed algorithms. Among other results, we show that the minimum weight based approach has a $k$-approximation algorithm for all-to-one fault tolerant topology control where $k$ is the number of disjoint paths. When $k=1$, this approach solves the minimum power all-to-one $1$-connected topology control problem. To the best of our knowledge, this paper is the first to study the fault tolerant topology control for all-to-one and one-to-all communication in asymmetric static wireless networks, and also is the first to demonstrate that the minimum power all-to-one 1-connected topology control problem has an optimal solution.

In many applications, transaction data arrive in the form of high speed data streams. These data contain a lot of information about customers that needs to be carefully managed to protect customerspsila privacy. In this paper, we consider the problem of preserving customerpsilas privacy on the sliding window of transaction data streams. This problem is challenging because sliding window is updated frequently and rapidly. We propose a novel approach, SWAF (sliding window anonymization framework), to solve this problem by continuously facilitating k-anonymity on the sliding window. Three advantages make SWAF practical: (1) Small processing time for each tuple of data steam. (2) Small memory requirement. (3) Both privacy protection and utility of anonymized sliding window are carefully considered. Theoretical analysis and experimental results show that SWAF is efficient and effective.

Network lifetime is a critical issue in Wireless Sensor Networks. It is possible to extend network lifetime by organizing the sensors into a number of sensor covers. However, with the limited bandwidth, coverage breach (i.e, targets that are not covered) can occur if the number of available time-slots/channels is less than the number of sensors in a sensor cover. In this paper, we study a joint optimization problem in which the objective is to minimize the coverage breach as well as to maximize the network lifetime. We show a "trade-off" scheme by presenting two strongly related models, which aim to tradeoffs between the two conflicting objectives. The main approach of our models is organizing sensors into non-disjoint sets, which is different from the current most popular approach and can gain longer network lifetime as well as less coverage breach. We proposed two algorithms for the first model based on linear programming and greedy techniques, respectively. Then we transform these algorithms to solve the second model by revealing the strong connection between the models. Through numerical simulation, we showed the good performance of our algorithms and the pictures of the tradeoff scheme in variant scenarios, which coincide with theoretical analysis very well. It is also showed that our algorithms could obtain less breach rate than the one proposed in [2].

Data aggregation is a fundamental yet time-consuming task in wireless sensor networks. We focus on the latency part of data aggregation. Previously, the data aggregation algorithm of least latency [1] has a latency bound of (Delta - 1)R, where Delta is the maximum degree and R is the network radius. Since both Delta andRcould be of the same order of the network size, this algorithm can still have a rather high latency. In this paper, we designed an algorithm based on maximal independent sets which has an latency bound of 23R+ Delta - 18. Here Delta contributes to an additive factor instead of a multiplicative one; thus our algorithm is nearly constant approximation and it has a significantly less latency bound than earlier algorithms especially when Delta is large.

In this paper, we study the Maximum lifetime Target Coverage problem (MTC), which is to maximize the network lifetime while guaranteeing the complete coverage of all the targets. Many centralized algorithms have been proposed to solve this problem. A very few distributed versions have also been presented but none of them obtains a good approximation ratio. In this paper, we propose two O(log n) localized algorithms. In particular, we first reduce the MTC problem to the domatic number problem in directed graphs. This relation shows that a feasible solution to the domatic number problem is also a feasible solution to the MTC problem. We next prove the lower and upper bounds of this domatic number. Based on this proof, we present two O(log n)-localized algorithms to solve the MTC problem.

Sensor networks can be used for event alarming applications. To date, in most of the proposed schemes, the raw or aggregated sensed data is periodically sent to a data consuming center. However, with this scheme, the occurrence of an emergency event such as a fire is hardly reported in a timely manner which is a strict requirement for event alarming applications. In sensor networks, it is also highly desired to conserve energy so that the network lifetime can be maximized. Furthermore, to ensure the quality of surveillance, some applications require that if an event occurs, it needs to be detected by at least k sensors where k is a user-defined parameter. In this work, we examine the timely energy-efficient k-watching event detection problem (TEKWEO). A topology-and-routing-supported algorithm is proposed which constructs a set of detection sets that satisfy the short notification time, energy conservation, and tunable quality of surveillance requirements for event alarming applications. Simulation results are shown to validate the proposed algorithm.

When wireless sensor networks are employed for event monitoring, such as fire detection and enemy movement monitoring, the observers are more interested in the monitored events rather than the readings from sensors. To answer event queries such as "Where was the fire detected during 2-6pm?", an energy-efficient query processing technique is required. This paper presents a data-centric storage strategy, called CM-DCS, and also proposes two distributed event query processing algorithms. Furthermore, the energy consumptions for query processing methods based on three kinds of storage strategies namely external storage, local storage and CM-DCS are analyzed and compared, so that users can have a guideline of choosing a correct storage strategy for different applications. Theoretical analysis and simulation results show that the event query processing algorithm based on CM-DCS can save more energy than those algorithms based on the external storage strategy and the local storage strategy in most cases.

In this paper, we consider the problem of maximizing the lifetime of a target-covering sensor network in which each sensor can adjust its sensing range. The network model consists of a large number of sensors with adjustable sensing ranges being deployed to monitor a set of targets. Since more than one sensor can cover a target, in order to be energy efficient, one can activate successive subsets of sensors that cover all targets. This paper addresses the problem of maximizing the total lifetime of such an activation schedule. In contrast to the approach taken by Cardei et al. (2005), our formulation directly maximizes the network lifetime rather than maximizing the number of sensor covers. We give a mathematical model of this problem using a linear program with exponential number of variables and solve this linear program using the approximation algorithm of Garg-Konemann (1998). Our experimental results on simulated data show a 4times increase in lifetime when compared with the previous approach taken by Cardei et al. (2005)

Energy conservation is an important concern in wireless networks. Many algorithms for constructing a broadcast tree with minimum energy consumption and other goals have been developed. However, no previous research work considers the total energy consumption and transmission delays of the broadcast tree simultaneously. In this paper, based on an (alpha, beta)-tree, a novel concept to wireless networks, we define a new strongly connected broadcast arborescence with bounded transmission delay (SBAT) problem and design the strongly connected broadcast arborescence (SBA) algorithm with linear running time to construct a strongly connected broadcast tree with bounded total power, while satisfying the constraint that the transmission delays between the source and the other hosts are also bounded. We also propose the distributed version of the SBA algorithm. The theoretical analysis and simulation results show that the SBA algorithm gives a proper solution to the SBAT problem

Abstract This paper addresses the problem of broadcasting and multicasting in large scale multihopad hocwireless networks. We focus on the energy‐efficient broadcast routing in stationary networks and consider the case where wireless nodes can dynamically control their transmission power for each broadcast session. Minimum spanning tree (MST) has the property that the longest edge in the tree is the shortest among all the spanning trees. We introduce a new algorithm called minimum longest edge (MLE) that constructs a broadcast tree based on MST, and for networks where nodes have different energy reserves, we introduce minimum weight incremental arborescence (MWIA) algorithm to compute the broadcast tree. Multicast tree can be obtained by pruning broadcast tree. These algorithms provide a scheme to balance the energy consumption among all nodes. The simulation results show that MLE and MWIA improved the energy balance and network lifetime for a wide range of networks, and the improvement is more significant when the network size grows. Copyright © 2006 John Wiley & Sons, Ltd.

A critical aspect of applications with wireless sensor networks is network lifetime. Power-constrained wireless sensor networks are usable as long as they can communicate sensed data to a processing node. Sensing and communications consume energy, therefore judicious power management and sensor scheduling can effectively extend network lifetime. To cover a set of targets with known locations when ground access in the remote area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The lack of precise sensor placement is compensated by a large sensor population deployed in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node (e.g. cluster head) for processing. In this paper we propose un efficient method to extend the sensor network life time by organizing the sensors into a maximal number of set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while all other nodes are in a low-energy sleep mode. By allowing sensors to participate in multiple sets, our problem formulation increases the network lifetime compared with related work [M. Cardei et al], that has the additional requirements of sensor sets being disjoint and operating equal time intervals. In this paper we model the solution as the maximum set covers problem and design two heuristics that efficiently compute the sets, using linear programming and a greedy approach. Simulation results are presented to verify our approaches.

Due to the limited power supplies of a wireless node, energy efficiency is a crucial aspect to the design of a broadcast protocol. In the minimum energy broadcast problem, each node adjusts its transmission power to minimize the total energy consumption. The minimum energy broadcast problem is proved to be NP-Complete. The Hitch-hiking model introduced recently in [M. Agarwal et al., (2004)] takes advantage of the physical layer to combine partial signals containing the same data in order to decode a complete message. Moreover, the wireless multicast advantage (WMA), that is a single transmission can be received by all the nodes that are within the transmission range of a transmitting node, reduces the total energy of the broadcast tree. In this paper, we take advantages of both Hitch-hiking and WMA to design an energy-efficient broadcast tree algorithm with Hitch-hiking (BHH). The simulation results show that BHH reduces the total energy of the broadcast tree greatly.

In mobile ad-hoc networks, hosts communicate with each other without the help of any physical infrastructure. Inevitably, the communication tends to be less efficient in terms of computational and communicational overhead. Recent studies have shown that virtual backbone can help reduce the communication overhead. However, the backbone structure is very vulnerable due to several reasons, e.g., node mobility and unstable links, etc. In this paper, we introduce a localized virtual backbone construction scheme, connected maximal independent set with multiple initiators (MCMIS), which takes node stability into consideration and can construct the backbone quickly. We design MCMIS aiming at three goals: small backbone size, fast construction, stable backbone. Through extensive simulations, we find that our scheme could obtain a better performance on stability and backbone size than other localized schemes.

Abstract Since no fixed infrastructure and no centralized management present in wireless networks, a connected dominating set (CDS) of the graph representing the network is widely used as a virtual backbone. Constructing a minimum CDS is NP‐hard. In this paper, we propose a new greedy algorithm, called S‐MIS, with the help of Steiner tree that can construct a CDS within a factor of 4.8 + ln5 from the optimal solution. We also introduce the distributed version of this algorithm. We prove that the proposed algorithm is better than the current best performance ratio which is 6.8. A simulation is conducted to compare S‐MIS with its variation which is rS‐MIS. The simulation shows that the sizes of the CDSs generated by S‐MIS and rS‐MIS are almost the same. Copyright © 2005 John Wiley & Sons, Ltd.