Information dissemination in dynamic social networks enables fast and frequent access to social news. Thereinto, the coexistence of public and private information creates a hybrid dissemination dynamics process in social networks. However, most existing information dissemination models treat the hybrid information in isolation and fail to consider their interactions through shared nodes and temporal dependencies. Thus, we propose a novel hybrid information dissemination (HID) model that explicitly captures the interconnected dissemination mechanisms of both public and private information within dynamic social networks. Additionally, considering heterogeneity among individuals, we further design a decision-making algorithm for the proposed HID model, aiming at maximizing individuals’ initiative. Furthermore, we derive equilibrium points and analyze their stability for the proposed HID model. Numerous experiments are conducted, and results show that the proposed HID model can effectively describe the dissemination process of hybrid information.

Colorectal cancer (CRC) is a highly heterogeneous disease with distinct molecular subtypes, exhibiting unique transcriptional programs and clinical behaviors. The development of subtype-specific gene regulatory network (GRN) prediction is critical for uncovering the dysregulated transcriptional circuits driving CRC progression, metastasis and therapy resistance. However, the existing GRN prediction methods have limitations in addressing data sparsity and generalization across CRC subtypes, failing to effectively capture subtype-specific regulatory relationships and struggling to handle uncharacterized regulatory factors. In this study, we propose a novel computational framework, Dual-view Contrastive Gene Regulatory Network (DiCoGRN), which integrate structural and semantic information through dual-view contrastive Transformer to infer CRC subtype-specific GRNs. DiCoGRN identifies different cell subpopulations (CRC subtypes) and applies Graph Contrastive Learning (GraphCL) to learn structural embeddings for each subtype. Then, DiCoGRN retrieves the highly variable genes and driver genes of CRC subtypes from the NCBI database, and uses a large language model (LLM) to extract the semantic embeddings of genes. These two-view information are fused through cross-attention and gating interaction mechanisms, ultimately inferring subtype-aware regulatory connections. Experimental results show that the proposed DiCoGRN outperforms existing methods in recovering regulatory interactions and generalization across 12 subtypes. The performed vitro wet experiments illustrate that GATA3 from our predicted regulatory relationships may be a potential CRC driver gene. Our method not only advances CRC subtype-specific GRN inference but also helps to advance toward personalized CRC treatment. Codes and data are available at https://github.com/Fraid-H/DiCoGRN.

Personalized federated learning (PFL) is a distributed learning paradigm designed to address data heterogeneity across clients. While PFL enhances model adaptability through local personalization, achieving a balance between robust privacy protection and effective personalized learning remains a critical challenge—particularly for sensitive data. To address this issue, we propose DFed-LaMA, a novel differentially private PFL framework that incorporates adaptive layer-wise model aggregation, optimizing the trade-off between personalization and privacy. Specifically, clients dynamically identify and personalize the most relevant model layers—determined via Kullback-Leibler divergence between local and global models—before applying differential privacy (DP) perturbation and uploading them to the server. Furthermore, we introduce a model-product integration strategy to align local updates with global objectives, mitigating performance degradation induced by DP noise. Extensive experiments on multiple benchmark datasets demonstrate that DFed-LaMA outperforms state-of-the-art methods in classification accuracy, training stability, and privacy guarantees.

The vulnerability of wireless communication links to eavesdropping poses significant challenges in securing AAV-assisted networks. To enhance security, reconfigurable intelligent surfaces (RIS) and artificial noise (AN) have emerged as promising technologies for mitigating eavesdropping by controlling wireless propagation environments and introducing interference against eavesdroppers. However, existing works have rarely combined transmitter beamforming, RIS, and AN integratedly considered, and leveraging their complementary characteristics for efficient security enhancement remains challenging. Additionally, optimizing such system security performance is complicated by the nonconvexity of secrecy rate maximization and the highly time-varying communication links caused by the mobility of AAVs and users. To address these challenges, we propose a secure communication framework that integrates RIS and AN transmission devices on AAVs. To solve the resulting nonconvex optimization problem, we develop a dual-mode framework based on twin delayed deep deterministic policy gradient (TD3), employing two subenvironments that interact independently before updating a global environment. Extensive simulations demonstrate that the proposed approach significantly enhances secrecy rate performance compared to other methods.

Convolutional neural networks (CNNs) and Transformer-based models have achieved remarkable success in hyperspectral image (HSI) classification tasks due to their outstanding ability to extract spatial and spectral features. However, most existing methods process spatial and spectral features separately, making it difficult to effectively learn their interactive features. To address this issue, we propose a spectral-spatial dual-branch fusion Transformer (S2DBFT) for HSI classification. Initially, we construct a spectral feature extraction module (SPEEM) and a spatial feature extraction module (SPAEM) to extract low-level features. These two modules consist of a one-dimensional convolution layer and a two-dimensional convolution layer, respectively, performing shallow extraction of spectral and spatial features. Next, the two feature sets obtained are fused through a weighted fusion process. Additionally, we design a multi-head spectral-spatial self-attention (MHS3A) mechanism to enhance the interactive fusion of spectral and spatial features. Upon completion of feature fusion, a linear layer is used to obtain the sample labels. Extensive experiments on four HSI datasets demonstrate the effectiveness of the proposed S2DBFT, compared to existing state-of-the-art methods. In terms of performance evaluation, the overall accuracy and average accuracy indicate the superiority and generalizability of S2DBFT.

As wind power systems expand in size and complexity, the imperative for cyber security measures becomes increasingly critical, especially in light of the escalating threat posed by the zero-dynamics attack. Unfortunately, when the relative degree of a continuous-time system is greater than two and the sampling period is small, the sampled-data system will inevitably introduce unstable zeros, making it vulnerable to attackers. Meanwhile, it is difficult for traditional output-based system monitoring methods to detect such attacks. The current mainstream approach employs the generalized hold or generalized sampler to achieve zero shifting, but the implementation is challenging. In this article, we propose a defense method based on electronically tunable passive components to address this challenge. The method effectively defends against the zero-dynamics attack by introducing a gain-scheduling combined with the real-time parameter adjustment function of the electronically tunable passive components to move the unstable zeros of the system. Simulation results show that this defense method can improve the ability of the wind power system to resist zero-dynamics attack.

The Controller Area Network (CAN) serves as a pivotal communication protocol for Electronic Control Units (ECUs) in modern automotive systems. However, the increasing interconnectivity and sophistication of these ECUs introduce significant vulnerabilities, rendering in-vehicle networks susceptible to a variety of cyber threats. This work presents a multi-class classification model for intrusion detection within vehicle CAN networks, utilizing Bidirectional Long Short-Term Memory (BILSTM) and Parc-Net architectures. We also introduce a novel feature extraction module that computes the rate of ID changes, significantly enhancing the model's detection capabilities. The proposed method was evaluated on the CAR-HACKING dataset, demonstrating remarkable performance. The proposed Intrusion Detection System (IDS) greatly contributes to the security of vehicle CAN networks by facilitating real-time detection and localization of potential intrusions.

Numerous studies have demonstrated, in-depth, the vulnerability of the deep reinforcement learning (DRL) model’s elements (e.g., reward), which is a factor limiting the widespread deployment of DRL in some crucial domains, including intelligent traffic signal control (ITSC). While partial poisoning attacks with insidious rewards are enabled undetectable by directly employing regularization or cumulative reward restrictions, these constraints are somewhat 1-D and fail to consider the time dependence of DRL. Moreover, the adversary should avoid injecting undesirable perturbations when agents’ policies are unstable, namely, effectively maximizing the attacking strategy’s benefit. It is thus a challenge to perturb the DRL model stealthily with as few disruption steps or modifications to the original sample as possible while ensuring the attack’s efficiency. In this work, two black-box reward space attack strategies are introduced, where we encourage the adversary to learn a malicious adversarial policy actively. The first is the Multiconstraint Stealthy Time Attack which is updated with the penalties earned by attacking crucial moments, and restricted through action confidence and perturbations’ total number, to ensure attack times’ stealthiness. The second technique is the multiobjective stealthy modification attack which is modeled as a multiobjective optimization problem, and the adversary balance attack performance and stealthy modification with weighting factor$\omega $. Extensive simulation results evaluated in SUMO, involving comparison assessment and attack distribution, exhibit a dramatic increase in average travel time, implying that our attacks impose pressure on the traffic flow, namely, the efficacy of proposed attack strategies.

With the widespread application of collaborative learning (CL) technology in mobile-crowdsourcing-related scenarios, special attention should be paid to the privacy disclosure problem therein. Many pioneer noise-perturbation-based methods, particularly the differentially private ones, provide only homogeneous protection, which is insufficient for the heterogeneous protection requirements of many practical CL cases. In this article, we propose a privacy-aware mechanism that uses appropriate Gaussian noises to obfuscate the local and aggregated models. The noise variance is determined based on clients’ different privacy requirements. By zero-concentrated differential privacy, we analyze clients’ privacy-preserving degrees (PPDs) in the uplink and downlink channels. The obtained PPDs demonstrate that the information received by the aggregating server and the peer clients has distinct preservation effects, indicating that our scheme achieves the goal of heterogeneous protection. Moreover, we conduct a theoretical analysis of the performance of the global models aggregated during the iterative process. Finally, we validate the correctness of our theory with experimental results using a real-world data set.

Multiagent reinforcement learning (MARL) promises outstanding performance for multiintersection traffic signal control systems (TSCS), enabling intelligent administration of cities. However, the vulnerability of MARL algorithms to adversarial attacks has raised concerns about the security of TSCS. In this article, we explore the robustness of MARL-based TSCS against adversarial attacks, propose a black-box multiobject attack strategy, and assign an attack budget to ensure stealthiness. We design a dynamic threshold-based selection of critical states to minimize the cumulative reward with a limited number of attacks. In addition, we present a lightweight agnostic dynamic threshold-based defense mechanism by enhancing the worst-case performance of the policy. We formulate it as a min-max optimization problem, i.e., minimizing the quantity of training sample alterations while maximizing the cumulative discount reward of policy against the perturbed states. Extensive experiments on simulation of urban mobility (SUMO) demonstrate that the proposed attack policy can significantly reduce the performance of TSCS.

Severe damage caused by transferable adversarial attacks has emerged as a prominent concern in recent years, especially in the smart grid. The security issue of the deep reinforcement learning (DRL)-based dynamic pricing system is directly related to the grid's reliability. Previous works have primarily focused on the attacks' transferability from the perspective of model architecture, whereas the concept of distribution bias offers a novel and relatively underexplored viewpoint. In this work, we propose transferable adversarial attacks with distribution (TAD) targeting the DRL model. The adversary emphasizes destroying the target model with the masqueraded malicious dataset while ensuring stealthiness. Concretely, the masqueraded dataset generated by the attacker is required to have a similar distribution to the original dataset, while perturb some of the critical samples to help the target model misdirect to the nonoptimal policy. To this end, we propose an innovative model named Masquerader, which leverages a variational auto-encoder and incorporates three elaborate loss functions to constrain the distribution and deviation of malicious samples. Extensive experiments in a DRL-based dynamic pricing system indicate that our attack strategy TAD could successfully perturb the target model's output. The aberrant flatness of retail prices and the grid system's reduction in daily profits further validate the attack's transferability and harmfulness.

With industrial control systems (ICSs) commonly connected to the cloud, the security of ICS has received widespread attention. Intrusion detection systems (IDSs) are widely employed to protect ICSs, yet most existing intrusion detection models require expert systems to select the important features and large amount of storage space to store data, both result in increased costs. This paper proposes a preprocessing approach based on hash functions, which does not require prior knowledge and saves a lot of storage space. First, we compute a hash code for each piece of original data with the hash function. Next, the hash codes are converted to decimal and normalised. Finally, a number between 0 and 1 is obtained as a feature for intrusion detection. In experiments, we combine our method with various machine learning algorithms, and extensive experimental results illustrate that our method combined with support vector machine (SVM) and k-nearest neighbor (K-NN) achieve good detection results.

In this paper, we address the issue of jamming attacks in the field of maritime communication and propose the application of reconfigurable intelligent surfaces (RISs) in anti-jamming communication at sea. The RIS is installed on an Unmanned surface vehicle (USV) to construct a RIS-assisted USV relay communication system, which mitigates jamming attacks while enhancing legitimate transmissions. Compared to traditional static RIS, the use of mobile USV with RIS enables better performance and greater flexibility. We jointly optimize the trajectory of the USV, the passive beamforming of the RIS, and the source power allocation for each time slot based on proximal policy optimization (PPO), aiming to maximize the average downlink throughput. Simulation results demonstrate that the deployment of RIS on USV effectively suppresses jamming attacks and protects legitimate transmissions.

Mobile edge computing (MEC) is becoming popular in many civilian applications. However, due to the broadcast nature of wireless connections, traditional MEC is open to eavesdropping attacks that endanger mobile users’ information confidentiality. Friendly jamming (FJ), as one of the physical layer security (PLS) techniques, can efficiently protect users from such threats by degrading attackers’ wiretap channel capacities. In this article, we propose an FJ-based PLS mechanism to safeguard the confidentiality of data uploading in MEC services. We design a comprehensive security scheme that uses FJ from both base station (BS) and nearby mobile users to cover upload transmission. Accordingly, the eavesdropping risk zone (ERZ) is formulated as an area under high secrecy outage probability (SOP). To promote FJ from users, we further introduce Device-to-device (D2D) communication-based FJ, which is inspired by nonorthogonal multiple access (NOMA) techniques. Then, we formulate, simplify, and solve the problem by optimizing the MEC secrecy performance under the required risk, with efficiencies in data uploading, energy consumption, and incentive delivery. Furthermore, we tackle the challenge of efficient incentive design under information asymmetry by introducing Contract Theory and providing differentiated rewards. By simulations, we evaluate the mechanism in terms of secrecy performance and incentive efficiency under information asymmetry, and the results accordingly show the effectiveness of the proposed security mechanism.

Distributed classification learning (DCL) is a promising solution to establish Internet of Things-based smart applications, especially due to its strong ability in dealing with large-scale and high-concurrency data. However, the performance of DCL may be seriously affected by the label flipping attack (LFA). Regarding the LFA-resilient learning problem, most existing works are built in more centralized settings. The work addressing the secure DCL issue makes an assumption that the label flipping rates are symmetric and available for scheme design. In this article, we remove this assumption and propose an LFA-resilient DCL scheme, named FENDER, without knowing the asymmetric flipping rates. The challenge is to guarantee both attack resilience and algorithm convergence. We carefully integrate a resilient loss and the alternating direction method of the multiplier scheme, making FENDER resilient to LFA. Further, we systematically analyze the performance of FENDER according to a metric reflecting the models obtained by all the servers at different iterations. In addition, we discuss and compare FENDER with some existing methods from the aspects of algorithm establishment and performance guarantee. Finally, extensive experiments with multiple real-world data sets are performed to validate the developed theory and evaluate the performance of the trained models.

The problem of multiple integrity attacks (attackers) detection and identification (MIADI) in cyber–physical systems (CPSs) is still a challenging problem to date. The goal of this article is to develop a knowledge-based method capable of simultaneously detecting and identifying multiple integrity attacks aiming at different sensors in a CPS. In this article, with the help of labeled random finite set (RFS) theory, a new solution to solve the MIADI problem is proposed. The main contributions of this article lie in the following two aspects, the first is the novel formulation of the MIADI problem, in which labeled RFSs are used to model the behaviors of multiple integrity attackers for the first time, and the second is the proposed labeled RFS-based solution, which provides an elegant framework to cope with the MIADI problem. Numerical experiments are conducted and experimental results demonstrate the effectiveness of the proposed solution. This proposed solution further extends the feasibility of the labeled RFS theory in the context of CPSs cybersecurity.

This article is concerned with the problem of multiple attacks detection (MAD) for distributed sensor networks (SNs) under multiple malicious attacks. The goal of this article is to develop an effective method capable of simultaneously detecting multiple attacks in distributed SNs. By integrating the theories of random finite set (RFS), fusion rules, and consensus, a novel distributed filter named consensus arithmetic average Gaussian mixture probability hypothesis density (AA-GMPHD) filter is proposed in this article, which can achieve the simultaneous detection of multiple attacks in the context of distributed SNs. The main contribution of this article, lies in the proposed consensus AA-GMPHD filter that solves the MAD problem in distributed SNs for the first time. Simulation experiments confirm the effectiveness of the proposed filter for the distributed MAD problem in the context of distributed SNs.

With the huge advantages of iris in authentication and identification, research on protecting iris information in real-world applications has gradually become an important research topic. In this paper, a method to hide the real feature information of the iris is proposed. Such an algorithm aims to prevent iris information from being maliciously utilized otherwise there will be serious consequences. In particular, we focus on accurately manipulating the iris features and ensuring that this method can also prevent iris distortion, thus people could hardly pick out the difference between before and after modification with the naked eye. Furthermore, we verify and evaluate our method and its effect through similarity detection and feature matching. The results show that it has a good performance.

In this article, we investigate the privacy issue of the remote state estimation problem in cyber-physical systems. Specifically, in the presence of an eavesdropper, a sensor observes a discrete linear time-invariant process and then sends the measurements to a remote state estimator with arbitrary finite kinds of transmission options through an unreliable wireless channel. The transmission options of the sensor are in silence state or transmitting aided by injection noise with different energy levels. The eavesdropper wiretaps the channel when the sensor transmits packets to the estimator. Aiming at minimizing the remote estimation error and the cost of the sensors transmission energy while maximizing the eavesdropper state estimation error, we theoretically prove that there exist some structural properties for the optimal transmission schedule for both the known and the unknown eavesdropper's estimation errors. Numerical simulation results are provided to validate the theoretical analysis.

Similarity measure has long played a critical role and attracted great interest in various areas such as pattern recognition and machine perception. Nevertheless, there remains the issue of developing an efficient two-dimensional (2D) robust similarity measure method for images. Inspired by the properties of subspace, we develop an effective 2D image similarity measure technique, named transformation similarity measure (TSM), for robust face recognition. Specifically, the TSM method robustly determines the similarity between two well-aligned frontal facial images while weakening interference in the face recognition by linear transformation and singular value decomposition. We present the mathematical features and some odds to reveal the feasible and robust measure mechanism of TSM. The performance of the TSM method, combined with the nearest neighbor rule, is evaluated in face recognition under different challenges. Experimental results clearly show the advantages of the TSM method in terms of accuracy and robustness.

To invade a cyber-physical system (CPS) successfully, hackers are prone to simultaneously launching multiple cyber attacks on different sensors in a CPS. However, little attention has been paid to the problem of detecting multiple cyber attacks up to now. Therefore, in this paper, we deal with the problem on how to efficiently detect multiple cyber attacks aiming at different sensors in CPSs. To achieve the goal of simultaneously detecting both the number of attacks and the attacked sensors, we formulate this problem via a random finite set (RFS) theory, and then apply an iterative RFS-based Bayesian filter and its approximation to solve the problem. Four numerical experiments with different attacks are provided, and the results have demonstrated the effectiveness of the RFS-based approach for the problem of multiple attacks detection in CPSs.

Database-driven cognitive radio has been well recognized as an efficient way to reduce interference between Primary Users (PUs) and Secondary Users (SUs). In database-driven cognitive radio, PUs and SUs must provide their locations to enable dynamic channel allocation, which raises location privacy breach concern. Previous studies only focus on unilateral privacy preservation, i.e., only PUs' or SUs' privacy is preserved. In this paper, we propose to protect bilateral location privacy of PUs and SUs. The main challenge lies in how to coordinate PUs and SUs to maximize their utilities provided that their location privacy is protected. We first introduce a quantitative method to calculate both PUs' and SUs' location privacy, and then design a novel privacy preserving Utility Maximization protocol (UMax). UMax allows for both PUs and SUs to adjust their privacy preserving levels and optimize transmit power iteratively to achieve the maximum utilities. Through extensive evaluations, we demonstrate that our proposed protocol can efficiently increase the utilities of both PUs and SUs while preserving their location privacy.

This article investigates the security issue based on the proactive channel hopping scheme in wireless networked control systems (WNCS), where the sensor sends the measurement data to the controller through multiple channels attacked by a periodic denial-of-service jammer. For a jammer with the limited energy, the number of attacks, which denotes the amount of time expended in launching the attack, increases with the decline in the channel number attacked at each time, which has an effect on the success of receiving the data. On the basis of this, the problem of making an optimal tradeoff between the channel number attacked at each time and the attack number to degrade the WNCS performance maximally is investigated. We formulate this problem as an integer programming problem based on the linear quadratic Gaussian control cost function. Then, a necessary condition of the optimal solution without integrality constraints for this optimization problem is given for the scenario where the sensor utilizes one channel at each time to perform the data transmission. We further investigate this problem for the case where the sensor can select several channels at each time to transmit the measurement data. Moreover, for both cases the corresponding algorithms are presented to approximate optimal schedules. Finally, the theoretical results and the validity of the algorithm proposed are verified via the numerical results.

Model-free predictive control is a novel data-driven control approach. It can calculate directly the control input by using a great deal of input and output datasets. Moreover, compared with the conventional model predictive control, it does not need to construct a precise mathematical model. In this work, we propose the model-free predictive control to the motion control of the autonomous underwater vehicle(AUV). The nonlinear motion control problem of AUV is effectively solved and the proposed approach can enhance the performance of trajectory tracking for AUV. At last, we demonstrate the availability of this method by the numerical simulations of trajectory tracking.

Detection of minimal repeated pattern (MRP) is an indispensable ingredient of the color dividing and plate-making system in the IoT-based printing fabric industry. We present a minimal repeated pattern detection problem and then design an effective and efficient neighbor similarity point location (NSPL) method for automatic detection of PRP. We theoretically reveal the effective working mechanism of NSPL and provide a specific implementation by transforming it into a sub-pattern retrieval problem. Evaluation on the printing fabric image set and the experimental results demonstrate that the NSPL is very suitable for the detection of the minimal repeated pattern of printed images and achieves greater performance than the manual and other existing automatic detection methods in detection accuracy, efficiency, and robustness.

Wireless energy harvesting is an emerging technology in the Internet of Things (IoT). Plenty of recent literature focused on balancing the information transfer and energy harvesting at the same time. Different from these works, we jointly consider the remote state estimation and wireless energy harvesting in IoT, and introduce a new cost function which is the weighted difference between the remote state estimation error at the estimator side and the harvested energy at the energy receiver side. In our framework, the collection of communication channel states is known asa prioriknowledge and the real-time channel state is not known. We consider the scenario that the transmitter can send the observation data for remote estimation and deliver the power for energy harvesting concurrently. A robust transmission power split algorithm is provided to minimize the cost function for the worst-case channel state. In addition, another robust power switch algorithm is designed for the scenario that the transmitter can only decide to send the observation data or deliver the power for energy harvesting at any time slot. At last, simulation examples are provided to show the effectiveness of our proposed robust transmission power allocation policies.

Interval programming problems are ubiquitous in real-world situations. There exist a variety of theories and methods for handling them; the existing methods, however, have adopted various dominance criteria to distinguish solutions, and these criteria are always subjective. Different dominance criteria will produce different optimal solution(s), and subjective criteria make users, especially for those who are not familiar with interval arithmetic, difficult to choose, which restricts their widespread applications. In this study, the idea of ensemble dominance on intervals for tackling these problems is proposed. Dominance criteria on intervals are first defined, and their correlations are depicted by equivalent, inclusive and non-included relations; then, a reduction scheme is derived by investigating the influence of different criteria on the ordering of solutions, and a novel ensemble dominance relation on intervals is defined to rationally and equally evaluate the quality of a solution; furthermore, the complexity of the proposed method is analyzed; finally, empirical results indicate the effectiveness of the proposed method.

Electronic countermeasure ECM attack has been an emerging threat to radar network in recent years. It is necessary to design a secured radar network against ECM attack. In this paper, we prove that the radar network with conventional measurement fusion schemes is insecure to deceptive ECM DECM attack. Then, a new measurement fusion scheme is proposed, which shows better security performance when DECM attack happens. Numerical simulations are presented to demonstrate the effectiveness of the proposed measurement fusion scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Database-driven cognitive radio has been well recognized as an efficient way to reduce interference between Primary Users (PUs) and Secondary Users (SUs). In database-driven cognitive radio, PUs and SUs must provide their locations to enable dynamic channel allocation, which raises location privacy breach concern. Previous studies only focus on unilateral privacy preservation, i.e., Only PUs' or SUs' privacy is preserved. In this paper, we propose to protect bilateral location privacy of a PU and an SU. The main challenge lies in how to coordinate the PU and SU to maximize their utility provided that their location privacy is protected. We first introduce a quantitative method to calculate both PU's and SU's location privacy, and then design a novel privacy preserving Utility Maximization protocol (UMax). UMax allows for both PU and SU to adjust their privacy preserving levels and optimize transmit power iteratively to achieve the maximum utility. Through extensive evaluations, we demonstrate that our proposed mechanism can efficiently increase the utility of both PU and SU while preserving their location privacy.

Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.