Neighboring Sensors Research Articles

Secure data collection and transmission for IoMT architecture integrated with federated learning

PurposeThe purpose of this paper is to secure health data collection and transmission (SHDCT). In this system, a native network consists of portable smart devices that interact with multiple gateways. It entails IoMT devices and wearables connecting to exchange sensitive data with a sensor node which performs the aggeration process and then communicates the data using a Fog server. If the aggregator sensor loses the connection from the Fog server, it will be unable to submit data directly to the Fog server. The node transmits encrypted information with a neighboring sensor and sends it to the Fog server integrated with federated learning, which encrypts data to the existing data. The fog server performs the operations on the measured data, and the values are stored in the local storage area and later it is updated to the cloud server.Design/methodology/approachSHDCT uses an Internet-of-things (IoT)-based monitoring network, making it possible for smart devices to connect and interact with each other. The main purpose of the monitoring network has been in the collection of biological data and additional information from mobile devices to the patients. The monitoring network is composed of three different types of smart devices that is at the heart of the IoT.FindingsIt has been addressed in this work how to design an architecture for safe data aggregation in heterogeneous IoT-federated learning-enabled wireless sensor networks (WSNs), which makes use of basic encoding and data aggregation methods to achieve this. The authors suggest that the small gateway node (SGN) captures all of the sensed data from the SD and uses a simple, lightweight encoding scheme and cryptographic techniques to convey the data to the gateway node (GWN). The GWN gets all of the medical data from SGN and ensures that the data is accurate and up to date. If the data obtained is trustworthy, then the medical data should be aggregated and sent to the Fog server for further processing. The Java programming language simulates and analyzes the proposed SHDCT model for deployment and message initiation. When comparing the SHDCT scheme to the SPPDA and electrohydrodynamic atomisation (EHDA) schemes, the results show that the SHDCT method performs significantly better. When compared with the SPPDA and EHDA schemes, the suggested SHDCT plan necessitates a lower communication cost. In comparison to EHDA and SPPDA, SHDCT achieves 4.72% and 13.59% less, respectively. When compared to other transmission techniques, SHDCT has a higher transmission ratio. When compared with EHDA and SPPDA, SHDCT achieves 8.47% and 24.41% higher transmission ratios, respectively. When compared with other ways it uses less electricity. When compared with EHDA and SPPDA, SHDCT achieves 5.85% and 18.86% greater residual energy, respectively.Originality/valueIn the health care sector, a series of interconnected medical devices collect data using IoT networks in the health care domain. Preventive, predictive, personalized and participatory care is becoming increasingly popular in the health care sector. Safe data collection and transfer to a centralized server is a challenging scenario. This study presents a mechanism for SHDCT. The mechanism consists of Smart healthcare IoT devices working on federated learning that link up with one another to exchange health data. Health data is sensitive and needs to be exchanged securely and efficiently. In the mechanism, the sensing devices send data to a SGN. This SGN uses a lightweight encoding scheme and performs cryptography techniques to communicate the data with the GWN. The GWN gets all the health data from the SGN and makes it possible to confirm that the data is validated. If the received data is reliable, then aggregate the medical data and transmit it to the Fog server for further process. The performance parameters are compared with the other systems in terms of communication costs, transmission ratio and energy use.

Fuzzy TOPSIS-based Secure Neighbor Discovery Mechanism for Improving Reliable Data Dissemination in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) being an indispensable entity of the Internet of Things (IoT) are found to be more and more widely utilized for the rapid advent of IoT environment. The reliability of data dissemination in the IoT environment completely depends on the secure neighbor discovery mechanism that are utilized for effective and efficient communication among the sensor nodes. Secure neighbor discovery mechanisms that significantly determine trustworthy sensor nodes are essential for maintaining potential connectivity and sustaining reliable data delivery in the energy-constrained self organizing WSN. In this paper, Fuzzy Technique of Order Preference Similarity to the Ideal Solution (TOPSIS)-based Secure Neighbor Discovery Mechanism (FTOPSIS-SNDM) is proposed for estimating the trust of each sensor node in the established routing path for the objective of enhancing reliable data delivery in WSNs. This proposed FTOPSIS-SNDM is proposed as an attempt to integrate the merits of Fuzzy Set Theory (FST) and TOPSIS-based Multi-criteria Decision Making (MCDM) approach, since the discovery of secure neighbors involves the exchange of imprecise data and uncertain behavior of sensor nodes. This secure neighbor is also influenced by the factors of packet forwarding potential, delay, distance from the Base Station (BS) and residual energy, which in turn depends on multiple constraints that could be possibly included into the process of secure neighbor discovery. The simulation investigations of the proposed FTOPSIS-SNDM confirmed its predominance over the benchmarked approaches in terms of throughput, energy consumption, network latency, communication overhead for varying number of genuine and malicious neighboring sensor nodes in network.

On Evaluating IoT Data Trust via Machine Learning

Data trust in IoT is crucial for safeguarding privacy, security, reliable decision-making, user acceptance, and complying with regulations. Various approaches based on supervised or unsupervised machine learning (ML) have recently been proposed for evaluating IoT data trust. However, assessing their real-world efficacy is hard mainly due to the lack of related publicly available datasets that can be used for benchmarking. Since obtaining such datasets is challenging, we propose a data synthesis method, called random walk infilling (RWI), to augment IoT time-series datasets by synthesizing untrustworthy data from existing trustworthy data. Thus, RWI enables us to create labeled datasets that can be used to develop and validate ML models for IoT data trust evaluation. We also extract new features from IoT time-series sensor data that effectively capture its autocorrelation as well as its cross-correlation with the data of the neighboring (peer) sensors. These features can be used to learn ML models for recognizing the trustworthiness of IoT sensor data. Equipped with our synthesized ground-truth-labeled datasets and informative correlation-based features, we conduct extensive experiments to critically examine various approaches to evaluating IoT data trust via ML. The results reveal that commonly used ML-based approaches to IoT data trust evaluation, which rely on unsupervised cluster analysis to assign trust labels to unlabeled data, perform poorly. This poor performance is due to the underlying assumption that clustering provides reliable labels for data trust, which is found to be untenable. The results also indicate that ML models, when trained on datasets augmented via RWI and using the proposed features, generalize well to unseen data and surpass existing related approaches. Moreover, we observe that a semi-supervised ML approach that requires only about 10% of the data labeled offers competitive performance while being practically more appealing compared to the fully supervised approaches. The related Python code and data are available online.

A Q-Learning-Based Routing Approach for Energy Efficient Information Transmission in Wireless Sensor Network

Nowadays, wireless sensor networks have played an important role in many applications. In these applications, a large number of wireless sensors are deployed in an environment to collect information and form network to transmit collected information to the base station or sink. Because wireless sensors have to work for a long period of time without maintenance, the energy consumption of wireless sensors has a great impact on the lifetime of wireless sensor network. To reduce and balance the energy consumption of wireless sensors, many information transmission routing approaches have been proposed. However, most of them do not consider all energy consumption factors of wireless sensors. To this end, an innovative information transmission routing approach based on Q-learning is proposed in this paper, which enables wireless sensors to adaptively select suitable neighboring sensors to achieve energy efficient information transmission in a decentralized manner. Based on the proposed approach, a wireless sensor first collects state and action information of its neighboring sensors, which includes information transmission direction and distance, the remaining energy, the energy consumption for information transmission and information transmission action. Then, all this information is used to update the Q-values of neighboring sensors, so as to enable the wireless sensor to select suitable neighboring sensor to transmit information according to Q-values. From simulation experiments, it can be seen that the proposed approach enables wireless sensors to reduce and balance the energy consumption of wireless sensors and extend the lifetime of the entire wireless sensor network.

Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture.

A significant technological transformation has recently occurred in the agriculture sector. Precision agriculture is one among those transformations that largely focus on the acquisition of the sensor data, identifying the insights, and summarizing the information for better decision-making that would enhance the resource usage efficiency, crop yield, and substantial quality of the yield resulting in better profitability, and sustainability of agricultural output. For continuous crop monitoring, the farmlands are connected with various sensors that must be robust in data acquisition and processing. The legibility of such sensors is an exceptionally challenging task, which needs energy-efficient models for handling the lifetime of the sensors. In the current study, the energy-aware software-defined network for precisely selecting the cluster head for communication with the base station and the neighboring low-energy sensors. The cluster head is initially chosen according to energy consumption, data transmission consumption, proximity measures, and latency measures. In the subsequent rounds, the node indexes are updated to select the optimal cluster head. The cluster fitness is assessed in each round to retain the cluster in the subsequent rounds. The network model's performance is assessed against network lifetime, throughput, and network processing latency. The experimental findings presented here show that the model outperforms the alternatives presented in this study.

CSCP: Energy Charging Mechanism for Surveillance Quality, Network Connectivity and Perpetual Lifetime in WRSNs

<p>In recent years, wireless charging techniques applied to sensor networks have been widely studied. The sensors in most studies are considered to be equally important, and the main purpose is to maintain the working sensors as many as possible. However, different sensors have different contributions to the surveillance application, especially for network connectivity and surveillance quality. The sensor closer to the base station has a larger contribution to network connectivity since its failure can block more data transmissions. On the other hand, a sensor in a sparse area has a larger contribution to surveillance quality because few or no neighboring sensors can execute the sensing operation instead of the sensor if it is energy exhaustion. This paper proposes an energy recharging mechanism, called CSCP, which adopts the mobile charger to recharge the sensors according to the recharging requests. The proposed CSCP considers the contribution of each grid in terms of network connectivity, surveillance quality as well as path cost, aiming to recharge the grids where the sensors can maximize the surveillance quality. Performance research reveals that the proposed CSCP has better performance than the related studies in the surveillance quality, the number of working sensors, and recharging efficiency. </p> <p> </p>

Leader-following consensus of multi-agent systems with connectivity-mixed attacks and actuator/sensor faults

This study investigates the leader-following consensus issue of multi-agent systems subject to simultaneous connectivity-mixed attacks, actuator/sensor faults and disturbances. Connectivity-mixed attacks are remodeled into connectivity-maintained and connectivity-paralyzed topologies in a switched version, and actuator/sensor faults are established with unified incipient-type and abrupt-type characteristics. Then, unknown input observer-based decoupling and estimation are incorporated to achieve unknown state and fault observations with the normalized technique, and the leader-following consensus-based compensation to faults, resilience to attacks and robustness to disturbances are also realized with the neighboring output information and sensor fault estimation through the distributed framework. Criteria of achieving exponential leader-following consensus of multi-agent systems under cyber-physical threats are derived with dual attack frequency and activation rate indicators. Simulation example is conducted to exemplify the validation and merits of the proposed leader-following consensus algorithm.

Dynamic Event-Triggered Approach for Distributed State and Parameter Estimation Over Networks Subjected to Deception Attacks

This study is focused on addressing the dynamic event-triggered distributed state and unknown parameter esti- mation problem for discrete-time nonlinear systems that have known linear dynamics and unknown nonlinearities and are subject to deception attacks. A neural-network-based unified estimation framework is introduced to estimate the unknown nonlinear function in conjunction with the system state and unknown parameters. Each sensor uses its own measurements and data from the neighboring sensors to calculate the overall estimates. The information-sharing network is assumed to be vulnerable to deception attacks, which are modeled using a Bernoulli distributed random variable. Additionally, a dynamic event-triggered strategy is adopted to alleviate resource consump- tion. Based on Lyapunov theory, the stability of the unified estimation framework is proven in terms of the uniformly ultimately bounded error. Moreover, the design conditions for the estimator are presented in the form of matrix inequalities. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed framework.

An efficient fault-tolerant distributed Bayesian filter based on conservative fusion

This paper proposes a fault-tolerant distributed Bayesian filter for multi-sensor state estimation using a peer-to-peer sensor network with incoherent local estimates problems. The proposed approach uses a Gaussian mixture rather than a single Gaussian distribution to represent the fusion result, which can effectively reduce the negative impact of corrupted local estimates on the fusion results. The resulting filter performs Bayesian recursion via Gaussian mixture. To accommodate a heterogeneous sensor network, we develop a novel arithmetic average fusion employing a set of covariance-dependent weighting coefficients, where the fusion error covariance is effectively reduced in the case of fusing information with different qualities. For intersensor communication, a partial flooding scheme is investigated, in which only valid-likely Gaussian components are disseminated and fused between neighbor sensors. Theoretically, it is shown that under reasonable assumptions, the presented fault-tolerant distributed estimator can guarantee local stability with the exponentially bounded estimation error in the mean square. The effectiveness and superiority of our approach are validated through both simulation and experiment scenarios.

Adaptive graph convolutional imputation network for environmental sensor data recovery

Environmental sensors are essential for tracking weather conditions and changing trends, thus preventing adverse effects on species and environment. Missing values are inevitable in sensor recordings due to equipment malfunctions and measurement errors. Recent representation learning methods attempt to reconstruct missing values by capturing the temporal dependencies of sensor signals as handling time series data. However, existing approaches fall short of simultaneously capturing spatio-temporal dependencies in the network and fail to explicitly model sensor relations in a data-driven manner. In this work, we propose a novel Adaptive Graph Convolutional Imputation Network for missing value imputation in environmental sensor networks. A bidirectional graph convolutional gated recurrent unit module is introduced to extract spatio-temporal features which takes full advantage of the available observations from the target sensor and its neighboring sensors to recover the missing values. In addition, we design an adaptive graph learning layer that learns a sensor network topology in an end-to-end framework, in which no prior network information is needed for capturing spatial dependencies. Extensive experiments on three real-world environmental sensor datasets (solar radiation, air quality, relative humidity) in both in-sample and out-of-sample settings demonstrate the superior performance of the proposed framework for completing missing values in the environmental sensor network, which could potentially support environmental monitoring and assessment.

Robustness Analysis of Distributed Kalman Filter for Estimation in Sensor Networks.

Motivated by the guaranteed stability margins of linear quadratic regulators (LQRs) and standard Kalman filter (KF) in the frequency domain, this article extends these results to the distributed Kalman-consensus filter (DKCF) for distributed estimation in sensor networks. In particular, we study the robustness margins of DKCF in two cases, one of which is based on the direct target observation while the other uses estimates from neighbor sensors in the network. The loop transfer functions of the two cases are established, and gain and phase margin robustness results are derived for both. The robustness margins of DKCF are improved compared to the single-agent KF. Furthermore, as communication topology varies in sensor networks, graph overall coupling strengths change. We also analyze the correlation between overall coupling strengths and the robustness margins of DKCF.

Assembly Performance Evaluation for Prefabricated Steel Structures Using k-nearest Neighbor and Vision Sensor

Assembly Performance Evaluation for Prefabricated Steel Structures Using k-nearest Neighbor and Vision Sensor

Interleaved Honeypot-Framing Model with Secure MAC Policies for Wireless Sensor Networks

The Wireless Medium Access Control (WMAC) protocol functions by handling various data frames in order to forward them to neighbor sensor nodes. Under this circumstance, WMAC policies need secure data communication rules and intrusion detection procedures to safeguard the data from attackers. The existing secure Medium Access Control (MAC) policies provide expected and predictable practices against channel attackers. These security policies can be easily breached by any intelligent attacks or malicious actions. The proposed Wireless Interleaved Honeypot-Framing Model (WIHFM) newly implements distributed honeypot-based security mechanisms in each sensor node to act reactively against various attackers. The proposed WIHFM creates an optimal Wireless Sensor Network (WSN) channel model, Wireless Interleaved Honeypot Frames (WIHFs), secure hash-based random frame-interleaving principles, node-centric honeypot engines, and channel-covering techniques. Compared to various existing MAC security policies, the proposed model transforms unpredictable IHFs into legitimate frame sequences against channel attackers. Additionally, introducing WIHFs is a new-fangled approach for distributed WSNs. The successful development of the proposed WIHFM ensures resilient security standards and neighbor-based intrusion alert procedures for protecting MAC frames. Particularly, the proposed wireless honeypot methodology creates a novel idea of using honeypot frame traps against open wireless channel attacks. The development of a novel wireless honeypot traps deals with various challenges such as distributed honeypot management principles (node-centric honeypot, secretly interleaved-framing principles, and interleaving/de-interleaving procedures), dynamic network backbone management principles (On Demand Acyclic Connectivity model), and distributed attack isolation policies. This effort provides an effective wireless attack-trapping solution in dynamic WSNs. The simulation results show the advantage of the proposed WIHFM over the existing techniques such as Secure Zebra MAC (SZ-MAC), Blockchain-Assisted Secure-Routing Mechanism (BASR), and the Trust-Based Node Evaluation (TBNE) procedure. The experimental section confirms the proposed model attains a 10% to 14% superior performance compared to the existing techniques.

Local residual random forest classifier for strain-based damage detection and localization in aerospace sandwich structures

To ensure the structural integrity of large aerospace structures during operation, structural health monitoring is a major challenge. The monitoring can be performed by distributed strain measurements using strain gauges or fiber optical sensors. In this work, an advanced local residual classifier for strain-based damage detection and localization is introduced. The key principle is that a change in the relationship between a strain sensor and its neighbors indicates the presence of damage. After defining a sensor grid with sensor locations and their orientation, the relationship can be obtained from numerical simulations of the healthy structure. Here, local regression models are estimated between each master sensor and its neighboring sensors. Then, residuals of the predicted and measured strains are evaluated using a random forest classifier. The evaluation of the residuals has the advantage that the method is independent of the load level, as well as the fact that it is independent of certain environmental influences that are uniformly distributed over the entire structure. In addition to the numerical healthy strains, synthetically generated damage data are used for training the classifier. The synthetic data are obtained by statistical modifications of the healthy strains. This procedure avoids time-consuming and expensive damage simulations. The health monitoring approach is applied to a glass fiber reinforced polymer sandwich structure, imitating an aircraft spoiler, with a hole in the face layer considered as damage. The validation is performed by numerical finite element simulations as well as physical experiments under random loading conditions. The results demonstrate the high potential of the presented approach for strain-based structural health monitoring in composite sandwich structures.

Best fit of mixture for multi-sensor poisson multi-Bernoulli mixture filtering

We propose a computationally efficient, the first so far, multi-sensor extension of the Poisson multi-Bernoulli mixture (PMBM) filter that accommodates both centralized and distributed sensor networks. In the distributed case, a distributed flooding algorithm is needed for internode communication, which iteratively shares the relevant multi-target posterior among neighbor sensors, thereby making each local sensor serve like a fusion center in the centralized case. The PMBM posterior yielded at each sensor is decomposed into two components corresponding to undetected and detected targets which are modelled by Poisson and multi-Bernoulli mixture (MBM) distributions, respectively. Both communication and fusion are performed with regard to the latter only. A “best-fit-of-mixture” fusion principle is adopted at each sensor to find an MBM that best fits the mixture of MBMs from distinct sensors, which results precisely in the arithmetic average (AA) of these MBMs. The information divergence of the AA from the true density is analyzed. We also provide a Bayesian model averaging interpretation of the MBM-AA fusion. Simulations in scenarios of different target detection probabilities demonstrate the performance of the proposed PMBM filter in terms of localization error, false-alarm/misdetection errors, and communication and computation costs, in comparison with the AA-based multisensor multi-Bernoulli filter.

IoT and Satellite Sensor Data Integration for Assessment of Environmental Variables: A Case Study on NO2.

This paper introduces a novel approach to increase the spatiotemporal resolution of an arbitrary environmental variable. This is achieved by utilizing machine learning algorithms to construct a satellite-like image at any given time moment, based on the measurements from IoT sensors. The target variables are calculated by an ensemble of regression models. The observed area is gridded, and partitioned into Voronoi cells based on the IoT sensors, whose measurements are available at the considered time. The pixels in each cell have a separate regression model, and take into account the measurements of the central and neighboring IoT sensors. The proposed approach was used to assess NO data, which were obtained from the Sentinel-5 Precursor satellite and IoT ground sensors. The approach was tested with three different machine learning algorithms: 1-nearest neighbor, linear regression and a feed-forward neural network. The highest accuracy yield was from the prediction models built with the feed-forward neural network, with an of 15.49 mol/m.

Distributed filtering in sensor networks based on linear minimum mean square error criterion with limited sensing range

One of the fundamental problems in sensor networks is to estimate and track the target states of interest that evolve in the sensing field. Distributed filtering is an effective tool to deal with state estimation in which each sensor only communicates information with its neighbors in sensor networks without the requirement of a fusion center. However, in the majority of the existing distributed filters, it is assumed that typically all sensors possess unlimited field of view to observe the target states. This is quite restrictive since practical sensors have limited sensing range. In this article, we consider distributed filtering based on linear minimum mean square error criterion in sensor networks with limited sensing range. To achieve the optimal filter and consensus, two types of strategies based on linear minimum mean square error criterion are proposed, that is, linear minimum mean square error filter based on measurement and linear minimum mean square error filter based on estimate, according to the difference of the neighbor sensor information received by the sensor. In linear minimum mean square error filter based on measurement, the sensor node collects measurement from its neighbors, whereas in linear minimum mean square error filter based on estimate, the sensor node collects estimate from its neighbors. The stability and computational complexity of linear minimum mean square error filter are analyzed. Numerical experimental results further verify the effectiveness of the proposed methods.

Selection of Cluster Head in Wireless Sensor Network using Convolution Neural Network Algorithm

Network lifetime enhancement is one of the primary goals for all the Wireless Sensor Network (WSN) applications. Clustering process helps the WSNs to improve its lifetime by forming a group with nearby sensor nodes. A primary node called cluster head is elected from the group for managing the data transmission from the other clustering groups to the basic sensor nodes. Also, the cluster heads are placed to transfer the signal collected from its sensor nodes to the base stations. Identifying an optimum node for making it as a cluster head is a challengeable process for many WSN applications. The proposed work is employed with a neural network algorithm for electing a cluster head by analyzing the residual energy, base station distance and neighbor sensor counts. The experimental work carried out in the paper indicates a betterment on survival node time and energy consumption.

PO-615-08 SINGLE SENSOR VERSUS MULTI-SENSOR ESOPHAGEAL MONITORING: WHAT ARE WE MISSING?

Esophageal temperature monitors are often used to decrease risk of esophageal injury during atrial fibrillation ablation. Multi-sensor monitors have been shown to have a superior thermodynamic profile in radiofrequency ablation, but there is limited data on single versus multi-electrode temperature discrepancies in cryoballoon ablation. We aim to evaluate temperature differences in the Circa S-Cath, a multi-sensor esophageal probe, to investigate its potential benefits over a single sensor probe in cryoablation. A total of 15 patients underwent cryoballoon ablation for atrial fibrillation under general anesthesia. A Circa probe was placed oropharyngeally after intubation. The Circa Esophageal Temperature Monitor has 12 sensors total located 8mm apart. Each sensor has a radiopaque marker and collects a temperature to the nearest 10th of a degree every half second. Position of the Crica probe could be adjusted under fluoroscopy. In the total of 15 patients, a minimum esophageal temperature below 25 °C was recorded in 8 cases during cryoablation. The lowest esophageal temperature did not occur in the same sensor for all cases or even freezes in the same patient. Sensor 7 recorded the coldest temperature in 7/15 of these freezes, Sensor 8 recorded 3/15, Sensor 10 recorded 2/15, and Sensors 4, 5, and 10 recorded one each. When compared to neighboring sensors, the largest difference between neighboring sensors was 13.14 degrees, with a range of 5.6 to 19.9 degrees. The disparity between neighboring temperature sensors suggest that single sensor probes are insufficient to detect the lowest temperature of the esophagus. Continuation of cryoablation to very low temperatures could increase risk of collateral injury to the esophagus. Use of multi-sensor temperature probes helps in accurately reflecting the coldest temperature point of the esophagus during cryoablation. Similar discrepancy may also be seen in radiofrequency ablation.

Distributed Kalman Filter With Faulty/Reliable Sensors Based on Wasserstein Average Consensus

This brief considers distributed Kalman filtering problem for systems with sensor faults. A trust-based classification fusion strategy is proposed to resist against sensor faults. First, the local sensors collect measurements and then update their state estimations and estimation error covariance matrices. Then, sensors exchange the information (state estimations and estimation error covariance matrices) with their neighboring sensors. After obtaining the estimation information from neighboring sensors, an iterative classification/clustering algorithm, which contains three steps ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Initialization Step</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Assignment Step</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Update Step</i> ), is proposed to classify the collected estimations into two clusters (trusted and untrusted clusters). Third, the fused states and error covariance matrices are computed by Wasserstein average algorithm. Finally, the time update is performed on the basis of fusion information. Stability and convergence of the proposed filter are analyzed. A target tracking simulation example is provided to verify the effectiveness of the proposed distributed filter in a wireless sensor network.