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1
Vijay, Kumar. "SURVEY OF FAULT DETECTION ALGORITHM IN WSN." INTERNATIONAL JOURNAL OF RESEARCH- GRANTHAALAYAH 5, no. 5 (2017): 207–13. https://doi.org/10.5281/zenodo.583910.
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In recent years, applications of wireless sensor networks (WSNs) have been improved due to its vast potential to connect the physical world to the virtual world. Also, a progress in microelectronic fabrication technology reduces cost of developed portable wireless sensor nodes. Faults occurring to sensor nodes are familiar due to the sensor device itself and the harsh environment where the sensor nodes are deploy. WSNs is mainly affect by the crash of sensor nodes. Possibility of sensor node failure increases with increase number of sensors. Wireless sensor networks have been recognized, at an early stage in their development, to be a useful measurement technology for environmental monitoring applications. Based on their independence from accessible infrastructures, wireless sensor networks can be deploy in virtually any location and provide sensor samples in a spatial and temporal resolution
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Krishna, M. Sai Rama, Ch Jnana Gayathri, and K. Laxmi Pallavi Rao. "Building Fault Tolerance Within Wsn- A Topology Model." International Journal of Advances in Applied Sciences 7, no. 2 (2018): 135. http://dx.doi.org/10.11591/ijaas.v7.i2.pp135-142.
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<p>Wireless Sensor network plays a crucial role which helps in visualizing, processing, and analyzing the information wirelessly. WSN is a network which consists of huge amount of sensor devices which are of low cost and low powered also known as sensor nodes. These type of networks are generally used in real time applications such as monitoring of environmental conditions, militaries, industries etc.,.but the problem that exists in WSN is may be due to different failures such as node failure, link failure, sink failure, interference, power dissipation and collision. If these faults are unable to handle then the desired network criteria’s may not be reached properly which results in inefficiency of the network. So, the main idea behind the investigation is to form a different networking topology which works in the event of failure</p>
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M., Sai Rama Krishna, Jnana Gayathri Ch., and Laxmi Pallavi Rao K. "Building Fault Tolerance Within Wsn-A Topology Model." International Journal of Advances in Applied Sciences (IJAAS) 7, no. 2 (2018): 135–42. https://doi.org/10.11591/ijaas.v7.i2.pp135-142.
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Wireless Sensor network plays a crucial role which helps in visualizing, processing, and analyzing the information wirelessly. WSN is a network which consists of huge amount of sensor devices which are of low cost and low powered also known as sensor nodes. These type of networks are generally used in real time applications such as monitoring of environmental conditions, militaries, industries etc., .but the problem that exists in WSN is may be due to different failures such as node failure, link failure, sink failure, interference, power dissipation and collision. If these faults are unable to handle then the desired network criteria’s may not be reached properly which results in inefficiency of the network. So, the main idea behind the investigation is to form a different networking topology which works in the event of failure
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Mardenov, Yerik, Aigul Adamova, Tamara Zhukabayeva, and Mohamed Othman. "Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study." Journal of Robotics and Control (JRC) 4, no. 6 (2023): 868–77. http://dx.doi.org/10.18196/jrc.v4i6.20216.
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The Wireless Sensor Network (WSN) consists of many sensors that are distributed in a specific area for the purpose of monitoring physical conditions. Factors such as hardware limitations, limited resources, unfavourable WSN deployment environment, and the presence of various attacks on nodes can lead to the presence of Faulty Nodes in a WSN. This raises the problem of detecting Faulty Nodes and avoiding Data loss. Detecting Faulty Nodes in real-world scenarios will improve the quality of a WSN. The research was aimed at developing an algorithm to determine the location of Faulty Nodes in a WSN. The algorithm uses characteristics of Machine Learning and Support Vector Machines (SVM), which use the classification of Data into true and false. A Mathematical Model for determining Faulty Nodes using the SVM is considered. A methodology for detecting a Faulty Node is demonstrated, which consists of Data Collection, Feature Extraction, Training, and Testing the Model. The Results of simulated experiments that were conducted with different numbers of nodes from 50 to 320 are shown. The Model is tested on Data very similar to real-world sensing Data to evaluate the ability of the Model to detect failed nodes and calculate training and testing errors. As a result, the training error is 4.6667%, the accuracy of detecting faulty nodes was 97%. The simulation results demonstrate the high stability of the proposed algorithm and are suitable for network environments with irregular node distribution or coverage gaps. In real scenarios, it can provide a high level of uninterrupted operation of the WSN and lossless data transmission. Shortcomings and prospects in research on fault detection in WSN, such as studying real-world hardware issues and its security, were presented.
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Smara, Mounya, and Al-Sakib Khan Pathan. "An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks." Network 4, no. 2 (2024): 150–74. http://dx.doi.org/10.3390/network4020008.
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Fault tolerance is a critical aspect for any wireless sensor network (WSN), which can be defined in plain terms as the quality of being dependable or performing consistently well. In other words, it may be described as the effectiveness of fault tolerance in the event of crucial component failures in the network. As a WSN is composed of sensors with constrained energy resources, network disconnections and faults may occur because of a power failure or exhaustion of the battery. When such a network is used for precision agriculture, which needs periodic and timely readings from the agricultural field, necessary measures are needed to handle the effects of such faults in the network. As climate change is affecting many parts of the globe, WSN-based precision agriculture could provide timely and early warnings to the farmers about unpredictable weather events and they could take the necessary measures to save their crops or to lessen the potential damage. Considering this as a critical application area, in this paper, we propose a fault-tolerant scheme for WSNs deployed for precision agriculture. Along with the description of our mechanism, we provide a theoretical operational model, simulation, analysis, and a formal verification using the UPPAAL model checker.
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Hu, Jiamin, Xiaofan Yang, and Luxing Yang. "A Novel Diagnosis Scheme against Collusive False Data Injection Attack." Sensors 23, no. 13 (2023): 5943. http://dx.doi.org/10.3390/s23135943.
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The collusive false data injection attack (CFDIA) is a false data injection attack (FIDA), in which false data are injected in a coordinated manner into some adjacent pairs of captured nodes of an attacked wireless sensor network (WSN). As a result, the defense of WSN against a CFDIA is much more difficult than defense against ordinary FDIA. This paper is devoted to identifying the compromised sensors of a well-behaved WSN under a CFDIA. By establishing a model for predicting the reading of a sensor and employing the principal component analysis (PCA) technique, we establish a criterion for judging whether an adjacent pair of sensors are consistent in terms of their readings. Inspired by the system-level fault diagnosis, we introduce a set of watchdogs into a WSN as comparators between adjacent pairs of sensors of the WSN, and we propose an algorithm for diagnosing the WSN based on the collection of the consistency outcomes. Simulation results show that the proposed diagnosis scheme achieves a higher probability of correct diagnosis.
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Xiao, Yang. "Dynamic Fault Tolerant Topology Control for Wireless Sensor Network Based on Node Cascading Failure." International Journal of Online Engineering (iJOE) 14, no. 05 (2018): 118. http://dx.doi.org/10.3991/ijoe.v14i05.8644.
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To address the node cascading failure (CF) of the wireless sensor networks (WSNs), considering such factors as node load and maximum capacity in scale-free topology, this paper establishes the WSN dynamic fault tolerant topology model based on node cascading failure, analyses the relationships between node load, topology and dynamic fault tolerance, and demonstrates the proposed model through simulation test. It studies the effects of topology parameter and load in case of random node failure in the network node cascading failure, and utilizes the theoretical derivation method to derive the structural feature of scale-free topology and the capacity limit for the WSNs large-scale cascading failure, effectively enhancing the cascading fault tolerance of traditional WSNs. The simulation test results show that, with the degree distribution parameter <em>C</em> increasing, the minimum network node degree will increase accordingly, and in highly intensive topology, the dynamic fault tolerance will be better; with the parameter<em> λ </em>increasing, the maximum degree of the network node will gradually decrease, and the degree distribution of topology structure tends to be uniform, so that the large-scale cascading failure caused by node failure will have less influence on the WSN, and further improve the dynamic fault tolerance performance of the system.
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Vasco Arone Mazibuco, Nguyen Phuong Nhung, and Nguyen Tuan Linh. "Fault detection in wireless sensor networks with deep neural networks." Journal of Military Science and Technology, CSCE7 (December 30, 2023): 27–36. http://dx.doi.org/10.54939/1859-1043.j.mst.csce7.2023.27-36.
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This paper addresses the challenge of fault detection in Wireless Sensor Networks (WSNs), commonly used in fields like environmental monitoring and healthcare. WSNs, prone to various faults due to their deployment in unpredictable environments, require effective solutions for fault detection. Traditional machine learning approaches show limitations such as unsuitability for streaming data and the detection of a single fault type. We propose the use of deep neural networks, particularly Recurrent Neural Networks (RNNs), for fault detection in WSNs, focusing on temperature and humidity data. The paper emphasizes the importance of careful model selection, tuning, and thorough evaluation to enhance the accuracy and robustness of fault detection in real-world WSN applications.
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Qin, Bo, Luyang Zhang, Heng Yin, and Yan Qin. "Multiple Feature Vectors Based Fault Classification for WSN Integrated Bearing of Rolling Mill." Journal of Control Science and Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3041591.
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For rolling mill machines, the operation status of bearing has a close relationship with process safety and production effectiveness. Therefore, reliable fault diagnosis and classification are indispensable. Traditional methods always characterize fault feature using a single fault vector, which may fail to reveal whole fault influences caused by complex process disturbances. Besides, it may also lead to poor fault classification accuracy. To solve the above-mentioned problems, a fault extraction method is put forward to extract multiple feature vectors and then a classification model is developed. First, to collect sufficient data, a data acquisition system based on wireless sensor network is constructed to replace the traditional wired system which may bring dangers during production. Second, the measured signal is filtered by a morphological average filtering algorithm to remove process noise and then the empirical mode decomposition method is applied to extract the intrinsic mode function (IMF) which contains the fault information. On the basis of the IMFs, a time domain index (energy) and a frequency index (singular values) are proposed through Hilbert envelope analysis. From the above analysis, the energy index and the singular value matrix are used for fault classification modeling based on the enhanced extreme learning machine (ELM), which is optimized by the bat algorithm to adjust the input weights and threshold of hidden layer node. In comparison with the fault classification methods based on SVM and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability.
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Shakya, Subarna. "Pollination Inspired Clustering Model for Wireless Sensor Network Optimization." September 2021 3, no. 3 (2021): 196–207. http://dx.doi.org/10.36548/jsws.2021.3.006.
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Remote and dangerous fields that are expensive, complex, and unreachable to reach human insights are examined with ease using the Wireless Sensor Network (WSN) applications. Due to the use of non-renewable sources of energy, challenges with respect to the network lifetime, fault tolerance and energy consumption are faced by the self-managed networks. An efficient fault tolerance technique has been provided in this paper as an effective management strategy. Using the network and communication nodes, revitalization and fault recognition techniques are used for handling diverse levels of faults in this framework. At the network nodes, the fault tolerance capability is increased by the proposed protocol model and management strategy. This enhances the corresponding data transmission in the network. When compared to the conventional techniques, the proposed model increases the network lifetime by five times. It is observed from the validation results that, with a 10% increase in the network lifetime, there is a 2% decrease in the fault tolerance proficiency of the network. The network lifetime and data transmission rate are improved while the network energy consumption is reduced significantly. The MATLAB environment is used for simulation purpose. In terms of energy consumption, network lifetime and fault tolerance, the proposed model offers optimal results.
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Ramineni, Padmasree, and Sainath Chaithanya Aravalli. "Fault detection in single-hop and multi-hop wireless sensor networks using a deep learning algorithm." International Journal of Informatics and Communication Technology 13, no. 3 (2024): 453–61. https://doi.org/10.11591/ijict.v13i3.pp453-461.
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The wireless sensor network (WSN) has received significant recognition for its positive impact on environmental monitoring, yet its reliability remains prone to faults. Common factors contributing to faults include connectivity loss from malfunctioning node interfaces, disruptions caused by obstacles, and increased packet loss due to noise or congestion. This research employs a variety of machine learning and deep learning techniques to identify and address these faults, aiming to enhance the overall lifespan and scalability of the WSN. Classification models such as support vector machine (SVM), gradient boosting clasifer (GBC), K-nearest neighbours (KNN), random forest, and decision tree were employed in model training, with the decision tree emerging as the most accurate at 90.23%. Additionally, a deep learning approach, the recurrent neural network (RNN), effectively identified faults in sensor nodes, achieving an accuracy of 93.19%.
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Manickavasagam, B., B. Amutha, M. Revathi, N. Karthick, K. Sree Kumar, and K. Priyanka. "Wireless body area network mutual trust analysis technique for fault detection using software defined network (SDN)." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 575–89. http://dx.doi.org/10.3233/jifs-200363.
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Wireless Sensor Node (WSN) helps to track inpatient and remote patient (home/working) health information. Mishandling of the electronic system, patient behaviour and environmental changes which are all lead to incorrect data generation while using WSN for medical purposes. It leads to a false alarm being raised, network resource wastage, a false node priority level and low reliability. We have introduced the Mutual Trust Model (MTM) for Wireless Body Area Network (WBAN) with the help of Fog-Node (FN) to address these issues and to ensure the trustworthiness of the information acquired. In this, First-Hand Trust Method calculates the confidence value of the individual sensor node. Then, with neighbor node support, the Stigmercy Trust Method (STM) is implemented to reinforce the trust source node. Ultimately, the individual patient’s confidence value for the MTM model is determined. With the assistance of the wireless-mininet network emulator and the RYU controller, the network environment model implement, and the results have been obtained. MTM predicts the confidence level of the collected data significantly and produces an accuracy of 92.3 percentage to prevent the emergency band from being used dispensable.
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Wang, Na, Jiacun Wang, and Xuemin Chen. "A Trust-Based Formal Model for Fault Detection in Wireless Sensor Networks." Sensors 19, no. 8 (2019): 1916. http://dx.doi.org/10.3390/s19081916.
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Wireless Sensor Networks (WSNs) are prone to failures and malicious attacks. Trust evaluation is becoming a new method for fault detection in WSNs. In our previous work, a comprehensive trust model based on multi-factors was introduced for fault detection. This model was validated by simulating. However, it needs to be redeployed when adjustment to network parameters is made. To address the redeployment issue, we propose a Trust-based Formal Model (TFM) that can describe the fault detection process and check faults without simulating and running a WSN. This model derives from Petri nets with the characteristics of time, weight, and threshold. Basic structures of TFM are presented with which compound structures for general purposes can be built. The transition firing and marking updating rules are both defined for further system analysis. An efficient TFM analysis algorithm is developed for structured detection models. When trust factor values, firing time, weights, and thresholds are loaded, precise assessment of the node can be obtained. Finally, we implement TFM with the Generic Modeling Environment (GME). With an example, we illustrate that TFM can efficiently describe the fault detection process and specify faults in advance for WSNs.
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Shen, Ji Tao, and Jun Yang Zhang. "Research on Optimal Deployment of HWSN Node Cost Based on Simulated Annealing Algorithm." Applied Mechanics and Materials 651-653 (September 2014): 1921–24. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1921.
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An optimal heterogeneous sensor differentiated deployment schemes based on simulated annealing algorithm is proposed to solve the problems of the high density of distributing heterogeneity nodes in WSN and geographical irregularity of the sensed event. This method can not only apply to Boolean perception model of the node, but also apply to perception model. The algorithm uses the cost of sensors deployment as objective function in the context of assuring the coverage and fault tolerant of networks. The simulation results show that, the optimization method proposed in this paper can effectively convergence, under the premise to ensure network fault tolerance and robustness, reduces the cost of network deployment, improve the quality of target monitoring network.
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Lei, Jinhui, Xiyan Tian, and Zhixia Zhang. "Life Cycle and Intrusion Tolerance Optimization Topology Models for Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 14, no. 05 (2018): 105. http://dx.doi.org/10.3991/ijoe.v14i05.8643.
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<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">Wireless sensor networks have such disadvantages as upper limit of node energy and poor intrusion tolerance, etc. In light of these disadvantages, by analyzing such key parameters as residual energy, load, node degree, this paper proposes a wireless sensor network (WSN) life-cycle model, which fully considers node energy consumption and load fault tolerance, and a scale-free intrusion tolerance and targeted attacks optimization topology model. Then it verifies their feasibility through simulation test. The results show that the WSN life cycle model takes into account the impacts of residual energy and load capacity on the life cycle and fault tolerance of the system and improves the connectivity probability of high energy consumption nodes and small load nodes, leading to more uniform energy consumption of the wireless sensor network. Through the load adjustment coefficient, the life cycle of the network model is significantly increased. The simulation results show that the fault tolerance and survival time of the proposed model are both improved to some extent compared with those of other models. The proposed scale-free intrusion tolerance and targeted attacks optimization topology model optimizes the power exponent of the network using the structure entropy, and the established scale-free topology structure can make the model more tolerant to intrusion. The simulation results show that the intrusion tolerance of the algorithm proposed in this paper is 2.5 times that of the traditional network model, and the average life cycle is also significantly increased compared to those of other models.</span>
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Padmasree, Ramineni, and Aravalli Sainath Chaithanya. "Fault detection in single-hop and multi-hop wireless sensor networks using a deep learning algorithm." International Journal of Informatics and Communication Technology (IJ-ICT) 13, no. 3 (2024): 453. http://dx.doi.org/10.11591/ijict.v13i3.pp453-461.
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<p>The wireless sensor network (WSN) has received significant recognition for its positive impact on environmental monitoring, yet its reliability remains prone to faults. Common factors contributing to faults include connectivity loss from malfunctioning node interfaces, disruptions caused by obstacles, and increased packet loss due to noise or congestion. This research employs a variety of machine learning and deep learning techniques to identify and address these faults, aiming to enhance the overall lifespan and scalability of the WSN. Classification models such as support vector machine (SVM), gradient boosting clasifer (GBC), K-nearest neighbours (KNN), random forest, and decision tree were employed in model training, with the decision tree emerging as the most accurate at 90.23%. Additionally, a deep learning approach, the recurrent neural network (RNN), effectively identified faults in sensor nodes, achieving an accuracy of 93.19%.</p>
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Amutha, R., G. G. Sivasankari, and K. R. Venugopal. "A prediction model for effective data aggregation materials and fault node classification in WSN." Materials Today: Proceedings 49 (2022): 2962–67. http://dx.doi.org/10.1016/j.matpr.2021.11.370.
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Sun, Guo-Wen, Gang Xiang, Wei He, Kai Tang, Zi-Yi Wang, and Hai-Long Zhu. "A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor." Computers, Materials & Continua 75, no. 1 (2023): 1157–77. http://dx.doi.org/10.32604/cmc.2023.035667.
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Liu, Zhi Ping. "Automotive Wire Control Steering Sensor Fault Detection Algorithm Research." Applied Mechanics and Materials 727-728 (January 2015): 708–11. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.708.
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This article to cancel after the mechanical connections between steering wheel and steering, wire control steering system security and reliability problems, put forward on the basis of the analytical redundancy software sensor method of wire control steering system. In order to solve the compared with the traditional steering system in terms of reliability and safety of the problems of structural changes, the wire control steering system of the main sensor fault diagnosis methods are studied. In wire control steering system associated with the vehicle dynamics model is established under the premise of hypothesis testing to double adaptive fading Kalman filtering technology as a platform, combined with according to the working state of each sensors to determine fault feature vector, to build the main sensor wire control steering automobile fault diagnosis method of residual error threshold. For fault diagnosis of automobile EPS sensor, the BP neural network is put forward to EPS sensor for auto are introduced in the fault diagnosis. For large-scale wireless sensor networks (WSN), reduce the fault detection accuracy, and larger load of communication problems, according to the spatial and temporal correlation characteristics of sensor nodes, proposes a distributed sensor fault detection algorithm based on cluster. These algorithms for sensor fault detection is of great significance.
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Liu, Yong, Baohua Liang, and Jiabao Jiang. "Information Processing and Data Management Technology in Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 14, no. 09 (2018): 66. http://dx.doi.org/10.3991/ijoe.v14i09.8270.
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<p>The wireless sensor network is essentially a data-centric network that processes the continuous stream of data, which is collected by different sensors. Therefore, the existing data management technologies regard the wireless sensor network, which is named WSN as a distributed database, and it is composed of continuous data streams from the physical world. Wireless sensor networks are emerging next-generation sensor networks, but their transmission of information is highly dependent. The wireless sensor network processes the continuous stream of data collected by the sensor. Based on the features of wireless sensor networks, this paper presents a topology-dependent model of cluster evolution with fault tolerance. Through the limited data management, resources have reasonably configured, while also saving energy. The model is based on the energy-aware routing protocol in its network layer protocols. The key point is the energy routing principle. According to its own local view, the cluster head node builds the inter-cluster topology to achieve fault-tolerant and energy-saving goals. Simulation results show that the model has good fault tolerance and energy efficiency.</p>
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Deng, Huiqiong, Junyuan Wu, Jie Luo, et al. "Research on a Power Grid Cascading Failure Prevention and Control Method considering WSN." Wireless Communications and Mobile Computing 2021 (November 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/9439977.
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The practical application of wireless sensor networks (WSNs) in hot fields is summarized. It is found that compared with traditional monitoring methods, it has better adaptability to complex environments and low cost. It is suitable for monitoring power grid operation parameters. Therefore, this paper combines the above network and cascading failures, analyzes its 24-hour continuous and dynamic monitoring of the operation parameters of the power grid, and considers how to use the obtained parameters to analyze the disturbance of the remaining lines after the initial fault of the power grid. To prevent cascading failures in the power grid, a preventive control model considering safety and economy is proposed, and the model is solved by nondominated sorting genetic algorithm II and particle swarm optimization (NSGA2-PSO). Finally, the rationality of this method is verified in the IEEE39 node system.
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Alauthman, Almamoon, and Abeer Al-Hyari. "Intelligent Fault Detection and Self-Healing Mechanisms in Wireless Sensor Networks Using Machine Learning and Flying Fox Optimization." Computers 14, no. 6 (2025): 233. https://doi.org/10.3390/computers14060233.
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WSNs play a critical role in many applications that require network reliability, such as environmental monitoring, healthcare, and industrial automation. Thus, fault detection and self-healing are two effective mechanisms for addressing the challenges of node failure, communication disruption, a energy constraints faced by WSNs. This paper presents an intelligent framework based on Light Gradient Boosting Machine integration for fault detection and a Flying Fox Optimization Algorithm in dynamic self-healing. The LGBM model provides very accurate and scalable performance related to effective fault identification, whereas FFOA optimizes the recovery strategies to minimize downtown and maximize network resilience. Extensive performance evaluation of the developed system using a large dataset was presented and compared with the state-of-the-art heuristic-based traditional methods and machine learning models. The results showed that the proposed framework could achieve 94.6% fault detection accuracy, with a minimum of 120 milliseconds of recovery time and network resilience of 98.5%. These results hence attest to the efficiency of the proposed approach in ensuring robust and adaptive WSN operations toward the quest for enhanced reliability within dynamic and resource-constrained environments.
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Gutierrez-Rojas, D., C. Kalalas, I. Christou, et al. "Detection and Classification of Anomalies in WSN-Enabled Cyber-Physical Systems." IEEE SENSORS JOURNAL 25, no. 4 (2025): 7193–204. https://doi.org/10.1109/JSEN.2024.3520507.
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Detection and classification of anomalies in industrial applications has long been a focus of interest in the research community. The integration of computational and physical systems has increased the complexity of interactions between processes, leading to vulnerabilities in both the physical and cyber layers. This work presents a model structure for anomaly detection in the Internet of Things (IoT)-enabled industrial cyber-physical systems (CPSs), enabled by wireless sensor networks (WSNs). The model comprises three primary data blocks in the cyber layer: sensor-based data acquisition, data fusion to convert raw data into useful information, and analytics for decision-making. The rationale behind these blocks highlights the critical role of anomaly detection and is demonstrated through three use cases, namely fault selection in power grids, anomaly detection in an industrial chemical process, and prediction of the CO2 level in a room. Furthermore, we integrate explainable AI (XAI) algorithms into an IoT-based system to enhance error detection and correction, while fostering user engagement by offering useful insights into the decision-making process. Our numerical results demonstrate high accuracy in anomaly detection across these scenarios, significantly improving system reliability and enabling timely interventions, which could ultimately reduce operational risks. © 2024 IEEE.
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Barbudhe, Vishwajit K., and Shruti K. Dixit. "Wireless Sensor Network's Fault Diagnosis using Energy Efficient Delay Sensitive." International Journal on Advances in Engineering, Technology and Science (IJAETS) 5, no. 2 (2024): 151–55. https://doi.org/10.5281/zenodo.12579224.
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<em>Abstract – </em>With the increasing prominence of Wireless Sensor Networks (WSNs), addressing fault diagnosis has become a pivotal research concern. The emergence of faulty nodes, often stemming from energy depletion, poses significant challenges to the network's communication reliability and performance. This paper introduces the Energy Efficient Delay Sensitive (EEDS) algorithm as a solution to enhance both energy efficiency and delay management in the presence of faulty nodes. The proposed EEDS algorithm leverages Particle Swarm Optimization (PSO), a well-established optimization technique, to determine an optimised route between source and destination nodes. The algorithm considers the residual energy of nodes as a key factor in initiating communication, ensuring efficient utilisation of available resources. Additionally, the EEDS method employs the Ad Hoc On-Demand Multipath Distance Vector (AOMDV) routing protocol to establish a multipath route, enhancing network robustness. This paper comprehensively details the working of the PSO process, the network model, energy model, fault model, and presents a flowchart along with the algorithmic steps of the EEDS method. The proposed approach not only addresses the challenges associated with faulty nodes but also contributes to minimising energy consumption, thus extending the overall lifetime of the network. The effectiveness of the EEDS algorithm is validated through simulations, demonstrating its potential to significantly improve the fault-tolerant capabilities of WSNs in real-world scenarios. <em>Keywords: Energy Optimization, WSN, Energy Conservation, Energy Efficient Delay Sensitive.</em>
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Sun, Qiao-yan, Yu-mei Sun, Xue-jiao Liu, Ying-xin Xie, and Xiang-guang Chen. "Study on fault diagnosis algorithm in WSN nodes based on RPCA model and SVDD for multi-class classification." Cluster Computing 22, S3 (2018): 6043–57. http://dx.doi.org/10.1007/s10586-018-1793-z.
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Rai, Ashok Kumar, Lalit Kumar Tyagi, Anoop Kumar, Swapnita Srivastava, and Naushen Fatima. "Enhancing Energy Efficiency in Cluster Based WSN using Grey Wolf Optimization." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 12, no. 1 (2023): e30632. http://dx.doi.org/10.14201/adcaij.30632.
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Wireless sensor networks (WSNs) are typically made up of small, low-power sensor nodes (SNs) equipped with capability for wireless communication, processing, and sensing. These nodes collaborate with each other to form a self-organizing network. They can collect data from their surrounding environment, such as temperature, humidity, light intensity, or motion, and transmit it to a central base station (BS) or gateway for additional processing and analysis. LEACH and TSEP are examples of cluster-based protocols developed for WSNs. These protocols require careful design and optimization of CH selection algorithms, considering factors such as energy consumption, network scalability, data aggregation, load balancing, fault tolerance, and adaptability to dynamic network conditions. Various research efforts have been made to develop efficient CH selection algorithms in WSNs, considering these challenges and trade-offs. In this paper, the Grey Wolf Optimization (GWO) algorithm is employed to address the problem of selecting CHs (CHs) in WSNs. The proposed approach takes into account two parameters: Residual Energy (RE) and the distance of node (DS)s from the BS. By visualizing and analyzing the GWO algorithm under variable parameters in WSNs, this research identifies the most appropriate node from all normal nodes for CH selection. The experimental results demonstrate that the proposed model, utilizing GWO, outperforms other approaches in terms of performance.
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Altaf, Saud, Shafiq Ahmad, Mazen Zaindin, Shamsul Huda, Sofia Iqbal, and Muhammad Waseem Soomro. "Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network." Sustainability 14, no. 16 (2022): 10079. http://dx.doi.org/10.3390/su141610079.
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The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.
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Xie, Nan, Yao Chen, and Ping He. "Application of Wireless Sensor Network Technology Using Intelligent Algorithm in Mismatch Detection of Photovoltaic Power Generation." Wireless Communications and Mobile Computing 2022 (April 25, 2022): 1–13. http://dx.doi.org/10.1155/2022/7688109.
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The paper was aimed at ensuring the stable operation of the photovoltaic power generation system (PVPGS) and improving the accuracy of automatic mismatch detection. Consequently, this paper presents a PVPGS-oriented mismatch detection system based on wireless sensing technology (WSN). Firstly, the photovoltaic array (PVA) is constructed using a microcontroller, power management chip, nRF24L01, temperature sensor, voltage, and current sensor. Then, a fault detection and localization (FDL) scheme based on the Hampel algorithm is optimized, and Matlab/Simulink implements the PVA simulation model. Finally, several typical mismatch faults are simulated to verify the feasibility of the proposed FDL scheme using the measured voltage and current data. The empirical findings corroborate that the proposed FDL scheme can automatically and regularly collect photovoltaic (PV) electrical characteristic data and quickly and accurately identify and position a mismatch. In the case of a PVA open-circuit fault, the output current loss of the PVA is equal to the sum of the current of the open-circuit fault string in the array during normal operation. When the PVA is short-circuited, the PVA output voltage loss equals the sum of the output voltages of the faulty components in the most serious fault string under normal operation. Overall, the classification accuracy of the proposed FDL scheme is 97.556%. Lastly, the experiment reveals that the classification accuracy of the proposed FDL scheme is 100% for array aging, shadow, and the open circuit. Therefore, the research proposal has a good application prospect.
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Xie, Nan, Yao Chen, and Ping He. "Application of Wireless Sensor Network Technology Using Intelligent Algorithm in Mismatch Detection of Photovoltaic Power Generation." Wireless Communications and Mobile Computing 2022 (April 25, 2022): 1–13. http://dx.doi.org/10.1155/2022/7688109.
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The paper was aimed at ensuring the stable operation of the photovoltaic power generation system (PVPGS) and improving the accuracy of automatic mismatch detection. Consequently, this paper presents a PVPGS-oriented mismatch detection system based on wireless sensing technology (WSN). Firstly, the photovoltaic array (PVA) is constructed using a microcontroller, power management chip, nRF24L01, temperature sensor, voltage, and current sensor. Then, a fault detection and localization (FDL) scheme based on the Hampel algorithm is optimized, and Matlab/Simulink implements the PVA simulation model. Finally, several typical mismatch faults are simulated to verify the feasibility of the proposed FDL scheme using the measured voltage and current data. The empirical findings corroborate that the proposed FDL scheme can automatically and regularly collect photovoltaic (PV) electrical characteristic data and quickly and accurately identify and position a mismatch. In the case of a PVA open-circuit fault, the output current loss of the PVA is equal to the sum of the current of the open-circuit fault string in the array during normal operation. When the PVA is short-circuited, the PVA output voltage loss equals the sum of the output voltages of the faulty components in the most serious fault string under normal operation. Overall, the classification accuracy of the proposed FDL scheme is 97.556%. Lastly, the experiment reveals that the classification accuracy of the proposed FDL scheme is 100% for array aging, shadow, and the open circuit. Therefore, the research proposal has a good application prospect.
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C S, Sharmila Babu. "Malicious Node Detection and Secure Data Storage in Wireless Sensor Networks Using Blockchain and Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2475–83. https://doi.org/10.22214/ijraset.2025.70728.
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Wireless Sensor Networks (WSNs) are widely used for monitoring and data collection in various environments. However, these networks are vulnerable to attacks from malicious nodes, which can compromise the integrity and reliability of the system. In this work, I propose a model that combines blockchain-based registration and authentication with machine learning for real-time malicious node detection. The system uses the Histogram Gradient Boost (HGB) classifier to identify threats and stores legitimate data in the Interplanetary File System (IPFS), with hashes recorded on the blockchain. To keep the system efficient, I use the Verifiable Byzantine Fault Tolerance (VBFT) consensus instead of Proof of Work (PoW). My results, based on the WSN-DS dataset, show that this approach not only improves detection accuracy but also reduces transaction costs compared to traditional methods
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Khan, Gulista, Wajid Ali, Swati Arya, and Vaibhav Sharma. "GREEN ROUTINGSTRATEGY FOR DYNAMICALLY ARRANGED HOMOGENEOUS WSN- MSCT2." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 7 (2014): 3712–18. http://dx.doi.org/10.24297/ijct.v12i7.3077.
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Wireless networks play a crucial role in the communication systems nowadays. Wireless networks are being increasingly used in the communication among devices of the most varied types and sizes. User mobility, affordability, flexibility and ease of use are few of many reasons for making them very appealing to new applications and more users everyday. A Wireless Sensor Network (WSN) is composed of sensor nodes spread over the field to sense the data. The sensed data must be gathered & transmitted to Base Station (BS) for end user queries. The used sensor nodes being in- expensive having low computation power & limited energy so are not as much reliable as their expensive macro sensor counter parts but their size and cost enable hundred to thousand of micro sensors to achieve high quality fault tolerant system. In an environment where in each round all sensor nodes have to send data to base station; it is required to effectively utilize energy of sensor nodes so as to increase the life- time of the system. The use of data aggregation & fusion as proposed in LEACH increases system lifetime by a factor of 8 as compared to conventional routing protocols. In this work, our main focus is the static sensors are randomly selected and the base stations have their information all a priori. Basically, the sensors are in direct communication range of each other and can transmit to and receive from the base station. The nodes periodically sense the environment and have always data to send in each round of communication. The nodes fuse/ aggregate the data they receive from the others with their own data, and produce only one packet regardless of how many packets they receive. The problem is to find a routing scheme or an efficient protocol to deliver data packets collected from sensor nodes to the base station. It maximizes the lifetime of the sensor network under the system model given above. However, the definition of quality of service of the sensor network provides is not specified. Secondly, where the nodes are densely deployed, the quality of the system is affected as soon as a significant amount of nodes die, since adjacent nodes record identical or related data. In this case, the lifetime of the network is the time elapsed until half of the nodes or some specified portion of the nodes die. In general terms, the time in rounds where the last node depletes all of its energy defines the lifetime of the overall sensor network. Taking these different possible requirements under consideration, our work provides a proper timing of all deaths for all algorithms in detail as well as chooses the shortest possible path for communication with better memory management scheme and leaves the decision which one to choose to system designers.
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Ridha Mohammed Alfoudi. "Fault Detection in Wireless Sensor Networks Using Horse Herd Algorithm and Convolutional Neural Network with Attention Layer." Journal of Electrical Systems 20, no. 11s (2024): 3338–57. https://doi.org/10.52783/jes.8089.
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Reliable and real-time detection of faults in Wireless Sensor Networks (WSNs) is significant for the ongoing flow of critical data despite being a strenuous task. In this article, we present a comprehensive WSN-suitable fault detection system as a solution to this challenge. The primary step involves a thorough pre-processing of data such as partitioning, data cleaning, reordering of sample windows, and normalization through the min-max technique. These steps are fundamental to preparing the dataset, while also assisting systematize the proposed solution. A Horse Optimization Algorithm (HOA) integrated with a Convolutional Neural Network is at the heart of the method. As such, the CNN is able to capture highly advanced spatial and temporal features embedded in the processed data, as its convolutional layers are proficient in pattern extraction. Further, hyperparameter optimization of CNN learning rates, batch sizes, and the total number of convolution layers is performed using HOA, to improve the CNN performance. WSNs can greatly benefit from this, with the proposed model identifying faults with 99.47% accuracy on the test dataset, and 99.63% accuracy on the training dataset. These results show the proposed method’s effectiveness and accuracy in addressing fault detection issues in WSNs towards enabling better stability and performance within the network.
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Liu, Jianlin, Fenxiong Chen, Jun Yan, and Dianhong Wang. "CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks." Sensors 19, no. 16 (2019): 3445. http://dx.doi.org/10.3390/s19163445.
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Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 °C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has good fault detection performance and anti-noise ability. When reconstructing data, the model can effectively avoid fault and noise data.
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Aravind, Kalavagunta, and Praveen Kumar Reddy Maddikunta. "Dingo Optimization Based Cluster Based Routing in Internet of Things." Sensors 22, no. 20 (2022): 8064. http://dx.doi.org/10.3390/s22208064.
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The Wireless Sensor Network (WSN) is a collection of distinct, geographically distributed, Internet-connected sensors, which is capable of processing, analyzing, storing, and exchanging collected information. However, the Internet of Things (IoT) devices in the network are equipped with limited resources and minimal computing capability, resulting in energy conservation problems. Although clustering is an efficient method for energy saving in network nodes, the existing clustering algorithms are not effective due to the short lifespan of a network, an unbalanced load among the network nodes, and increased end-to-end delays. Hence, this paper proposes a novel cluster-based approach for IoT using a Self-Adaptive Dingo Optimizer with Brownian Motion (SDO-BM) technique to choose the optimal cluster head (CH) considering the various constraints such as energy, distance, delay, overhead, trust, Quality of Service (QoS), and security (high risk, low risk, and medium risk). If the chosen optimal CH is defective, then fault tolerance and energy hole mitigation techniques are used to stabilize the network. Eventually, analysis is done to ensure the progression of the SADO-BM model. The proposed model provides optimal results compared to existing models.
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Su, Jinglei, Xue Chu, Seifedine Kadry, and Rajkumar S. "Internet-of-Things-Assisted Smart System 4.0 Framework Using Simulated Routing Procedures." Sustainability 12, no. 15 (2020): 6119. http://dx.doi.org/10.3390/su12156119.
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The environment and energy are two important issues in the current century. The development of modern society is closely linked to energy and the environment. Internet of Things (IoT) and Wireless Sensor Networks (WSNs) have recently been developed substantially to contribute to the fourth transformation of the power grid, namely the smart grid. WSNs have the potential to improve power grid reliability via cable replacements, fault-tolerance features, large-scale protection, versatility to deploy, and cost savings in the smart grid environment. Moreover, because of equipment noise, dust heat, electromagnetic interference, multipath effects, and fading, current WSNs are making it very difficult to provide effective communication for the smart grid (SG) environment, in which WSN work is more difficult. For the smart system 4.0 framework, a highly reliable communication network based on the WSN is critically important for the successful operation of electricity grids in the next decade. To solve the above problem, a Robust Bio-Dynamic Stimulated Routing Procedure (RDSRP) has been proposed based on the real-time behavior of a new Hybrid Bird Optimizer (HBO) model. The presented innovative research and development is a small yet important aspect of continuous critical activities that address one of our society’s major challenges and that reverse the dangerous trends of environmental destruction. This study explores some of the most recent advances in this area, including energy efficiency and energy harvesting, which are expected to have a significant impact on green topics under smart systems in the Internet of things. The experimental results show that the proposed distributed system suggestively enhances network efficiency and reduces the transmission of excess packets for wireless sensor network-based smart grid applications.
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Minochkin, A., М. Masesov, and P. Shatsilo. "Applied mathematical providing of management of energy of resource of knots of sensory network for the (special) military setting charges." Communication, informatization and cybersecurity systems and technologies, no. 5 (June 1, 2024): 87–100. http://dx.doi.org/10.58254/viti.5.2024.08.87.
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The analysis of experience of realization of soldiery operations in war of the Russian federation against Ukraine witnessed, that the use in the armed forces of frontrank technologies the increase of troops of enemy allows in good time to disrobe on borders between the states, that take part in a military conflict, to reduce the strong points of opponent, redeployment of troops, their preparation to the rocket inflicting blows and other. Self introduction of such technologies in CASS of management troops and weapon allows in good time to react on influence of various external factors and gives an opportunity to the command and troops on a battlefield quickly to adapt to the changeable situation on all space spacious realizations of battle actions. To such frontrank technologies the soldiery scientists of armies of frontrank countries of the world take technologies of wireless sensory networks (WSN). Such descriptions of WSN as: 1 – is a capacity for rapid development; 2 – is independently organization; 3 – is high fault tolerance; 4 – is ability of application in different environment. The results of research of architecture of sensory network of the special (military) setting are examined in the article, her basic descriptions, principles of functioning and methods of management of knot of network (as a base component of the applied mathematical providing of management the charges of resource) an energy consumption from the point of view of criterion of the least energy consumption. For introduction of methods of management of charges in the system of operative management a sensory network worked out her functional model on the platform of methodology of IDEF (Integrated Definition is integral exactness).
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Gazis, Alexandros, and Eleftheria Katsiri. "IoT Cloud Computing Middleware for Crowd Monitoring and Evacuation." International Journal of Circuits, Systems and Signal Processing 15 (December 23, 2021): 1790–802. http://dx.doi.org/10.46300/9106.2021.15.193.
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Map-Reduce is a programming model and an associated implementation for processing and generating large data sets. This model has a single point of failure: the master, who coordinates the work in a cluster. On the contrary, wireless sensor networks (WSNs) are distributed systems that scale and feature large numbers of small, computationally limited, low-power, unreliable nodes. In this article, we provide a top-down approach explaining the architecture, implementation and rationale of a distributed fault-tolerant IoT middleware. Specifically, this middleware consists of multiple mini-computing devices (Raspberry Pi) connected in a WSN which implement the Map-Reduce algorithm. First, we explain the tools used to develop this system. Second, we focus on the Map-Reduce algorithm implemented to overcome common network connectivity issues, as well as to enhance operation availability and reliability. Lastly, we provide benchmarks for our middleware as a crowd tracking application for a preserved building in Greece (i.e., M. Hatzidakis’ residence). The results of this study show that IoT middleware with low-power and low-cost components are viable solutions for medium-sized cloud computing distributed and parallel computing centres. Potential uses of this middleware apply for monitoring buildings and indoor structures, in addition to crowd tracking to prevent the spread of COVID-19.
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Zhou, Xiaoyang. "Wireless Sensor Network Deployment in Cyberphysical Machine Tool System Based on Optimal Allocation of Memory Buffers." Journal of Sensors 2021 (February 2, 2021): 1–18. http://dx.doi.org/10.1155/2021/6680718.
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As an important direction of Industry 4.0, cyberphysical machine tool systems (CPMTS) can realize the deep integration and real-time interaction of physical components and information to optimize manufacturing processes. Wireless sensor network (WSN), an important part of CPMTS, is responsible for data collection and transmission. However, in the process of data transmission, due to memory limitations and noise interference, unreasonable sensor distribution will affect the performance of CPMTS. At the same time, data accuracy will be affected due to the resource constraints of CPMTS. To solve the problems above, this paper firstly presented a single-station transfer model to ensure the layout of sensors in each sink, which can meet the detection capability of fault/monitoring data. Then, by using fuzzy graphs, a multihop-station transfer model and data-collecting model are developed to describe the data flow and memory allocation in the wireless network. Taking noise interference and data position into consideration, a MILP problem is formulated and the optimization solution is obtained by using the “branch and bound” method. Finally, case studies about optimal sensor distribution on the single station and path optimization on the multihop station are presented to illustrate the proposed strategy. The case studies validated that the proposed sensor distribution in a single station can achieve higher detectability with fewer resources, and the optimization path strategy can achieve the best performance in two proposed experiments, compared to the shortest path and noninferior path strategies.
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Gaddam, Anuroop, Tim Wilkin, Maia Angelova, and Jyotheesh Gaddam. "Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions." Electronics 9, no. 3 (2020): 511. http://dx.doi.org/10.3390/electronics9030511.
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The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is growing to become the global digital nervous systems. It is quite evident that in the near future, hundreds of millions of individuals and businesses with billions will have smart-sensors and advanced communication technology, and these things will expand the boundaries of current systems. This will result in a potential change in the way we work, learn, innovate, live and entertain. The heterogeneous smart sensors within the Internet of Things are indispensable parts, which capture the raw data from the physical world by being the first port of contact. Often the sensors within the IoT are deployed or installed in harsh environments. This inevitably means that the sensors are prone to failure, malfunction, rapid attrition, malicious attacks, theft and tampering. All of these conditions cause the sensors within the IoT to produce unusual and erroneous readings, often known as outliers. Much of the current research has been done in developing the sensor outlier and fault detection models exclusively for the Wireless Sensor Networks (WSN), and adequate research has not been done so far in the context of the IoT. Wireless sensor network’s operational framework differ greatly when compared to IoT’s operational framework, using some of the existing models developed for WSN cannot be used on IoT’s for detecting outliers and faults. Sensor faults and outlier detection is very crucial in the IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. The data collected by sensors are initially pre-processed to be transformed into information and when Artificially Intelligent (AI), Machine Learning (ML) models are further used by the IoT, the information is further processed into applications and processes. Any faulty, erroneous, corrupted sensor readings corrupt the trained models, which thereby produces abnormal processes or outliers that are significantly distinct from the normal behavioural processes of a system. In this paper, we present a comprehensive review of the detecting sensor faults, anomalies, outliers in the Internet of Things and the challenges. A comprehensive guideline to select an adequate outlier detection model for the sensors in the IoT context for various applications is discussed.
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Shi, Ke-Xin, Shi-Ming Li, Guo-Wen Sun, Zhi-Chao Feng, and Wei He. "A fault diagnosis method for wireless sensor network nodes based on a belief rule base with adaptive attribute weights." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-54589-6.
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AbstractDue to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved.
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Li, Ming. "Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN." EURASIP Journal on Information Security 2023, no. 1 (2023). http://dx.doi.org/10.1186/s13635-023-00149-w.
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AbstractWireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks.
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Mohapatra, Hitesh, and Amiya Kumar Rath. "Fault Tolerance in WSN Through Uniform Load Distribution Function." International Journal of Sensors, Wireless Communications and Control 10 (May 25, 2020). http://dx.doi.org/10.2174/2210327910999200525164954.
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Aim: The existing cluster-based energy-efficient models such as Low Energy Adaptive Clustering Hierarchy (LEACH) and Stable Election Protocol (SEP) are fails in the distribution of sensor nodes uniformly during cluster formation. The non-uniform cluster distribution structure leads to rapid energy depletion and high energy consumption. So, this paper aims to create uniform loadbased cluster formation. Background: This proposed idea motivated with a famous saying "If a Fault is Handling Fault Then That's not a Fault". Designing of energy-efficient and fault-tolerant model is indeed in wireless sensor network deployments. The involvement of WSN's is not only limited to domestic purpose but also applied in a harsh and hostile environment. Objective: In this paper, we focus on energy depletion-based fault occurrence and its tolerance. Here we propose a Uniform Load Distribution Function (ULDF) with two objectives. The first one is to form equilibrium energy level clusters, and the second one is to avoid the frequent involvement of SNs in cluster formation. The proposed function is compared against the performance of both homogeneous LEACH and heterogeneous SEP protocols. Methods: Efficient clustering through equal distribution of SNs based on their current residual energy. Results: Our analysis concludes with results where the proposed ULDF performing better than LEACH and SEP and reduces SNs involvement in cluster formation, which indirectly implies minimum energy consumption. Conclusion: Energy saving through uniform load distribution.
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Begum, Beneyaz Ara, and Satyanarayana V. Nandury. "Composite Interference Mapping Model to Determine Interference-Fault Free Schedule in WSN." IEEE Access, 2022, 1. http://dx.doi.org/10.1109/access.2022.3211654.
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"A Proposed Method to Enhance the Quality of Data Communication in WSN using Modular Arithmetic." International Journal of Innovative Technology and Exploring Engineering 9, no. 8 (2020): 879–81. http://dx.doi.org/10.35940/ijitee.g5797.069820.
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Wireless sensor networks (WSN) are the current direction to monitor the resources and processes by developing fault tolerant distributed auto configure systems. High reliability is required to use WSN in safety systems, real time monitoring systems, guard systems and industrial control for all levels of the OSI model. To eliminate the noise and to process the information parallel by extending the signal spectrum using FHSS and Residue number system (RNS) based transformation. These approaches increase the reliability of data transmission in a WSN physical layer only. It is essential to have reliable data transmission in the network layer. When network topology is modified, packet loss is caused by overload and emergency or inaccessibility of units. Delay time increases because of packet retransmission. These considerations have led us to propose to work on “Performance studies on RNS based spread spectrum techniques for few communication channels”
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Machiwa, Erick J., Verdiana G. Masanja, Michael F. Kisangiri, and Joseph W. Matiko. "A comprehensive survey on linear programming and energy optimization methods for maximizing lifetime of wireless sensor network." Discover Computing 27, no. 1 (2024). http://dx.doi.org/10.1007/s10791-024-09454-5.
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AbstractThe wireless sensor network (WSN) is considered as a network, encompassing small-embedded devices named sensors that are wirelessly connected to one another for data forwarding within the network. These sensor nodes (SNs) follow an ad-hoc configuration and are connected with the Base Station (BS) through the internet for data sharing. When more amounts of data are shared from several SNs, traffic arises within the network, and controlling and balancing the traffic loads (TLs) are significant. The TLs are the amount of data shared by the network in a given time. Balancing these loads will extend the network’s lifetime and reduce the energy consumption (EC) rate of SNs. Thus, the Load Balancing (LB) within the network is very efficient for the network’s energy optimization (EO). However, this EO is the major challenging part of WSN. Several existing research concentrated and worked on energy-efficient LB optimization to prolong the lifetime of the WSN. Therefore, this review collectively presents a detailed survey of the linear programming (LP)-based optimization models and alternative optimization models for energy-efficient LB in WSN. LP is a technique used to maximize or minimize the linear function, which is subjected to linear constraints. The LP methods are utilized for modeling the features, deploying, and locating the sensors in WSN. The analysis proved the efficacy of the developed model based on its fault tolerance rate, latency, topological changes, and EC rates. Thus, this survey briefly explained the pros and cons of the developed load-balancing schemes for EO in WSN.
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C, Umarani, Gokul Prasad C, Velumani R, and Thangaraj K. "Energy-Efficient Fault Data Prediction and Transmission in WSN IoT using Bio-Inspired Optimization and Deep Learning." Journal of Machine and Computing, April 5, 2025, 1186–203. https://doi.org/10.53759/7669/jmc202505094.
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Wireless sensor networks (WSNs) are crucial for several applications. WSN nodes frequently function with constrained battery capacity, rendering energy efficiency a critical issue for clustering and routing. Moreover, a principal challenge of WSNs is ensuring the dependability and security of transmitted data in susceptible contexts to avert hostile node attacks. This study seeks to establish a secure and energy-efficient routing system for fault data prediction to improve the longevity and dependability of WSNs. This paper presents a sophisticated framework for intelligent fault prediction and energy-efficient data transmission in WSN, utilising bio-inspired optimisation and deep learning methodologies. The model initiates data fault prediction with Multi-Term Fourier Graph Neural Networks (MTFGNN), which examine temporal and spatial relationships to detect anomalies and defective nodes prior to clustering. Faultless nodes are subsequently categorised by Fuzzy C-Means (FCM) clustering, facilitating adaptive and efficient cluster creation. Quokka Swarm Optimisation (QSO) is utilised to improve energy efficiency by selecting ideal cluster heads (CH), thereby balancing energy usage and reducing intra-cluster communication expenses. A trust-based routing technique employs Proximal Policy Optimisation (PPO), a reinforcement learning method that dynamically identifies secure and energy-efficient pathways for data transfer, while reducing the influence of unreliable nodes. The experimental results indicate that it surpasses the rival methods across multiple performance parameters. The performance outcomes of quality of service (QoS) metrics are delineated as follows: energy consumption (0.204), throughput (0.701), packet delivery rate (94.24%), network lifetime (1310 rounds), and fault prediction accuracy (99.78%), precision (98.69%), recall (97.52%) and F1 score (97.83).
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Swathi, B., Dr M. Amanullah, and S. A. Kalaiselvan. "Energy-efficient and Fault-Tolerant Routing Mechanism for WSN using Optimizer based Deep Learning Model." Sustainable Computing: Informatics and Systems, October 2024, 101044. http://dx.doi.org/10.1016/j.suscom.2024.101044.
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Ara, Tabassum. "Energy efficient secured cluster based distributed fault diagnosis protocol for IoT." International Journal of Communication Networks and Information Security (IJCNIS) 10, no. 3 (2022). http://dx.doi.org/10.17762/ijcnis.v10i3.3586.
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The rapid growth of internet and internet services provision offers wide scope to the industries to couple the various network models to design a flexible and simplified communication infrastructure. A significant attention paid towards Internet of things (IoT), from both academics and industries. Connecting and organizing of communication over wireless IoT network models are vulnerable to various security threats, due to the lack of inappropriate security deployment models. In addition to this, these models have not only security issues; they also have many performance issues. This research work deals with an IoT security over WSN model to overcome the security and performance issues by designing a Energy efficient secured cluster based distributed fault diagnosis protocol (EESCFD) Model which combines the self-fault diagnosis routing model using cluster based approach and block cipher to organize a secured data communication and to identify security fault and communication faults to improve communication efficiency. In addition we achieve an energy efficiency by employing concise block cipher which identifies the ideal size of block, size of key, number of rounds to perform the key operations in the cipher.
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Balraj, Lavina, and Aruchamy Prasanth. "An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network." Transactions on Emerging Telecommunications Technologies 35, no. 4 (2024). http://dx.doi.org/10.1002/ett.4971.
Full textAbstract:
AbstractOf late, the Internet of Things (IoT) has progressed in its pervasiveness across the globe for diverse applications. Wireless Sensor Network (WSN) is one of the prominent technologies employed in IoT environments where multiple tiny sensor nodes are distributed to sense real‐time observations about unforeseeable areas for control and managerial purposes. Owing to the presence of sensors in inaccessible regions and their battery restrictions, different types of software faults occur in IoT‐enabled WSNs (IWSNs). These faults create uncertainty in data reading which causes serious damage to the sensor network. Hence, the IWSN necessitates an effective fault‐detection methodology to continue optimal activity despite the existence of software faults. This work proposes a novel Energy‐Aware Hierarchical Rule‐based Software Fault Detection (HRSFD) model to identify various software faults with minimum energy depletion in the IWSN environment. Primarily, the proposed model extracts antecedent attributes from the characteristics of the sensed data. Its abnormal values can be identified based on the obtained antecedent attributes. Subsequently, the category of the software fault is determined by applying a hierarchical rule strategy. Finally, from the simulation results, it is apparent that the fault detection accuracy rate of the proposed HRSFD model attains 99.12% for dense networks. The lifetime of the network is also prolonged by 18% as compared to the existing state‐of‐the‐art models.
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"Fault Tolerant Coverage and Connectivity Model for Wireless Sensor Networks in Real time Environment." International Journal of Recent Technology and Engineering 8, no. 4 (2019): 5083–8091. http://dx.doi.org/10.35940/ijrte.d8302.118419.
Full textAbstract:
Both connectivity and coverage are considered as the basic performance standards of the service yielded through a Wireless Sensor Network (WSN). Sensing field’s monitoring quality is represented through coverage. So, coverage represents the quality of tracking of the sensing field through the sensors. Connectivity exhibits the quality of the information delivery along with the sensor nodes, or to the base station. This paper aims for pre-estimation for the sensor numbers that are to be placed in an adverse situation for achieving required coverage. This paper promotes 𝐾-coverage and 𝐾-connectivity models that focuses on multipath effects as well as shadowing fading’s combined effect. The value of 𝐾 differs for different types of applications. For measuring the coverage and connectivity probabilities, in shadowing as well as multipath fading presence, a mathematical model is obtained. Moreover, the coverage and connectivity probability derivations which are derived with the help of lognormal shadowing fading as well as Rayleigh fading are approved through the deployments of nodes utilizing Poisson distribution. The simulation section of this paper clearly shows that coverage and connectivity are dependent on the density of node, fading parameters like the standard deviation, and path loss exponent. The sensing model proposed by us is proved to be more appropriate for realistic environment as sensor’s ideal quantity necessary in order to attain desirable coverage in fading conditions.