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Wireless sensor network (WSN) is a developed wireless network consisting of some connected sensor nodes. The WSN is employed in many fields such as military, industrial, and environmental monitoring applications. These nodes are equipped with sensors for sensing the environmental variables such as temperature, humidity, wind speed, and so on. In mo...
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... These performance metrics are recall, precision, F1-score and accuracy. Such metrics are based on the following conditions [30,31]: The performance metrics can be computed using the following formulas [32]: ...
... A hybrid methodology based on mutual information change and wavelet transform technique is presented to detect malfunctioning sensors in WSN. 35 The wavelet transform is proposed for self-management sensor fault detection in this work, and it is utilized to break nonstationary signals down into approximation and detail coefficients. The wavelet transform is used to determine the exact fault and to track each sensor. ...
Wireless sensor networks (WSNs) are ad hoc networks that consist of tiny sensor nodes deployed in the sensed environment to monitor the natural phenomenon and send the monitored data to the base station for further processing. Due to the unfriendly deployment of the sensor nodes and their resource‐constrained nature, the sensor nodes are subjected to frequent node failure. Detecting sensor node failures and recovering them is a major challenge in WSN. Due to the failure of the nodes in WSN, there exist various problems such as a change in the network topology, communication link failure, a disjoint partition of the network, and failure to transmit the data to the sink. Various authors have proposed various techniques to detect and recover from node failure in the WSN. In this paper, a comprehensive analysis of the various node failure and node recovery techniques has been carried out to highlight the advantages and limitations along with the open research challenges for addressing the failure of the nodes in an effective manner. Detecting sensor node failures and recovering them is a major challenge in WSN. Due to the failure of the nodes in WSN, there exist various problems such as a change in the network topology, communication link failure, a disjoint partition of the network, and failure to transmit the data to the sink.
... M-Z interferometric FOS has high sensitivity and is independent of the action point and Frequency (F) of PM [24]. M-Z interferometric FOS is prone to relative drift and amplitude fading because the arm lengths of the reference arm and signal arm cannot be consistent [25]. ...
To improve the signal recognition effect of the security system, this paper studies the security system based on intelligent Fiber Optic Sensor (FOS) technology. Firstly, the research background of intelligent FOS is introduced, and its current situation in feature extraction, recognition, and detection is introduced. Secondly, the double Mach–Zehnder (M-Z) Optical Fiber- (OF-) based interferometer model is implemented, and the Wavelet Analysis (WA) theory is introduced to analyze the characteristic threshold and Frequency (F) characteristics of intrusion signal. Finally, the distributed intelligent FOS-based perimeter security system is constructed, and an empirical study is conducted to verify its performance. The results show that the intruder knocking-induced signal F, intruder climbing-induced signal F, noiseless environment-induced signal F, and rainy environment-induced signal F are 0–250 kHz, 0–25 kHz, 0–1.5 kHz, and 0–3.5 kHz, respectively; in all the four cases, excellent results have been obtained after wavelet threshold denoising. Meanwhile, the received signal is decomposed into seven layers through multiscale WA theory. The signal feature classification is based on WA and takes variance as the representation, and the clear classification results are obtained; when the False Alarm Rate (FAR) = 1%, the detection probability of the proposed system is about 99%, while the detection probability of the traditional system is about 3%. The reference arm and sensing arm of the distributed OF-based perimeter security system can be laid in the same optical cable. Therefore, the designed wavelet threshold filtering method is feasible, and the detection probability of the designed WA-based system is higher than that of the traditional security system. The research content provides a reference for the development of intelligent FOS technology in the field of security.
... It is fundamental to detect faulty nodes before performing the necessary recovery procedures to guarantee a high quality level of service. WSN fault detection [54,55] is a technique that identifies an error when it occurs and identifies the fault's type and location. ...
Wireless sensor networks (WSNs) have garnered much attention in the last decades. Nowadays, the network contains sensors that have been expanded into a more extensive network than the internet. Cost is one of the issues of WSNs, and this cost may be in the form of bandwidth, computational cost, deployment cost, or sensors' battery (sensor life). This paper proposes a dual-level sensor selection (DLSS) model used to reduce the number of sensors forming WSNs. The sensor reduction process is performed at two consecutive levels. First, a combination of the Fisher score method and ANOVA test at the filter level weighs all the network sensors and produces only a reduced set of sensors. Additionally, the grey wolf optimizer algorithm produces the optimum sensor subset, while an adaptive sensor recovery solution is proposed to extend the network lifetime even longer using sensors failure management. The proposed model performance is evaluated using four different datasets. In comparison with to other similar methods, the results indicated that the proposed model achieved a more efficient subset of sensors preserving a high accuracy rate.
... These techniques addressed several combinatorial optimization bottlenecks, such as local optimality and costly computational value. A hybrid methodology was proposed [10] based on wavelet transform (WT) and mutual information change (MIC) for faulty sensor identification. The results showed that approaches WT and MIC obtained a more considerable accuracy in most fault varieties. ...
