Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

Li, Gang; He, Bin; Huang, Hongwei; Tang, Liang

doi:10.3390/s16101601

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered WSN was proposed by Li et al [9]. Temporal Data-Driven Sleep Scheduling (TDSS) diminishes the sensor data redundancy for the same node in time sequences, whereas Spatial Data-Driven Anomaly Detection (SDAD) detects outlier or anomalous data and preserves the accuracy of the sensor data for the specific node.…”

Section: Related Workmentioning

confidence: 99%

Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

Alkhatib¹,

Abed-Al²

2022

Intelligent Automation &Amp; Soft Computing

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Wireless sensor networks have been a very important means in forest monitoring applications. A clustered sensor network comprises a set of cluster members and one cluster head. The cluster members are normally located close to each other, with overlaps among their sensing coverage within the cluster. The cluster members concurrently detect the same event to send to the Cluster Head node. This is where data aggregation is deployed to remove redundant data at the cost of data accuracy, where some data generated by the sensing process might be an outlier. Thus, it is important to conserve the aggregated data's accuracy by performing an outlier data detection process before data aggregation is implemented. This paper concerns evaluating multivariate outlier detection (MOD) analysis on aggregated accuracy of data generated by a forest fire environment using OMNeT++ and performing the analysis in MATLAB R2018b. The findings of the study showed that the MOD algorithm conserved approximately 59.5% of aggregated data accuracy, compared with an equivalent algorithm, such as the FTDA algorithm, which conserved 54.25% of aggregated data accuracy for the same event.

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Section: Related Workmentioning

confidence: 99%

Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

Alkhatib¹,

Abed-Al²

2022

Intelligent Automation &Amp; Soft Computing

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“…Demura [8] proposed a protocol to send data over less crowded paths. Li [9] proposed time-data-driven sleep scheduling and spatial-data-driven anomaly detection approaches to reduce data redundancy. Rajeswari [10] proposed an improved traffic generation mechanism based on TCP/IP protocol to alleviate network congestion.…”

Section: Related Workmentioning

confidence: 99%

“…Here we use the area center method to find the membership degree of the parameter increments ∆k p , ∆k i and ∆k d which are mathematically expressed as Equations (9)- (11). µ ∆k p = min µ NB (e) , µ NB (ec) (9) µ(∆k i ) = min µ NB (e) , µ NB (ec)…”

Section: Deblurringmentioning

confidence: 99%

A Novel Fuzzy PID Congestion Control Model Based on Cuckoo Search in WSNs

Lin

You

Chen

2020

Sensors

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Wireless Sensor Networks (WSNs) consist of multiple sensor nodes, each of which has the ability to collect, receive and send data. However, irregular data sources can lead to severe network congestion. To solve this problem, the Proportional Integral Derivative (PID) controller is introduced into the congestion control mechanism to control the queue length of messages in nodes. By running the PID algorithm on cluster head nodes, the effective collection of sensor data is realized. In addition, a fuzzy control algorithm is proposed to solve the problems of slow parameter optimization, limited adaptive ability and poor optimization precision of traditional PID controller. However, the parameter selection of the fuzzy control algorithm relies too much on expert experience and has certain limitations. Therefore, this manuscript proposes the Cuckoo Fuzzy-PID Controller (CFPID), whose core idea is to apply the cuckoo search algorithm to optimize the fuzzy PID controller’s quantization factor and PID parameter increment. Simulation results show that in comparison with the existing methods, the instantaneous queue length and real-time packet loss rate of CFPID are better.

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“…The latter includes cross-sectional deformation, uneven settlement and stagger between adjacent structural sections [18,19,20]. Different kinds of devices and methods for identifying specific structural defects have been developed in recent years [21,22,23,24]. For example, surface defects in a metro tunnel can be identified by optical imaging, infrared imaging and laser imaging-based devices or methodologies [25,26,27,28,29,30,31,32,33,34,35,36,37,38].…”

Section: Introductionmentioning

confidence: 99%

Rapid Acquisition and Identification of Structural Defects of Metro Tunnel

Yuan

2019

Sensors

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Metro systems in urban cities demand rapid inspection methods, in order to identify critical structural defects in a timely manner. However, traditional inspection methods are only specific to one kind of structural defect, which reduces the overall efficiency of inspection. This study proposes an integrated solution for rapidly acquiring and identifying two kinds of structural defects (surface defects and cross-sectional deformation) in a metro tunnel, using a cart equipped with non-metric cameras. The integrity and rapidity are considered in formulating a systematic design for the development of the acquisition device. Methodologies based on image processing and photogrammetry are proposed to identify the structural defects of the metro tunnel. A series of on-site tests validate that the proposed method has enough speed and has acceptable accuracy in detecting critical structural defects of metro tunnels. The cost and efficiency analysis shows that the proposed method is competitive, which will greatly improve the efficiency and reduce the costs of the inspection of metro tunnels.

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Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

Cited by 6 publications

References 17 publications

Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

A Novel Fuzzy PID Congestion Control Model Based on Cuckoo Search in WSNs

Rapid Acquisition and Identification of Structural Defects of Metro Tunnel

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