Mobility-Aware Hierarchical Clustering in Mobile Wireless Sensor Networks

Zafar, Sherin; Bashir, Abeer; Chaudhry, Shafique Ahmad

doi:10.1109/access.2019.2896938

Cited by 54 publications

(21 citation statements)

References 15 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extracte the target from the captured image, and transform into 2D array by RGB color histogram. HAC [47] firstly calculate the distance between the sample points, merge the closest points into the same class each time; then calculate the distance between classes and combine the closest classes into one category. Keep merging until synthesized a class.…”

Section: Image Feature Extractionmentioning

confidence: 99%

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Zhu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper focuses on how to perspective the melting behavior of solid iron tailings in molten blast furnace slag and take a new non-contact visual analytical method to predict its melting law. The optimized convolution neural network (CNN) is used to track the moving target in charge coupled device (CCD) camera system efficiently and accurately, and the melting behavior of SiO 2 is described by coordinate translation transformation theory. Hierarchical agglomerative clustering (HAC) and delaunay triangulation were used to extract the characteristic parameters of the melting process of SiO 2. The prediction model of the melting rate of SiO 2 at high temperature was established by least square fitting (LSF) and dimensional analysis, and compared with the actual melting rate of SiO 2 obtained by experiments. The results show that the melting characteristics of SiO 2 at high temperature are in accord with certain function rule. The performance of optimized CNN in terms of processing time and accuracy is significantly improved, and the fusion rate prediction model of SiO 2 is verified by 100% accuracy. It provides theoretical support and model basis for the improvement of slag cotton preparation technology. INDEX TERMS Melting rate, target tracking, feature extraction, dimensional analysis, best match.

show abstract

Section: Image Feature Extractionmentioning

confidence: 99%

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Zhu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Organizing sensors into a hierarchical topology with clusters has long been suggested for wireless sensor networks to reduce the energy consumption in sensors and the communication burden and latency in the communication network [11], [18], [21]- [30], [33]; and many authors proposed procedures to build clusters [29]- [32]. In fact, many authors proposed procedures in which sensors self-organize into clusters [33], [35]- [39], [54].…”

Section: B Previous Studies On Clustering Algorithms For Wireless Sementioning

confidence: 99%

“…In fact, many authors proposed procedures in which sensors self-organize into clusters [33], [35]- [39], [54]. In these procedures, a sensor decides to become a clusterhead either randomly [35] or based on other network aspects [32], such as residual energy [36] or maintaining connectivity [33], [37]. The clusterhead sensor then broadcasts a message announcing the newly created cluster.…”

Section: B Previous Studies On Clustering Algorithms For Wireless Sementioning

confidence: 99%

“…Many of the above clustering algorithms explicitly aim to partition sensors into clusters [22], [27], [28], [30]- [32], [35]- [37], [54]; i.e., they produce clusters where a sensor belongs to a single cluster. While communicating with a single clusterhead saves energy in sensors, it can harm detection performance.…”

Section: B Previous Studies On Clustering Algorithms For Wireless Sementioning

confidence: 99%

See 1 more Smart Citation

A Clustering Algorithm to Improve the Scan Statistic in Sensor Detection Systems

Fonseca

2020

IEEE Access

View full text Add to dashboard Cite

The success of the scan statistic in detecting anomalies in georeferenced data has motivated its use in distributed sensor systems to detect an emitter at an unknown location. Sensors are grouped into clusters, cluster statistics are produced, and the scan statistic decides that the emitter is present if any cluster statistic is above a threshold. Although the scan statistic is not the optimal fusion rule, it avoids combining strong measurements from sensors near the emitter with weak measurements from sensors far from the emitter. The question that motivates this paper is: could a clustering algorithm improve the detection performance of the scan statistic? Previous studies on the scan statistics considered that the set of clusters is given or is the product of a scanning process; and previous studies on clustering algorithms for wireless sensor networks have not considered forming clusters specifically for the scan statistic. Our first goal is to highlight the opportunity of improving the scan statistic by carefully designing the cluster set. We discuss how the cluster set influences not only the detection performance, but also processing and communication in the system. Our second goal is to propose and study a new clustering algorithm to build the cluster set for the scan statistic. Although suboptimal, our algorithm produces cluster sets that reach similar or better detection performance than the usually considered cluster sets with a significantly lower number of clusters, which results in less processing and communication in the system.

show abstract

“…It would be interesting to study how the proposed route cost estimation procedure could operate together with clustering procedures [54][55][56][57][58]. Clustering procedures build a hierarchical topology in which sensors communicate with clusterheads, which forward the message to other clusterheads that relay the message until it reaches the destination.…”

mentioning

confidence: 99%

Improving Route Selections in ZigBee Wireless Sensor Networks

Meka

Fonseca

2019

Sensors

View full text Add to dashboard Cite

The ZigBee wireless communication specifications forecast the use of multihop routes between nodes and define that nodes select their routes based on their costs. The specifications define how to compute a route cost from the probability of successfully transmitting on each of the routes’ links; and it is recommended that such probabilities be obtained by counting received link status messages or averaging link quality indicators from received packets. In this paper, we study the performance of these two recommended procedures, show that they can lead to degraded route selections, and propose a procedure that can improve route selections without modifications to the ZigBee protocol or frame formats. Our procedure estimates the probability of successful transmission on each link, based on information from the medium access layer during unicast packet transmissions, and includes a modification into how ZigBee nodes treat routing messages internally in order to reduce variations in the link cost estimates. Focusing on a home environment with one or two hops, our simulation results show that, in several scenarios, our procedure performs better than either of the two procedures recommended in the ZigBee specifications.

show abstract

Mobility-Aware Hierarchical Clustering in Mobile Wireless Sensor Networks

Cited by 54 publications

References 15 publications

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

A Clustering Algorithm to Improve the Scan Statistic in Sensor Detection Systems

Improving Route Selections in ZigBee Wireless Sensor Networks

Contact Info

Product

Resources

About