Data similarity aware dynamic node clustering in wireless sensor networks

Gielow, Fernando; Jakllari, Gentian; Nogueira, Michele; Santos, Aldri

doi:10.1016/j.adhoc.2014.07.008

Cited by 29 publications

(31 citation statements)

References 26 publications

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“…Some researchers have observed that synchronicity is a useful abstraction in many contexts and applications [14][15][16]. Also, few existing synchronization protocols have taken advantage of bio-inspired models [8,12].…”

Section: Related Workmentioning

confidence: 99%

Firefly-inspired and robust time synchronization for cognitive radio ad hoc networks

Lipa

Mannes

Santos

et al. 2015

Computer Communications

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Section: Related Workmentioning

confidence: 99%

Firefly-inspired and robust time synchronization for cognitive radio ad hoc networks

Lipa

Mannes

Santos

et al. 2015

Computer Communications

Self Cite

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“…Some works, such as [15][16][17][18], study the spatial-temporal correlation between sensor readings as the aggregation process of similar data. In [15], the authors propose a spatial-temporal correlation based fault-tolerant event detection scheme, called STFTED, which leverages a two-stage decision fusion and exploiting spatial-temporal correlation of sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

“…In [15], the authors propose a spatial-temporal correlation based fault-tolerant event detection scheme, called STFTED, which leverages a two-stage decision fusion and exploiting spatial-temporal correlation of sensor nodes. In [17], the authors propose an architecture for dynamic and distributed data-aware clustering, and the Dynamic Data-aware Firefly-based Clustering (DDFC) protocol to handle spatial similarity between node readings. The DDFC operation takes into account the biological principles of fireflies to ensure distributed synchronization of the clusters similar readings aggregations.…”

Section: Related Workmentioning

confidence: 99%

“…The objective of this part of the algorithm is to select from correlated nodes the nodes to be activated in the next period and having the highest remaining energy. In a second step, for the nodes that do not have any correlation with other sensors, they must be in active mode for next rounds (lines [15][16][17][18][19]. After that, all the nodes in set E will send their readings of the first period to the sink and be activated for the next period, while the others do not sen d their readings and go to sleep node for the next period.…”

Section: Correlation Degree and Residual Energy (Cdre) Strategymentioning

confidence: 99%

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Energy-efficient scheduling strategies for minimizing big data collection in cluster-based sensor networks

Harb

Makhoul

2018

Peer-to-Peer Netw. Appl.

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Today, wireless sensor networks (WSNs) have been widely used in monitoring various applications, such as environment, military and healthcare, etc. The explosive growth of the data volume generated in these applications has led to one of the most challenging research issues of the big data era.To deal with such amounts of data, exploring data correlation and scheduling strategies have received great attention in WSNs. In this paper, we propose an efficient mechanism based on the Euclidean distance for searching the spatialtemporal correlation between sensor nodes in periodic applications. Based on this correlation, we propose two sleep/active strategies for scheduling sensors in the network. The first one searches the minimum number of active sensors based on the set covering problem while the second one takes advantages from the correlation degree and the residual energy of the sensors for scheduling them in the network. Our mechanism with the proposed strategies were successfully tested on real sensor data. Compared to other existing techniques, the simulation results show that our mechanism significantly extends the lifetime of the network while conserving the quality of the collected data and the coverage of the monitored area.

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“…Additionally, H-DHAC still could be operational in the clear presence of GPS failures. Gielow, Fernando et al [19] proposed architecture for dynamic and distributed data-aware clustering, and the Dynamic Data-aware Firefly-based Clustering (DDFC) protocol to deal with spatial similarity between node readings. The DDFC operation takes into consideration the biological principles of fireflies to make sure distributed synchronization of the clusters' similar readings aggregations.…”

Section: Pasccc: Priority-based Application-specific Congestion Contrmentioning

confidence: 99%

A Study of Congestion Control Protocols for Wireless Sensor Networks

Singh¹,

Singh²

2015

IJCA

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Within the last couple of years a comparatively large amount of clustering routing protocols have already been developed for WSNs. This paper is an effort to comprehensively review and critically discuss the absolute most prominent clustering routing algorithms which have been developed for WSNs. The goals with this survey are to produce a large audience conscious of the existence and of the usually good performance of numerous clustering routing protocols in WSNs. From the survey, it has been discovered that none of technique performs effectively in most fields. Therefore the paper ends with the future scope to overcome these issues.

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Data similarity aware dynamic node clustering in wireless sensor networks

Cited by 29 publications

References 26 publications

Firefly-inspired and robust time synchronization for cognitive radio ad hoc networks

Firefly-inspired and robust time synchronization for cognitive radio ad hoc networks

Energy-efficient scheduling strategies for minimizing big data collection in cluster-based sensor networks

A Study of Congestion Control Protocols for Wireless Sensor Networks

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