Algorithm for data similarity measurements to reduce data redundancy in wireless sensor networks

Ghaddar, Alia; Razafindralambo, Tahiry; Simplot-Ryl, Isabelle; Tawbi, Samar; Hijazi, Abbas

doi:10.1109/wowmom.2010.5534888

Cited by 23 publications

(10 citation statements)

References 18 publications

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“…Other papers worked on data correlation and data similarity between several features as in [16], [5], [13], [12], [1] and [4]. Authors in [12] present a Bayesian Inference Approach to detect data with high spatio-temporal correlated data, to avoid transmitting data that can be reconstructed from another data such as temperature and humidity in some cases.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Data Reduction in WSN for Smart Agriculture

Salim

Mitton

2020

Advanced Information Networking and Applications

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Nowadays, the agriculture domain faces a lot of challenges for a better usage of its natural resources. For this purpose, and for the increasing danger of climate change, there is a need to locally monitor meteorological data and soil conditions to help make quicker and more adapted decision for the culture. Wireless Sensor Networks (WSN) can serve as a monitoring system for those types of features. However, WSN suffer from the limited energy resources of the motes which shorten the lifetime of the overall network. Every mote periodically captures the monitored feature and sends the data to the sink for further analysis depending on a certain sampling rate. This process of sending big amount of data causes a high energy consumption of the sensor node and an important bandwidth usage on the network. In this paper, a Machine Learning based Data Reduction Algorithm (MLDR) is introduced. MLDR focuses on environmental data for the benefits of agriculture. MLDR is a data reduction approach which reduces the amount of transmitted data to the sink by adding some machine learning techniques at the sensor node level by keeping data availability and accuracy at the sink. This data reduction helps reduce the energy consumption and the bandwidth usage and it enhances the use of the medium while maintaining the accuracy of the information. This approach is validated through simulations on MATLAB using real temperature data-sets from Weather-Underground sensor network. Results show that the amount of sent data is reduced by more than 70% while maintaining a very good accuracy with a variance that did not surpass 2 degrees.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Based Data Reduction in WSN for Smart Agriculture

Salim

Mitton

2020

Advanced Information Networking and Applications

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“…A local elimination has been accomplished which expels the repeated messages locally in every state of the automaton. Further, the approach proffered in [10] depicts a methodology where a method is proposed to estimate the similarity between the data gathered to the base station.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Event Coverage and Redundant Data Minimization Employing Pentagonal Scalar Premier Selection in Wireless Multimedia Sensor Networks (WMSNs)

2019

IJITEE

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In the modern era, data redundancy has become one among the predominant ultimatums encountered in Wireless Multimedia Sensor Networks (WMSNs), which occurs because of event information reporting through the scalars residing at the superimposing zones of field of views (FoVs) of multiple camera sensors. As a result, same data is transferred many times, thus leading to redundancy in data transfer. Therefore, the aim is to select the representatives of scalar sensors called scalar premiers (SPs) that can report the event information in lieu of all the scalars while diminishing the redundant data transfer and improving the event coverage. We have proffered a pentagonal scheme of SP selection that chooses five SPs in each of the virtual compartments of the monitored zone efficiently. The chosen SPs operate as nominee of scalars for event information transmittal. Extensive experiments have been accomplished to affirm the efficiency of our proffered method. We changed the number of cameras deployed (noc) and the number of scalars deployed (nos). The results attained from the experimental studies in terms of number of camera sensors activated (nca), coverage ratio (cr), redundancy ratio (rr), event loss ratio (elr) and energy expenditure for camera actuation (eeca) assert the superiority of our profferred approach over existing approaches.

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“…The fuzzy aggregation technique in equation 7 [9] can measure the data similarity degree considering the spatial correlation of two data a and b that is sensed by two adjacent nodes.…”

Section: Dynamic Node Clustering Based On Data Similaritymentioning

confidence: 99%

Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

Misbahuddin

Ratna

Sari

2018

INT J COMPUT COMMUN

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In multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. The cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. This paper proposes a dynamic multihop routing algorithm named Data Similarity Aware for Dynamic Multi-hop Routing Protocol (DSA-DMRP) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. The DSA-DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. In this mechanism, each node can recognize and note its similar neighbor nodes. Next, K-hop Clustering Algorithm (KHOPCA) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. The DSA-DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA and can satisfy the requirement of the dynamic multi-hop routing protocol.

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Algorithm for data similarity measurements to reduce data redundancy in wireless sensor networks

Cited by 23 publications

References 18 publications

Machine Learning Based Data Reduction in WSN for Smart Agriculture

Machine Learning Based Data Reduction in WSN for Smart Agriculture

Enhanced Event Coverage and Redundant Data Minimization Employing Pentagonal Scalar Premier Selection in Wireless Multimedia Sensor Networks (WMSNs)

Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

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