Energy Aware Efficient Data Aggregation (EAEDAR) with Re-scheduling Mechanism Using Clustering Techniques in Wireless Sensor Networks

Loganathan, D.; Balasubramani, M.; Sabitha, R.

doi:10.1007/s11277-020-07985-w

Cited by 5 publications

(9 citation statements)

References 26 publications

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“…Several data reduction algorithms based on clustering techniques have been proposed in the past few years 25–33 . The PFF strategy is used in the sensor and aggregator devices 25 .…”

Section: Related Workmentioning

confidence: 99%

“…Then, it uses an improved k‐means clustering algorithm to aggregate similar data sets generated by surrounding nodes into the same clusters, reducing the quantity of data transmitted to the sink even more. Loganathan et al 29 proposed a data reduction method based on aggregation and re‐scheduling using clustering techniques to save energy in sensor networks. The cluster formation and cluster head selection are implemented in an energy‐efficient way.…”

Section: Related Workmentioning

confidence: 99%

“…Several data reduction algorithms based on clustering techniques have been proposed in the past few years. [25][26][27][28][29][30][31][32][33] The PFF strategy is used in the sensor and aggregator devices. 25 The Jaccard similarity is used by researchers in the sensor node to lower the redundancy of data, and set similarity in the aggregator node to minimize duplicated sets of data.…”

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confidence: 99%

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Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Hussein

Idrees

Couturier

2022

Int J Communication

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Summary In the modern world, it will be necessary to deploy a large number of sensor devices to sense everything around us in order to detect changes, risks, and hazards and to mitigate them. This increasing number of sensor devices represents an essential data provider in the Internet of Things (IoT). The devices generate and transmit a huge amounts of data which requires a large amount of storage and high processing power to come real‐time processing and speed up the network. It also leads to an increase in high energy consumption. Thus, it is important to remove redundant data to reduce the data transmission before sending it to the gateway while maintaining a good level of data quality. In this paper, a distributed energy‐efficient data reduction (DEDaR) approach based on prediction and compression to minimize the data transmission in IoT Networks is proposed. The DEDaR is used in periods to make decision. In each period, the autoregressive prediction (ARP) is used to predict the data of the next period and make a decision on whether to send the data of the current period to the gateway or not. In the case of data transmission, the redundant data are eliminated using an efficient compression approach based on adaptive piecewise constant approximation (APCA), symbolic aggregate approximation (SAX), and finally fixed code dictionary (FCD) based on Huffman encoding. The simulation results based on real‐sensed data show that the proposed DEDaR approach outperforms the other recent methods in terms of data reduction percentage, transmitted data size, energy consumption, and data accuracy.

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“…Several data reduction algorithms based on clustering techniques have been proposed in the past few years 25–33 . The PFF strategy is used in the sensor and aggregator devices 25 .…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Hussein

Idrees

Couturier

2022

Int J Communication

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“…WSN is the research topic nowadays enabling several real-time operations, such as tracking the environmentalconditions,climaticandweatherconditions (LSThisaraKandambige.,2018),effective trafficcontrolling(DeepakRewadkarandDharmpalDoye.,2018)preventionofdisasters,monitoring andrecognizingthehealthcareissues (EmanueleCannizzaro,et al,2020;ChoudhuryandAvishek., 2021),industryandenterprisesmanagement,andvideosurveillance (Akyildiz,et al,2002;Mehra,et al,2020;Shyjith,et al,2020,RenjithThomasandDr.MJS.Rangachar.,2019.WSNisconfigured withavastnumberofcloselydistributednodesthatcollectanddistributetheinformationdetectedfrom theenvironment (Loganathan,et al,2020).Boththephysicalandenvironmentalfactorsareeffectively regulatedandsensedusingthesensornodespresentinWSNandtheuserscangatherinformation fromthecollecteddata (Shyjith,et al,2020).Suchadvantageoussensornodescarryasmallbatteryfor performingdifferenttaskslikedatacommunicationanddataprocessingthateasilydraintheenergies inthenodes.Hence,theenergyconsumptionshouldbeminimizedwithoutaffectingthebehaviorof thenetworkandthisaspectisamajorconcerninWSNasthehugeenergylossreducesthelifetimeof thenetworkleadingtonetworkfailure (Pattnaik&Sahu,2020;Li&Liu,2009).Hence,thechallenge reliesonrestrainingthenodalenergywhiletransmittingthedata (Mehta&Saxena,2020).Someof thewell-organizedalgorithmssuchasclusteringalgorithmsarealsodevelopedbyvariousresearchers tominimizetheenergyconsumedtoimprovethenetwork'stime(Soundaram&Arumugam2020).…”

Section: Introductionmentioning

confidence: 99%

“…TheclusteringschemesemployedintheWSNsenhancethelongevityandtheproductivity ofthenetwork (Pattnaik&Sahu,2020).Theclusteringmethoddividesallthesensornodesinthe formoflogicalgroupsandthiscluster-baseddivisionisdonetoachievescalabilityandenergy efficiencyinWSN (Mehta&Saxena,2020).Themainpurposeoftheclusteringschemesisto choosetheCHsamongthecategorizedclustersandfinallytotransmittheobtainedinformationto thesink (Singh&Verma,2017;Pattnaik&Sahu,2020).Foralongperiod,asingleclusternodeis madeascoordinatorforeverynodewhichreducestheenergyconsumedandasaresult,thenetwork lifetime is improvedgradually(Soundaram&Arumugam 2020). Due to these reasons, efficient energy-basedroutingisneededinWSN (Loganathan,et al,2020)suchthatthisenergyeffective routingplaysanimportantroleinimprovingthelifetimeofthenetwork.Theroutingprotocolsare thesetofruleswhichareusedtocreateanappropriatepathinbetweenthecurrentnodeandthe basestationorsinknode(BS).Generally,theroutingprotocoliscategorizedintothreetypesbased onthenetworkstructuressuchashierarchical,location-based,andflat.Hierarchical-basedrouting isfoundtobethebestchoiceamongallthethreetypesforitsperformanceinenergyscalability and efficiency (Lotf, et al, 2010). Routing based on the hierarchical structure is responsible forestablishingtheclusters,withtheCHasthecontrollerofdatagathering,accumulation,and distributionofdataintotheBS (Mehta&Saxena,2020).…”

Section: Introductionmentioning

confidence: 99%

Protruder Optimization-Based Routing Protocol for Energy-Efficient Routing in Wireless Sensor Networks

Thakare

Sankar

2021

International Journal of Business Data Communications and Networking

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The energy efficiency problem is addressed using the Cluster Head (CH) formation, data aggregation, and routing techniques. Therefore,an energy-aware routing algorithm named as protruder optimization algorithm is proposed, which boosts the network lifetime through finding the optimal routing path. The proposed protruder optimization is developed with the hybridization of the wave propagator characteristics and weed characteristics in such a way that the global optimal convergence is boosted while selecting the optimal routing path. Moreover, the communication in the network through the optimal path is progressed through the optimal CHs selection based on fractional artificial bee colony optimization (FABC) and in turn, the energy minimization problem is aided with data aggregation process using sliding window approach that avoids retransmission of the data. The results of the proposed method are compared with the existing methods on the basis of its performance measures such as energy, alive nodes and throughput.

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A survey on network lifetime maximization using data aggregation trees

Kale,

Nene

2024

Int J Communication

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SummaryThe sensor networks are the primary and essential components on which the world of Internet of Things (IoT) is built. IoT empowers smart communication, computation, and sensing capabilities. In sensor networks, the data are collected by the sensor nodes and sent to the sink along a communication path. These communication paths are collaboratively established by the nodes and the sink. By incorporating energy‐efficient data gathering techniques, the lifetime of these networks is improved. The major contribution of the study in this work is to provide a survey of various techniques for data aggregation (DA) and the employed algorithmic strategies that facilitate and influence network lifetime (NL) in these environments. DA in wireless sensor networks (WSN), IoTs, and cloud computing extend the lifetime of these networks since it enables efficient merging of traffic flows, thus reducing transmissions and energy consumption of devices. In sensor networks, data aggregation tree (DAT)‐based routing facilitates energy‐efficient routing that extends NL. NL maximization using DATs constructs DATs with optimal NL and is a known NP‐complete problem. Subsequently, the study in this work surveys the various approaches employed by researchers to construct DATs and discusses techniques for DAT scheduling. This work further explores various sensor deployment techniques and discusses real world scenario in which NL is influenced by uncertainty in communication links. Finally, the study in this survey highlights the achievements in realizing NL improvement using DAT and identifies the limitations and research challenges.

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Energy Aware Efficient Data Aggregation (EAEDAR) with Re-scheduling Mechanism Using Clustering Techniques in Wireless Sensor Networks

Cited by 5 publications

References 26 publications

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Protruder Optimization-Based Routing Protocol for Energy-Efficient Routing in Wireless Sensor Networks

A survey on network lifetime maximization using data aggregation trees

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