BHCDA: Bandwidth efficient heterogeneity aware cluster based data aggregation for Wireless Sensor Network

Mantri, Dnyaneshwar S.; Prasad, Neeli R.; Prasad, Ramjee

doi:10.1109/icacci.2013.6637324

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…The paper [23] discusses a mechanism for selecting the clusters similar to that discussed in [19], but for three-level heterogeneous WSNs. In [24], the bandwidth efficient heterogeneity aware cluster based data aggregation (BHCDA) protocol is discussed that uses intra and inter-cluster aggregation on the randomly distributed nodes with variable data generation rate while routing the data to sink. It uses the correlation of data within the packet for applying the aggregation function on the data generated by nodes.…”

Section: Literature Surveymentioning

confidence: 99%

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Energy Efficient Clustering Protocol Using Fuzzy Logic for Heterogeneous WSNs

Singh

Chand

Kumar

2015

Wireless Pers Commun

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In this paper, we propose a multilevel heterogeneous network model characterized by a parameter, called model parameter that helps the model defining a network of 0-level, 1-level, 2-level, 3-level, and 4-level heterogeneity. We consider the hybrid energy efficient distributed (HEED) clustering protocol to estimate the network lifetime and accordingly name it as HEEDML (HEED MultiLevel). Depending on the heterogeneity, its variants have been named as HEEDML-0, HEEDML-1, HEEDML-2, HEEDML-3, and HEEDML-4 for 0-level, 1-level, 2-level, 3-level, and 4-level heterogeneity. The HEEDML-0 is the original HEED. The model parameter also determines the numbers of nodes of each type. We use the same parameters as in the HEED to decide the cluster heads: residual energy and node density. We also consider fuzzy implementation of the HEEDML. The HEEDML-1, HEEDML-2 HEEDML-3, and HEEDML-4 increase network lifetime by 39.61, 117.38, 182.69, and 223.7 %, corresponding to the increase in the network energy as 9.2, 17.40, 21.80, and 24 %, with respect to the HEEDML-0. The fuzzy implementation further increases the network lifetime. The HEEDML-FL-0, HEEDML-FL-1, HEEDML-FL-2, HEEDML-FL-3, and HEEDML-FL-4 increase the network lifetime by 193.84, 270.31, 375.84, 448.33, and 589.07 %, corresponding to the same increase in network energy as that for HEEDML (all levels) with respect to that of the HEEDML-0. The HEEDML-FL-0 increases the network lifetime by 193.84 % with respect to the original HEED without increasing the network energy. The packet delivery, total energy consumption, throughput, average delay, and traffic load have better results.

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Section: Literature Surveymentioning

confidence: 99%

“…In literature, most of the existing models [16][17][18][21][22][23][24][25] consider either two-level or three-level heterogeneity as multilevel heterogeneity. We here consider five-level heterogeneity in our model.…”

Section: Multilevel Heterogeneous Network Modelmentioning

confidence: 99%

Energy Efficient Clustering Protocol Using Fuzzy Logic for Heterogeneous WSNs

Singh

Chand

Kumar

2015

Wireless Pers Commun

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“…to maintain and distribute a detailed picture within the closer vicinity and, coarser and coarser information about increasingly far-off regions. However, many data aggregation approaches (Mantri, Prasad, & Prasad, 2013a;Tian, Marron, & Rothermel, 2005;Yu, Gong, & Xu, 2008;Yuan, Zhan, & Wang, 2014) and applications based on aggregation are proposed in literature, little is explored about the fundamental limitations and requirements of VANET data aggregation. As mentioned earlier that VANET is bandwidth constrained, data aggregation is one of the possibility to address this problem.…”

Section: Introductionmentioning

confidence: 99%

Data relationship degree–based clustering data aggregation for VANET

Kumar

Dave

2015

International Journal of Electronics

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Data aggregation is one of the major needs of vehicular ad hoc networks (VANETs) due to the constraints of resources. Data aggregation in VANET can reduce the data redundancy in the process of data gathering and thus conserving the bandwidth. In realistic applications, it is always important to construct an effective route strategy that optimises not only communication cost but also the aggregation cost. Data aggregation at the cluster head by individual vehicle causes flooding of the data, which results in maximum latency and bandwidth consumption. Another approach of data aggregation in VANET is sending local representative data based on spatial correlation of sampled data. In this article, we emphasise on the problem that recent spatial correlation data models of vehicles in VANET are not appropriate for measuring the correlation in a complex and composite environment. Moreover, the data represented by these models is generally inaccurate when compared to the real data. To minimise this problem, we propose a group-based data aggregation method that uses data relationship degree (DRD). In the proposed approach, DRD is a spatial relationship measurement parameter that measures the correlation between a vehicle's data and its neighbouring vehicles' data. The DRD clustering method where grouping of vehicle's data is done based on the available data and its correlation is presented in detail. Results prove that the representative data using proposed approach have a low distortion and provides an improvement in packet delivery ratio and throughput (up to of 10.84% and 24.82% respectively) as compared to the other state-of-the-art solutions like Cluster-Based Accurate Syntactic Compression of Aggregated Data in VANETs. IntroductionVehicular ad hoc networks (VANETs) have raised much attention from both industry and academia during the past several years. VANET is a special type of ad hoc network where nodes are vehicles that communicate with each other and sometimes with roadside units. The interest and popularity of VANETs come from the distributed traffic information systems (DTIS). DTIS constitutes a developing system that provides greater safety, comfort and convenience in transportation systems. DTIS needs less investment as compared to the centralised traffic information systems and also has stronger resilience to disruptions and disasters. Moreover, it is more suitable for vital traffic safety applications like highway collision avoidance

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“…BHCDA use the absolutely compressible aggregationfunction [1] to enhance the bandwidth utilization and energysaving as compare with EECDA.The aggregation ratio [2] increases as the number of nodes and their density in the network increase compare to other solutions that are not affected by the parameters.Further cluster adjustment and routing update [3] with a small footprint running on the nodes which indicate EHGUC-OAPR can efficiently balance the energy consumption of the whole network and proficiently improve the data delivery ratio.…”

Section: Related Workmentioning

confidence: 99%

“…Also most of data aggregation methods are either based upon the clustering or treebased approach but the use of hybrid data aggregation has been ignored by the most of the researchers. [1,10,16] So, using the hybrid data aggregation in energy harvesting based WSNs is the main motivation of this research work.…”

Section: Research Gaps and Research Motivationmentioning

confidence: 99%