Data Aggregation with Spatially Correlated Grouping Technique on Cluster-Based WSNs

Cho, Chuan-Yu; Lin, Chun-Lung; Hsiao, Yu-Hsiang; Wang, Jia-Shung; Yang, Kai-Chao

doi:10.1109/sensorcomm.2010.93

Cited by 18 publications

(15 citation statements)

References 22 publications

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“…In [12], the idea of minimizing energy consumption by use of additive and divisible aggregation function at cluster head and sink is explored. In [13] Cluster Wide Correlated Grouping (CWCG), proposes the hybrid structure for data aggregation. It uses the concept of temporal and spatial grouping of nodes.…”

Section: Related Workmentioning

confidence: 99%

Mobility and Heterogeneity Aware Cluster-Based Data Aggregation for Wireless Sensor Network

Mantri

Prasad

2015

Wireless Pers Commun

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Internet of things is the modern era, which offers a variety of novel applications for mobile targets and opens the new domains for the distributed data aggregations using wireless sensor networks. However, low cost tiny sensors used for network formation generate the large amount of redundant sensing data and hence, results in energy and bandwidth constraints. In this context, the paper proposes the sink mobility and nodes heterogeneity aware cluster-based data aggregation algorithm (MHCDA) for efficient bandwidth utilization and an increase in network lifetime. The proposed algorithm uses a predefined region for the aggregation of packets at the cluster head for minimizing computation and communication cost. MHCDA exploits correlation of data packets generated by nodes with a variable packet generation rate to reduce energy consumption by 8.66 %. Also, it prolongs the network life by 23.53 % as compared to with and without mobility of the sink and state of the-art solutions.

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Section: Related Workmentioning

confidence: 99%

Mobility and Heterogeneity Aware Cluster-Based Data Aggregation for Wireless Sensor Network

Mantri

Prasad

2015

Wireless Pers Commun

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“…[2,[4][5][6][7][8][9][10][11][12]. Various studies use correlation in aggregation technique for FS model such as Ref.…”

Section: Back Ground and Previous Workmentioning

confidence: 99%

“…[2,[4][5][6][7][8][9][10][11][12]. In [10], the authors proposed the method to organize nodes inside the same cluster into highly spatial-correlated groups and compress the data using linear regression method. CAG (Clustered AGgregation) [5] and TiNA (Temporal coherency-aware in-Network Aggregation) [7] use a similar mechanism to group several close values into one specific equivalent value, but in ADA (Adaptive Data Aggregation) [8], the number of sensors that must send their data are calculated based on the specified reliability according to their correlation.…”

Section: Back Ground and Previous Workmentioning

confidence: 99%

“…Various studies use correlation in aggregation technique for FS model such as Ref. [2,[4][5][6][7][8][9][10][11][12]. In 3D-DCT (3D-Discreet Cosine Transform) [4] with respect to using Centralized Source Coding (CSC), the discreet cosine transform technique is applied for performing the lossy CSC for aggregating the data.…”

Section: Back Ground and Previous Workmentioning

confidence: 99%

“…CAG (Clustered AGgregation) [5] and TiNA (Temporal coherency-aware in-Network Aggregation) [7] use a similar mechanism to group several close values into one specific equivalent value, but in ADA (Adaptive Data Aggregation) [8], the number of sensors that must send their data are calculated based on the specified reliability according to their correlation. In [10], the authors proposed the method to organize nodes inside the same cluster into highly spatial-correlated groups and compress the data using linear regression method. In [11], by measuring the spatial correlation between data sampled by different sensors, the algorithm constructs a dominating set as the sensor network backbone is used to realize the data aggregation based on the information description performance of the dominators.…”

Section: Back Ground and Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

DAMORD: an aggregation algorithm for mobile point source using rate‐distortion theory in wireless sensor networks

Aminian

Sabaei

Akbari

2014

Int J Communication

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By rapid advances in the development of electronic components and with declining cost of sensor network deployment, we are witnessing the amazing growth of these networks. The energy consumption constraints have imposed the importance and necessity of new concepts such as data aggregation. In this paper, a new method is proposed for aggregating the data received from the mobile point source in a sensor network. In this method called DAMORD (Data Aggregation for Mobile Object using Rate-Distortion), by using RD theory and considering spatial and temporal correlation, we can reduce the load of data transmitted to base station with considering the maximum distortion that can be tolerated by the user. To the best of our knowledge, nobody has yet used the RD theory for the data aggregation of mobile object in the sensor networks. A mathematical model followed by detailed model verification demonstrated the efficiency of the proposed method under different conditions.

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Optimal Node Selection Using Estimated Data Accuracy Model in Wireless Sensor Networks

Karjee

Jamadagni

2012

Lecture Notes in Electrical Engineering

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One of the major task of wireless sensor network is to sense accurate data from the physical environment. Hence in this paper, we develop an estimated data accuracy model for randomly deployed sensor nodes which can sense more accurate data from the physical environment. We compare our results with other information accuracy models and shows that our estimated data accuracy model performs better than the other models. Moreover we simulate our estimated data accuracy model under such situation when some of the sensor nodes become malicious due to extreme physical environment. Finally using our estimated data accuracy model we construct a probabilistic approach for selecting an optimal set of sensor nodes from the randomly deployed maximal set of sensor nodes in the network.

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Data Aggregation with Spatially Correlated Grouping Technique on Cluster-Based WSNs

Cited by 18 publications

References 22 publications

Mobility and Heterogeneity Aware Cluster-Based Data Aggregation for Wireless Sensor Network

Mobility and Heterogeneity Aware Cluster-Based Data Aggregation for Wireless Sensor Network

DAMORD: an aggregation algorithm for mobile point source using rate‐distortion theory in wireless sensor networks

Optimal Node Selection Using Estimated Data Accuracy Model in Wireless Sensor Networks

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