REDD: Redundancy eliminated data dissemination in cluster based mobile sinks

Ramachandran, Sumalatha; Gopi, Aswin Kumar; Elumalai, Giridara Varma; Chellapa, Murugesan

doi:10.1109/icrtit.2011.5972478

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…An efficient cluster-based data aggregation scheme for heterogeneous sensor networks was developed in [15], where inter-(intra-) cluster data aggregation is performed to eliminate redundant data. The cluster-based approach in [25] uses a context-aware approach to validate data, while intra-cluster and inter-cluster redundancy is eliminated when sensors belong to the same cluster or neighboring clusters, respectively, for the validated data. The work in [28] focuses on the issues of accuracy, traffic load, redundancy elimination and delay when performing data aggregation, and proposed a model to address the aforementioned issues.…”

Section: Related Workmentioning

confidence: 99%

“…During the training stage of the algorithm, the computational complexity at the FC is O(N p 2 ) dominated by PCA. During the operational stage, the reconstruction formulas in(24) and(25), reveal that the computational complexity at the FC is of theorder of O(L • T • ∑ M m=1 (|M m |− 1)). The scalability of the algorithm to handle data from large number of sensors is another important aspect in practical deployments.…”

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confidence: 99%

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Data-driven sensors clustering and filtering for communication efficient field reconstruction

2017

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A novel communication efficient scheme for reconstructing a field sensed by spatially scattered sensors is proposed. The field is formed by multiple sources, while a fusion center gathers the sensor measurements. The goal is to reconstruct the field at the fusion center using only the measurements of a small number of sensors. The framework entails learning the correlation structure of the field by determining clusters of correlated sensors observing the same set of sources. Combining moving-average filtering along with principal component analysis applied in the sensor data covariance the number of sources can be determined, while norm-one regularized canonical correlations are utilized to determine the different correlated clusters. A novel iterative interplay of regularized canonical correlations with principal component analysis is designed that determines correctly the correlated clusters as the number of training data goes to infinity. From each cluster only a head sensor transmits data to the fusion center, while the measurements of the remaining sensors are reconstructed using proper linear filters that learn the correlation pattern within a cluster via normalized least mean squares. The novel approach substantially reduces the communication cost. Extensive numerical tests demonstrate the effectiveness of the proposed scheme in field recovery.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

Data-driven sensors clustering and filtering for communication efficient field reconstruction

2017

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“…It considers a network with heterogeneous nodes in terms of energy and mobile sink to aggregate data packets. A novel cluster-based strategy for eliminating redundancy in the data dissemination process was proposed by Ramachandran et al 12 Obviously, for multi-node prediction, the overall computation cost in the network significantly increases, but the amount and frequency of communication are largely reduced.…”

Section: Introductionmentioning

confidence: 99%

Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks

Song

Sui

Han

et al. 2020

International Journal of Distributed Sensor Networks

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Nodes in a wireless sensor network are normally constrained by hardware and environmental conditions and face challenges of reduced computing capabilities and system security vulnerabilities. This fact calls for special requirements for network protocol design, security assessment models, and energy-efficient algorithms. Data aggregation is an effective energy conservation technique, which removes redundant information from the data aggregated from neighbor sensor nodes. How to further improve the effectiveness of data aggregation plays an important role in improving data collection accuracy and reducing the overall network energy consumption. Unfortunately, sensor nodes are normally deployed in an open environment and thus are subject to various attacks conducted by adversaries. Consequently, data aggregation brings new challenges to wireless sensor network security. In this article, we propose a novel secure data aggregation solution based on autoregressive integrated moving average model, a time series analysis technique, to prevent private data from being learned by adversaries. We leverage the autoregressive integrated moving average model to predict the data volume in sensor nodes, and update and synchronize the model as needed. The experimental results demonstrate that our model provides accurate predictions and that, compared with competing methods, our solution achieves better security, lower computation and communication costs, and better flexibility.

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An approach to data redundancy reduction and secured data delivery using spatial‐temporal correlation factors in heterogeneous Mobile Wireless Sensor Network

Thandapani¹,

Arunachalam

Durairaj

2022

Int J Communication

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As the state-of-the-art technology in the areas of IoT, battlefield surveillance, medical, military applications, and so on, the Mobile Wireless Sensor Network (MWSN) has attracted the attention of vendors and academics in recent years.In MWSN, energy optimization is the major issue because of its dynamicity property. The energy optimization can be achieved by avoiding node compromise attacks and ensuring secured data transmission in the network. In this paper, an algorithm SDCH-M (Secured Data for Mobile Clustering Hierarchy) is proposed to ensure secure data delivery in a double cluster head hybrid topology, which optimizes the network energy consumption. Data redundancy is reduced by estimating spatial-temporal correlation factors, and the variantround voting mechanism is used to verify the accuracy of delivered data and to identify compromised nodes through voting by the base station. Results from the simulation show that the proposed SDCH-M algorithm optimizes energy than M-LEACH and C-LEACH algorithm. The average delivery ratio of SDCH-M (DCH), SDCH-M (SCH), E 2 RP, M-LEACH, and C-LEACH are 0.9175, 0.9037, 0.828, 0.799, and 0.681, respectively. The average delay of M-LEACH, C-LEACH, E 2 RP, SDCH-M (DCH), and SDCH-M (SCH) algorithms is 6.851, 8.655, 6.3, 5.585, and 5.97 s, respectively.

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REDD: Redundancy eliminated data dissemination in cluster based mobile sinks

Cited by 5 publications

References 6 publications

Data-driven sensors clustering and filtering for communication efficient field reconstruction

Data-driven sensors clustering and filtering for communication efficient field reconstruction

Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks

An approach to data redundancy reduction and secured data delivery using spatial‐temporal correlation factors in heterogeneous Mobile Wireless Sensor Network

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