- Title
- Marginal and average weight-enabled data aggregation mechanism for the resource-constrained networks
- Creator
- Jan, Syed; Khan, Rahim; Khan, Fazlullah; Jan, Mian; Balasubramanian, Venki
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176909
- Identifier
- vital:15190
- Identifier
-
https://doi.org/10.1016/j.comcom.2021.04.004
- Identifier
- ISBN:0140-3664 (ISSN)
- Abstract
- In Wireless Sensor Networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes a considerable amount of sensor node resources. Data redundancy occurs due to the spatial and temporal correlation among the data gathered by the neighboring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates the knowledge extraction by eliminating the correlated data. However, most of the data aggregation techniques have lower accuracy as they do not cater for erroneous data from faulty nodes and pose an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels, i.e., at the node level and at the cluster head level. At the node level, the data aggregation is performed using Exponential Moving Average (EMA) and a threshold-based mechanism is adopted to detect any outliers for improving the accuracy of aggregated data. At the cluster head level, we have employed a modified version of Euclidean distance function to provide highly-refined aggregated data to the base station. Our experimental results show that our approach reduces the communication cost, transmission cost, energy consumption at the nodes and cluster heads, and delivers highly-refined and fused data to the base station. © 2021 Elsevier B.V. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
- Publisher
- Elsevier B.V.
- Relation
- Computer Communications Vol. 174, no. (2021), p. 101-108
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 Elsevier B.V.
- Subject
- 0805 Distributed Computing; 0906 Electrical and Electronic Engineering; 1005 Communications Technologies; Data aggregation; EMWA; Energy efficiency; MWA; Weight-based; Wireless Sensor Network
- Reviewed
- Hits: 1417
- Visitors: 1382
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|