An energy-efficient data aggregation approach for cluster-based wireless sensor networks
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An energy-efficient data aggregation approach for cluster-based wireless sensor networks Syed Rooh Ullah Jan1 · Rahim Khan1
· Mian Ahmad Jan1
Received: 12 August 2020 / Accepted: 2 November 2020 © Institut Mines- and Springer Nature Switzerland AG 2020
Abstract In wireless sensor networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes considerable sensor node resources. Data redundancy occurs due to the spatial and temporal correlations 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 knowledge extraction by eliminating the correlated data. However, most data aggregation techniques have low accuracy because they do not consider the presence of erroneous data from faulty nodes, which represents 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: the node level and the cluster head level. At the node level, the data aggregation is performed using the exponential moving average (EMA), and a threshold-based mechanism is adopted to detect any outliers to improve the accuracy of data aggregation. At the cluster head level, we have employed a modified version of the 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, and energy consumption at the nodes and cluster heads and delivers highly refined, fused data to the base station. Keywords Wireless sensor network · Data aggregation · Energy efficiency · Accuracy · Outlier detection
1 Introduction In WSNs, a large number of nodes sense the environment, collect the raw data, and forward it to a centralized base station for further analysis [1]. In these networks, the nodes are densely deployed to generate spatially and temporally correlated data streams [2]. The transmission and processing of these streams consume a considerable amount of the available network resources. All of the network entities suffer considerably while processing and transmitting these streams. Forwarding these redundant data streams exposes the network to various challenges such as energy depletion, bandwidth consumption, and numerous overhead costs that are associated with data communication, processing, Rahim Khan
[email protected] 1
Department of Computer Science, Abdul Wali Khan University, Mardan, KPK, Pakistan
and storage [3]. Furthermore, in these networks, the performance of resource-constrained miniature nodes is adversely affected by various in-node operations such as data communication, computation, and sensing. Among these operations, data communication consumes considerably more energy. Transmitting higher volumes of raw redundant streams rapidly depletes the resources of these nodes [4
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