Energy Aware Efficient Data Aggregation (EAEDAR) with Re-scheduling Mechanism Using Clustering Techniques in Wireless Se
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Energy Aware Efficient Data Aggregation (EAEDAR) with Re-scheduling Mechanism Using Clustering Techniques in Wireless Sensor Networks D. Loganathan1 · M. Balasubramani1 · R. Sabitha2 Accepted: 11 November 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In present scenario of wireless sensor networks and communications, efficient sensed data transmission among nodes is being a great confrontation because of the impulsive and volatile nature of sensors in the network. For providing that and enhancing network lifetime, there are several approaches are developed, specifically using clustering techniques. Still, there are requirements for energy based efficient routing in WSN. With that note, this paper develops anEnergy Aware Efficient Data Aggregation (EAEDAR) and Data Re-Schedulingwith the incorporation of clustering techniques. Moreover, the model used energy based cluster formation and cluster head selection for increasing the network stability and data delivery rate. The model comprises four main phases, namely, Energy factor based cluster formation, Aggregator_SN (Sensor Node) Selection, Efficient Data Aggregation (EDA) and Data Re-Scheduling based on delay and processing time. Furthermore, the model is updated with respect to the status of the nodes and links, for providing consistent network with improved reliable data transmissions. The simulation results portrays the effectiveness of the proposed model over other compared works in terms of the performance factors such as, throughput, packet delivery ratio, network lifetime, transmission delay and packet drop. Keywords Energy aware efficient data aggregation (EAEDA) · Data re-scheduling · Clustering · Aggregator node · Data aggregation · Transmission delay
* D. Loganathan [email protected] M. Balasubramani [email protected] R. Sabitha [email protected] 1
Department of Electronics and Communication Engineering, Info Institute of Engineering, Coimbatore, Tamilnadu 641107, India
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Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, Tamilnadu 641035, India
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1 Introduction Wireless Sensor Network (WSN) is commonly defined as the collection of closely distributed sensor nodes for the accumulation and propagation of sensed data about the environment. There are several applications of WSN in environmental sensing such as Pollution Monitoring, Military Applications, Industrial Monitoring, Fire Detection, etc. [1]. Moreover, the sensor networks observe and controls their surrounding area using remote locations also. However, the sensor nodes that are used in the network for environmental sensing are with some limitations on storage, computational ability, power and energy. Hence, required models are to be designed that provides efficient resource utilization in WSN [2]. The General Mode of Wireless Sensor Network is showed in the Fig. 1. In WSN, data aggregation process is incorporated for enhancing the network lifetime by collecting and
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