Measurement: Sensors (Dec 2022)
Data aggregation scheme for IOT based wireless sensor network through optimal clustering method
Abstract
Wireless sensor network (WSN)based Internet of Things (IoT) has gained much popularity, in offering solutions to several real-time applications by interaction between sensor nodes and physical world entities through interconnected technology. WSN based IoT has become key technology in achieving real-time quality of service (QoS), low-cost operation and long-term reliability. IoT based WSN nodes, permits interconnection of various objects wirelessly and these nodes are tiny, equipped with irreplaceable battery and are resource constrain. Due to continuous sensing and gathering, IoT produce huge volume of data which may results in high computation overhead, data redundancy, packet collision and high energy consumption. To address resource constrains, significant research has been conducted to optimize energy consumption of nodes to extend network lifetime. Most of the existing methods focus on extending network lifetime by scheduling method through duty cycles, however these methods fails to handle data redundancy and has lower throughput. Cluster based data aggregation schemes have offered solution to eliminate data redundancy and harvest energy. In this paper we propose an efficient data aggregation scheme (EDAS) for IoT based WSN, this scheme considers improved low energy adaptive clustering algorithm (I-LEACH) to form optimal number of cluster head (CH), by considering node residual energy and average network energy. Data redundancy is eliminated using network coding, this technique integrates liner XOR operation and ensures non-replicated data transmissions. Finally, we performed simulations of proposed EDAS scheme to evaluate network parameters and performance of EDAS is compared with existing schemes.
