Wireless sensor networks attract so much attention in current IoT-enabled industrial and domestic applications having either homogeneous or heterogeneous sensors deployed to acquire information of intent. WSNs are designed to operate using self-powered sensor nodes as their choice of application is geographic critical. Such nodes must support energy efficiency so that network longevity becomes high. Cluster head selection plays a crucial stage in a WSN architecture which mainly focuses on the minimization of network energy consumption. It groups sensor nodes in such a way that a sophisticated network cluster is formed to have enhanced life time besides a low power consumption. A popular clustering technique, known as LEACH and its variants, is found to be energy efficient compared to its counterparts. The authors propose a novel fully connected energy efficient clustering (FCEEC) mechanism using the electrostatic discharge algorithm to establish a fully connected network with shortest path routing from sensor nodes (SNs) to cluster head (CH) in a multihop environment. The proposed electrostatic discharge algorithm (ESDA) enhances network life time while attaining energy efficient full connectivity between sensor nodes. As a result of ESD, the dead node count is reduced significantly so that the network longevity is increased. In the end, simulation results exhibited improved performance metrics such as energy efficiency, dead node count, packet delivery, and network latency compared to certain conventional CH selection approach.
Real world data aggregation and delivery in Internet of Things (IoT) technology are essential to predict and retrieve target data in short time so that the end user feels no delay but ensures a high quality of information. In addition to habitat monitoring and disaster management, these networks have a wide range of other uses, including security and military operations. The processing capabilities of sensor nodes are restricted due to the fact that they have a limited battery life and hence a modest size and processing capacity. WSNs are also susceptible to failure as a result of the limited battery power available. In WSNs, data aggregation is practiced as an energy efficient strategy to reduce computing and transmission latency. It is because of sensor node distribution density that shares the same data at a time data redundancy comes to exist. It is possible to reduce redundancy by adopting a suitable machine learning algorithm while executing the data aggregation process. Researchers are still chasing behind algorithms and modeling strategies effectively to ease the process of developing an effective and acceptable data aggregation strategy from existing wireless sensor network (WSN) models. A three stage framework is proposed for an efficient data aggregation mechanism, and the stages are Modified LEACH, extreme learning machines (ELM), adaptive Kalman filter, and Bi-LSTM. This experiment result shows better performance than the existing methods.
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