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Wireless Sensor Networks (WSN) became a novel technology for ubiquitous livelihood and still remains a hot research topic because of its applicability in diverse domains. Energy efficiency treated as a crucial factor lies in the designing of WSN. Clustering is commonly applied to increase the energy efficiency and reduce the energy utilization. The proper choice of cluster heads (CHs) and cluster sizes is important in a cluster-based WSN. The CHs which are placed closer to base station (BS) are affected by the hot spot issue and it exhausts its energy faster than the usual way. For addressing this issue, a new unequal clustering and routing technique using ant colony optimization (ACO) algorithm is presented. Initially, CHs are chosen and clusters are constructed based on several variables. Next, the ACO algorithm with levy distribution is applied for the selection of optimal paths between two nodes in the network. A comprehensive validation set takes place under diverse situations under the position of BS. The experimental outcome verified the superiority of the presented model under several validation parameters.

The Internet of Things (IoT) is a developing technology in the world of communication and embedded systems. The IoT consists of a wireless sensor network with Internet service. The data size of the sensor node is small, but the routing of the data and energy consumption are important issues that need to be advocated. The Mobile Adhoc Network (MANET) plays a very important role in IoT services. In MANET, nodes are moving within the network. So, routes are created dynamically on demand and do not have any centralized units. The route optimization method addresses issues like selecting the best routes in terms of overhead, loop free, traffic control, balancing, throughput, route maintenance, and so on. In this paper, IoT routes are created between sensors to sink through MANET nodes with WSN routing ideology. The Krill Herd and Feed Forward Optimization (KH-FFO)-based method discovers the routes. The Krill herd algorithm clusters the network. This method increases network speed and reduces energy waste. Feed-forward optimization involves learning all the nodes in the network and identifying the shortest and most energy-efficient route from source to sink. The overall performance of the KH-FFO protocol has improved the network’s capacity, reduced packet loss, and increased the energy utilization of the nodes in the network. The ns-3 simulation for KH-FFO is tested in different node densities and observed energy utilization is increased by 28%, network life is increased by 7%, Packet delivery ratio improved by 7.5%, the End-to-End delay improved by 31% and the Throughput is 3%. These metrices are better than the existing works in the network.

Wireless Sensor Network (WSN) is gaining popularity day by day in a large area of applications. However, the operation of WSN is facing a multitude of challenges, mainly in terms of energy consumption since WSN nodes operate with battery power and changing the batteries is a complicated task, as networks may include hundreds to thousands of nodes. In this context, it is very crucial to know the remaining energy value in the battery of the sensor node to take required actions before losing sensor’s function. Sending these measurements is very expensive in terms of energy and reduces the battery lifetime of the sensor and thus of the entire network. In this paper, we are interested in defining a probabilistic approach which aims to estimate these monitoring energy values and optimize energy consumption in WSN. Our approach is based on hidden Markov chains and includes two phases namely a learning phase and a prediction phase. Our approach is implemented as a web service. We illustrate our approach with a sensor-based health-care monitoring case study for COVID-19 patients. To evaluate our approach, we carry out experimentations based on the AvroraZ^a simulator with a test for different types of applications and for different energy models: μAMPS-specific model, Mica2-specific model, and Mica2-specific model with actual measurements. These experimentations demonstrate the accuracy and efficiency of our approach. Our results show that periodic WSN applications i.e. applications which send monitoring data periodically, tested with the μAMPS-specific model perform an accuracy of 98.65%. In addition, our approach can perform a gain up to 75% of the battery charge of the sensor with an estimation of three-quarters of the remaining energy values.

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