Improved Smart Energy Efficient Routing Protocol for Wireless Sensor Node Internet of Things

Recently, wireless sensor networks (WSNs) have been considered the fundamental structure that paved the way for the development of the Internet of Things (IoT).However, when wireless sensor networks (WSNs) are connected to the Internet of Things (IoT), a challenging problem occurs since their nodes use too much energy and the network does not last long. As a result, energy limits in sensor nodes, sensor data exchange and routing protocols are the fundamental topics in WSN. This research provides a new method called the enhanced smart-energy-efficient routing protocol (ESEERP). This technology improves the network's connectivity and extends its lifespan in order to address the flaws described above. It chooses the Cluster Head (CH) based on an effective optimization strategy that is developed from a variety of objectives. It helps to reduce the number of sleeping sensor nodes and lowers energy consumption. After the CH selection, the A Sail Fish Optimizer (SFO) is utilized to find a suitable route to the sink node for data transfer. The proposed methodology has been quantitatively investigated in terms of energy utilization, bandwidth, packet delivery ratio, and network longevity. The results have been compared to those of current approaches, including Genetic Algorithm (GA), Ant Lion Optimization (ALO), and Particle Swarm Optimization (PSO).The simulation demonstrates that the proposed technique for the longevity of the network has 3500 cycles, that energy utilization reaches a maximum of 0.5 Joules, that bandwidth transfers data at a rate of 0.52 MBPS, and that the packet delivery ratio (PDR) is 96% for 500 nodes.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-04
DOI
https://doi.org/10.5281/zenodo.15222064
Primary Topic
Advanced Technologies and Applied Computing
Type
article
Field-Weighted Citation Impact
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article

Improved Smart Energy Efficient Routing Protocol for Wireless Sensor Node Internet of Things

Zenodo (CERN European Organization for Nuclear Research)
Advanced Technologies and Applied Computing
article

Improved Smart Energy Efficient Routing Protocol for Wireless Sensor Node Internet of Things

article en

Abstract

Recently, wireless sensor networks (WSNs) have been considered the fundamental structure that paved the way for the development of the Internet of Things (IoT).However, when wireless sensor networks (WSNs) are connected to the Internet of Things (IoT), a challenging problem occurs since their nodes use too much energy and the network does not last long. As a result, energy limits in sensor nodes, sensor data exchange and routing protocols are the fundamental topics in WSN. This research provides a new method called the enhanced smart-energy-efficient routing protocol (ESEERP). This technology improves the network's connectivity and extends its lifespan in order to address the flaws described above. It chooses the Cluster Head (CH) based on an effective optimization strategy that is developed from a variety of objectives. It helps to reduce the number of sleeping sensor nodes and lowers energy consumption. After the CH selection, the A Sail Fish Optimizer (SFO) is utilized to find a suitable route to the sink node for data transfer. The proposed methodology has been quantitatively investigated in terms of energy utilization, bandwidth, packet delivery ratio, and network longevity. The results have been compared to those of current approaches, including Genetic Algorithm (GA), Ant Lion Optimization (ALO), and Particle Swarm Optimization (PSO).The simulation demonstrates that the proposed technique for the longevity of the network has 3500 cycles, that energy utilization reaches a maximum of 0.5 Joules, that bandwidth transfers data at a rate of 0.52 MBPS, and that the packet delivery ratio (PDR) is 96% for 500 nodes.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
Openalex Percentile: Top 48%
Advanced Technologies and Applied Computing
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