Enhancing computational energy transportation in IoT systems with an efficient wireless tree-based routing protocol

Abstract

In this era of technological advancements, our globe has witnessed the tremendous potential of wireless technology, which has revolutionized data transport. One area with a lot of promise for increasing the usage of this technology is traffic control. Real-time path planning algorithms have emerged as an important tool for reducing traffic congestion in urban areas, addressing current state-of-the-art. Real-time traffic data collection, which leverages the capabilities of Vehicle Ad-Hoc Networks (VANETs), offers opportunities to reduce travel costs and complexity. Our study offers an optimized cluster tree-based routing protocol (OCTRPL) within the framework of an advanced IoT-based computational energy transportation system to accomplish this. Vehicles serve as network nodes in this architecture, while traffic stations serve as hubs and routers. We assure the efficient implementation of a shortest-path strategy by using the OCTRPL, thereby lowering data traffic between network nodes and increasing the network's overall lifespan. The Euclidean distance facilitates the development of an ideal cluster, allowing for the selection of Cluster Heads (CHs) using a game theoretic method. Following that, criteria such as expected transmission count, queue utilization ratio, and residual energy ratio (RER) are considered in the route selection process. This set of criteria, known as ETC, assists in selecting the best path for efficient energy transit throughout the network. We illustrate the outstanding performance of our suggested approach in meeting its intended aims by a comparative study of data. The sophisticated IoT-based computational energy transportation system, in conjunction with the efficient wireless tree-based routing protocol, demonstrates promising results and opens up new options for further research and development in this sector.