Industrial Internet of Things and Advanced Techniques for Sensor Data Aggregation and Fusion

7Efficient Traffic Detection and Localization in 5G Networks Using IoT-Enhanced Dynamic Ad-Hoc Clusters

Nirmalkumar K.^* and Ramprashath R.

School of Computer Application, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India

Abstract

The advent of 5G networks has ushered in a new connectivity era, bringing unprecedented speed and bandwidth. In this context, the deployment of dynamic ad-hoc networks plays a pivotal role in enhancing traffic detection capabilities. The proposed system leverages the flexibility of ad-hoc networks to create a dynamic infrastructure that adapts to varying traffic patterns, ensuring efficient data transmission and reception. To enhance the precision of traffic detection, the system incorporates advanced clustering algorithms for localization. Clustering algorithms enable the forming of groups of nearby nodes, optimizing communication within these clusters, and minimizing intercluster interference. The integration of Internet of Things (IoT) devices within this framework further enhances its effectiveness. These IoT devices, equipped with various sensors, continuously collect and transmit real-time traffic data. Edge computing nodes process these data locally to reduce latency and improve responsiveness. Moreover, IoT devices facilitate decentralized data analysis and decision-making, allowing clusters to operate autonomously and efficiently.

Keywords: MANETs, network clustering, digital twin, network traffic, network planning, cluster analysis, ...