Adaptive Location-based Routing Protocols for Dynamic Wireless Sensor Networks in Urban Cyber-physical Systems

Abstract

This study investigates the development and enhancement of adaptive location-based routing protocols within dynamic wireless sensor networks (WSNs) in urban cyber-physical systems, recommending the implementation of the study’s innovative Urban Adaptive Location-based Routing Protocol (UALRP). This innovative protocol integrates real-time data analytics and adaptive machine learning models into its algorithmic framework to dynamically optimize routing decisions based on continuously changing urban conditions. Through the utilization of data-driven simulation models and machine learning techniques, the research sought to significantly improve the efficiency, reliability, and scalability of urban WSNs. Existing protocols such as Geographic Adaptive Fidelity (GAF), Greedy Perimeter Stateless Routing (GPSR), and Dynamic Source Routing (DSR) were critically assessed under urban settings using extensive datasets detailing New York City's traffic patterns and environmental variables. The analysis demonstrated that while GPSR showed superior performance in terms of latency, throughput, and energy efficiency among the traditional protocols, the introduction of UALRP, with its advanced predictive and adaptive capabilities, can further optimize these metrics. The study affirms the critical role of enhancing location accuracy and the ongoing advancement of machine learning models within urban routing protocols. These insights advocate for the broader implementation of adaptive strategies like UALRP to foster the development of more resilient and efficient urban cyber-physical systems.