Journal Press India^®

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The rapid expansion of urban populations and vehicular networks has intensified the demand for efficient, real-time traffic monitoring and management systems. Traditional cloud-based approaches, while powerful, often suffer from high latency, excessive bandwidth consumption, and limited adaptability under dynamic traffic and network conditions. Edge computing, by decentralizing computational capabilities closer to data sources, presents a viable solution to address these challenges. However, heterogeneous edge devices with diverse processing capabilities and energy constraints require optimized resource allocation strategies to maintain low latency, energy efficiency, and scalability. This paper presents a comprehensive literature survey on algorithms, architectures, energy-efficient techniques, and resource optimization methods for edge computing in the context of real-time traffic monitoring. The reviewed works span deep reinforcement learning, heuristic algorithms, game theory, hybrid optimization methods, and predictive models, with application domains ranging from vehicular edge computing to Internet of Things (IoT)-enabled smart city environments. Based on the critical analysis of 29 selected papers published between 2018 and 2024, the study formulates the core research problem, identifies current limitations, and outlines an adaptive, reinforcement learning-driven framework for heterogeneous edge resource allocation. The proposed research aims to bridge the gap between computational efficiency, energy conservation, and responsiveness, thereby enabling scalable and sustainable smart traffic management systems.

Keywords

Edge computing; Resource allocation; Real-time traffic monitoring; Energy efficiency; Heterogeneous networks; YOLOv8; Task offloading; Vehicular networks

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