Energy-efficient data routing strategies for IoT-based telematics networks

Telematics-based Internet of Things networks operate under severe energy constraints while facing continuous mobility, bursty traffic, and delay-sensitive data delivery. Routing inefficiencies in such environments directly shorten network lifetime and degrade service reliability. Most existing energy-aware routing approaches depend on reinforcement learning or centralized optimization, which introduce computational overhead, slow adaptation, and limited practicality in highly dynamic telematics scenarios. This study proposes a lightweight, mobility-aware energy-efficient routing framework that relies on localized decision metrics instead of learning-driven control. The routing strategy jointly considers residual energy, link stability, traffic load, and buffer occupancy to adapt paths in real time without global network state. Simulation results obtained over 30 independent runs show that the proposed framework improves packet delivery ratio by approximately 6-9% and extends network lifetime by about 12% compared to RLEAFS, LEA-RPL, and genetic algorithm–based routing schemes. End-to-end delay and routing control overhead are reduced by up to 15% under high- mobility and traffic- load conditions. The available solution provides a scalable and implementable routing system to the energy limited telematics IoT networks that strike a balance between efficiency, stability and operational simplicity.