A Comprehensive Planning Framework for Connected Elevated LiDAR Sensors

Abstract

The combination of mobile edge computing (MEC) and sensing technologies, such as light detection and ranging (LiDAR), offers a viable path toward enhancing autonomous vehicle navigation and traffic monitoring in the context of intelligent transportation systems. In order to meet these needs, this article offers a methodology that investigates the use of elevated LiDAR (ELiD) and its integration with MEC. Our work focuses on two main challenges: optimizing the placement of ELiDs to ensure extensive urban coverage and minimizing network latency by efficiently routing data to MEC servers. By proposing a heuristic for real-time task allocation, we aim to enhance safety and operational efficiency in smart cities. Our findings show a modest optimality gap where the heuristic achieves a balance between computational efficiency and minimized cloud dependency, albeit at the cost of a marginally increased latency, highlighting the nuanced tradeoffs in edge-to-cloud task distribution for efficient LiDAR data processing in smart cities.