TrajEdge: An Efficient and Lightweight Trajectory Data Analysis Framework in Edge Environments

Trajectory data analysis benefits numerous real-world applications and has attracted substantial attention from the research community. With the rapid proliferation of IoT devices and the emergence of edge computing, there has been an increasing demand for efficient trajectory data analytics in edge environments. However, most existing trajectory analysis systems are designed for cloud-based architectures, which face significant limitations in edge settings. These include resource constraints, dynamic network conditions, and inefficient query handling, leading to sub-optimal performance in edge scenarios. To fill this gap, we propose TrajEdge, an efficient and lightweight framework for trajectory data analysis in edge environments. Implementing TrajEdge requires overcoming obstacles posed by limited resources and the dynamic nature of edge networks. To achieve this, we design a novel trajectory composite compression algorithm that delivers high compression ratios, significantly reducing storage pressure on edge devices. Additionally, we introduce three coflow control strategies optimized for varying network conditions, enabling higher system throughput. To further enhance the efficiency of trajectory queries, we develop a spatiotemporal-aware trie-based peer-to-peer (P2P) index. Experimental evaluations on two real-world datasets and one larger synthetic dataset demonstrate that TrajEdge achieves remarkable performance improvements: more than 200 × gains in storage and query efficiency, up to 64% increases in network throughput, compression ratios of up to 95%, and exceptional scalability compared to the state-of-the-art systems. Our source code is available at https://github.com/ZJU-DAILY/TrajEdge.