Research Article
Real Time Tracking of the Position of Intelligent Logistics Cold Chain Transportation Vehicles Based on Wireless Sensor Networks
@INPROCEEDINGS{10.1007/978-3-031-50552-2_7, author={Dong’e Zhou and Xunyan Bao}, title={Real Time Tracking of the Position of Intelligent Logistics Cold Chain Transportation Vehicles Based on Wireless Sensor Networks}, proceedings={Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part IV}, proceedings_a={ADHIP PART 4}, year={2024}, month={3}, keywords={Wireless Sensor Network Smart Logisticsm Cold Chain Transportation Vehicle Position Tracking}, doi={10.1007/978-3-031-50552-2_7} }- Dong’e Zhou
Xunyan Bao
Year: 2024
Real Time Tracking of the Position of Intelligent Logistics Cold Chain Transportation Vehicles Based on Wireless Sensor Networks
ADHIP PART 4
Springer
DOI: 10.1007/978-3-031-50552-2_7
Abstract
In order to more effectively track intelligent logistics cold chain transportation vehicles in real-time, This article proposes a real-time tracking algorithm for the position of intelligent logistics cold chain transportation vehicles based on wireless sensor networks. In the first stage, the received RSSI value of the anchor node is directly used for coarse positioning. In the second stage, the coarse positioning value is used as the initial solution optimization iteration of the wireless sensor network. In addition, this article also conducts research on the prediction and tracking of location nodes for smart logistics cold chain transportation vehicles, and discusses real-time tracking algorithms for EKF and wireless sensor networks. In view of the fact that EKF algorithm needs to abandon the information of higher order items of the system when approaching the nonlinear, which has caused error accumulation to some extent, the real-time tracking algorithm of wireless sensor network uses UT transformation and deterministic sampling strategy to linearly map the nonlinear system, which retains the information of the system to the greatest extent, and realizes effective prediction and real-time tracking of the nonlinear system.
