Feasibility Study of XJTLU Campus-Wide Indoor Localization System Based on Deep Neural Networks
Table of Contents
People
Faculty
- Dr Kyeong Soo (Joseph) Kim, Department of Electrical and Electronic Engineering, XJTLU
- Dr Sanghyuk Lee, Department of Electrical and Electronic Engineering, XJTLU
- Dr Chengzhang Lee, Department of Civil Engineering, XJTLU
- Prof Thomas Chen, Department of Electrical & Electronic Engineering, City University of London
Research Associates
- Mr Xintao Huan, Department of Electrical and Electronic Engineering, XJTLU
- Mr Jaehoon Cha, Department of Electrical and Electronic Engineering, XJTLU
Grants
- Xi'an Jiaotong-Liverpool University Key Programme Special Fund (KSF)–Exploratory Research Programme (under Grant KSF-E-25)
Duration
- Jan./2019–Mar./2022 (3 years and 3 months)
Overview
Location awareness is one of enabling technologies for future smart and green cities; understanding where people spend their times and how they interact with environments is critical to realising this vision. The partners in this project, who have expertise in diverse areas, will carry out a feasibility assessment and road mapping for XJTLU Campus Information and Visitor Service System with the aim of identifying key component technologies and preparing plans for its implementation as a test bed for large-scale location-aware services in SIP & its use cases for behavioural study of students and visitors on the campus. At the core of the system is indoor localisation based on wireless fingerprinting, which utilises received signal strengths (RSSs) from wireless network infrastructure to estimate a user location; based on this localisation, the system can provide location-aware services by integrating existing data/services available on XJTLU Intranet and tailoring them for the location.
Figure 1: XJTLU campus information and visitors service system.
Meetings
Outcomes
- Xintao Huan, Kyeong Soo Kim, and Junqing Zhang, "Improvement and defense of clock-skew-based node identification in WSNs against spoofing attacks based on radio information and a single-input multiple-output convolutional neural network," submitted to IEEE Transactions on Communications, Nov. 10, 2020.
- Xintao Huan and Kyeong Soo Kim, "Per-hop delay compensation in time synchronization for multi-hop wireless sensor networks based on packet-relaying gateways," IEEE Communications Letters, vol. 24, no. 10, pp. 2300-2304, Oct., 2020. [SCI] (DOI)
- Jaehoon Cha, Kyeong Soo Kim, and Sanghyuk Lee, "Hierarchical auxiliary learning," Machine Learning: Science and Technology, vol. 1, no. 4, pp. 1-11, Sep. 11, 2020. (DOI)
- Xintao Huan, Kyeong Soo Kim, Sanghyuk Lee, Eng Gee Lim, and Alan Marshall, "Improving multi-hop time synchronization performance in wireless sensor networks based on packet-relaying gateways with per-hop delay compensation," submitted to IEEE Transactions on Communications, Sep. 6, 2020.
- Xintao Huan, Kyeong Soo Kim, Sanghyuk Lee, Eng Gee Lim, and Alan Marshall, "A beaconless asymmetric energy-efficient time synchronization scheme for resource-constrained multi-hop wireless sensor networks," IEEE Transactions on Communications, vol. 68, no. 3, pp. 1716-1730, Mar. 2020. [SCI] (DOI)
- Xintao Huan, Kyeong Soo Kim, Sanghyuk Lee, and Moon Keun Kim, "Optimal message bundling with delay and synchronization constraints in wireless sensor networks," Sensors, vol. 19, no. 18:4027, Sep. 18, 2019. [SCIE] (DOI)
Related Projects
- XJTLU SURF-201830: Trajectory Estimation of Mobile Users/Devices based on Wi-Fi Fingerprinting and Deep Neural Networks
- XJTLU SURF-201739: Indoor Localisation Based on Wi-Fi Fingerprinting with Fuzzy Sets
- XJTLU RISGC-2017-4: Feasibility Assessment and Roadmap for XJTLU Campus Information and Visitor Service System as A Test Bed for Large-Scale Location-Aware Services in SIP