Cyber–Physical–Social Systems for Smart City

Xiong, Gang; Anwar, Noreen; Ye, Peijun; Chen, Xiaoyu; Zhao, Hongxia; Lv, Yisheng; Zhu, Fenghua; Wang, Chao; Liu, Ryan Wen

doi:10.1002/9781119857433.ch19

Cited by 4 publications

(2 citation statements)

References 57 publications

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“…This approach can also overcome the scalability disadvantage of fingerprint-based systems [ 30 , 31 ] by providing a flexible and easily deployable system that can be integrated with existing network infrastructure. However, the implementation of a smart space setting involves cyber-physical systems (CPSs) infrastructure along with other requirements such as security, privacy, safety, and more [ 32 ]. CPSs are a new generation of integrated frameworks and mechanisms that enable the creation of living virtual DTs that replicate the behavior of their physical counterparts.…”

Section: Introductionmentioning

confidence: 99%

“…Considering cyber-physically in indoor positioning can also improve the efficiency and controllability of complex systems through intelligent human-machine interaction in cyberspace through a user interface [ 32 ]. Human operators can utilize DTs to remotely observe the real-time conditions in the operating environment and make better decisions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach

Karakusak,

Kivrak,

Watson

et al. 2023

Sensors

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In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%