Enhancing the Applicability of Low Power Wide Area Networks in IoT-Oriented Smart City Deployment


Student thesis: Doctoral Thesis

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Award date22 Jan 2021


The development of smart cities (SCs) has been a global trend. The emerging low power wide area network (LPWAN) technologies have gained much attention because they can provide a phenomenal range of up to tens of kilometers with a battery life of approximately ten years or more but scarifies data rate performance. Therefore, LPWANs are not suitable for all Internet of things (IoT) applications and may not be able to replace conventional technologies (e.g., Wi-Fi, Long Term Evolution (LTE), and radio-frequency identification detection (RFID)). Deciding whether LPWANs apply to specific IoT-oriented applications in SC deployment is challenging. Moreover, several competitive LPWAN technologies (e.g., LoRa, Sigfox, and narrowband-IoT) exist in the market, rendering the selection of most appropriate LPWAN technology another challenging issue. The adoption of non-appropriate technologies may lead to heavy extra expenditure and even failure of the whole project. Therefore, this thesis focuses on enhancing the applicability of LPWANs in the deployment of IoT-oriented SC applications and addresses the following five contributions:

1. An LPWAN index (LPWAN-Dex) is defined to provide a fair basis for the comparison of performances among emerging LPWAN technologies according to specific application requirements. LPWAN-Dex helps to decide the applicability of LPWANs and select the most appropriate LPWAN technology that contributes to the achievement of best practices.

2. The applicability of LPWANs in indoor localization is explored. LoRaWAN is selected, and indoor localization with boundary autocorrelation based on extreme received signal strength (ERSS) is proposed for indoor services that are latency-tolerant and location-based. ERSS is defined and sorted out to build a fingerprint radio map that is stable and robust against environmental dynamics. The searching complexity of conventional maximal likelihood estimation is relatively high when the fingerprint radio map is large. Thus, a novel RSS boundary autocorrelation-based location estimation algorithm is proposed to narrow the searching scope. Experimental results showed that the proposed method achieved sub-10-meter localization accuracy, which surpasses existing LPWAN-based localization systems.

3. The applicability of LPWANs in healthcare is explored. Smart healthcare is summarized with “7P” characteristics and its potential applications for LPWAN are examined. With today’s healthcare has transformed from face-to-face treatment to patient-oriented health management, healthcare data is increasingly important. The standards of healthcare data management are reviewed to guide the data interoperability enhancement in future SC development.

4. The applicability of LPWANs in railway safety protection is explored. LPWAN may not be appropriate in this application given the extreme environment (e.g., underground tunnels) and real-time and high data transmission frequency demands. Thus, conventional LTE and RFID technologies are adopted. Most of the failures on rail integrity happen at the weld joint locations (WJLs). Therefore, a railway fault identification (RFI) system based on RFID detection is developed to automatically detect the failures at WJLs and further provide a risk assessment to the rail.

5. A distributed intrusion detection scheme with an adaptive kernel based on the detection of network traffic is proposed to enhance further the applicability of LPWANS through improving the security performance of IoT networks in the SCs. The proposed scheme can distinguish and classify normal traffic from legitimate users against attack traffic in the network. Moreover, a blockchain-based consensus mechanism is developed to synchronize information across all distributed detection points.