Survey/review study A Survey on Evolved LoRa-Based Communication Technologies for Emerging Internet of Things Applications Fang Yao 1,3, Yulong Ding 2,3,*, Shengguang Hong 2,4, and Shuang-Hua Yang 2,5,* 1 The Ta-tech company, Nanjing 210009, China 2 Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, Department of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China 3 Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China 4 The School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150006, China 5 Department of Computer Science, University of Reading, West Berkshire RG6 6UR, United Kingdom * Correspondence: dingyl@sustech.edu.cn; Shuang-hua.yang@reading.ac.uk      Received: 17 September 2022 Accepted: 17 October 2022 Published: 22 December 2022   Abstract: The concept of Internet of Things (IoT) greatly extends the coverage area that human being is able to perceive, access, and even control. By connecting various “Things” to the Internet, the IoT makes it possible to measure and manage the physical world as needed. As one of the most widely adopted Low Power Wide Area network technologies, the Long-Range-Radio (LoRa) has the features of long range, low power, and robustness, and thus plays an important role in building IoT applications where IoT objects are connected to the internet at affordable costs. Since the development of LoRa, many IoT applications have adopted LoRa and achieved success in the market. Currently, IoT technologies keep evolving towards different fields, giving rise to multifarious IoT applications including industrial IoT, smart city IoT, healthcare IoT, and direct-to-satellite IoT. In the meantime, LoRa also keeps developing and finding its position in various IoT applications either as a main or complementary player. The objective of this survey is to (1) provide a fundamental understanding of the LoRa technology; (2) explore research activities studying LoRa based communication systems for new IoT applications; and (3) demonstrate how the LoRa technology works together with other technologies to deliver better IoT services to end users.

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Component-level heterogeneous redundancy is gaining popularity as an approach for preventing single-point security breaches in Industrial Control Systems (ICSs), especially with regard to core components such as Programmable Logic Controllers (PLCs). To take control of a system with componentlevel heterogeneous redundancy, an adversary must uncover and concurrently exploit vulnerabilities across multiple versions of hardened components. As such, attackers incur increased costs and delays when seeking to launch a successful attack. Existing approaches advocate attack resilience via pairwise comparison among outputs from multiple PLCs. These approaches incur increased resource costs due to them having a high degree of redundancy and do not address concurrent attacks. In this paper we address both issues, demonstrating a data-driven component selection approach that achieves a trade-off between resources cost and security. In particular, we propose (i) a novel dual-PLC ICS architecture with native pairwise comparison which can offer limited yet comparable defence against single-point breaches, (ii) a machine-learning based selection mechanisms which can deliver resilience against non-concurrent attacks under resource constraints, (iii) a scaled up variant of the proposed architecture to counteract concurrent attacks with modest resource implications.

Nowadays, the population has been overgrowing due to urbanization, yielding many severe problems in the urban area, including traffic congestion, unbalanced distribution of urban hotspots, air pollution and so on. Due to the uncertainty of the urban environment, it always needs to integrate experts' domain knowledge into solving these issues. In recent years, the visual analytics method has been widely used to assist domain experts in solving urban problems with its intuitiveness, interactivity and interpretability. In this survey, we first introduce the background of urban computing, present the motivation of visual analytics in the urban area and point out the characteristics of visual analytics methods. Second, we introduce the most frequently used urban data, analyse the main properties and provide an overview on how to use these data. Thereafter, we propose our taxonomy for visual analytics in the urban area and illustrate the taxonomy. The taxonomy provides four levels for visual analytics on urban data from a new perspective based on the four stages in data mining.Four levels from our taxonomy include: descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. Finally, we conclude this survey by discussing the limitations of the existing related works and the challenges to visual analytics in the urban area.data mining, urban data, visual analytics, visualization | INTRODUCTIONRecently, the demand for building smart cities has been accelerated by the desire for high-quality daily lives for human beings and technological development. However, urbanization causes many human behaviours in the urban area and generates multi-source heterogeneous urban data.Meanwhile, an increase in the population can also lead to many severe problems, including traffic congestion, unbalanced distribution of urban hotspots, air pollution and many other domains. Paulos and Goodman (2004) first proposed the term 'urban computing', and many researchers have moved into this area so far. For example, in previous research, people can use the GPS trajectory data collected from vehicles to analyse traffic conditions (Xin et al., 2011) and keep safe traffic control (Abbasi et al., 2020). Furthermore, urban planners adopt cell phone data (Cho et al., 2011) collected from the base stations to explore the human mobility problem and optimize the location of urban hotspots. They also use air quality data collected from many sensors located in an urban area to analyse the air quality (Deng et al., 2019;Harbola et al., 2021). Moreover, such data can also be used on public health analysis (Antweiler et al., 2021), transit route planning, crime analysis (Svicarovic et al., 2021) and decision-making for emergency response (Johnson & Jankun-Kelly, 2021). This is beneficial to the development of a modern city and raises the happiness quotient.

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