Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encour-aging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility‐as‐a‐service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope

Federated blockchain-based tracking and liability attribution framework for employees and cyber-physical objects in a smart workplace

The systematic integration of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) into the supply chain to increase operational efficiency and quality has also introduced new complexities to the threat landscape. The myriad of sensors could increase data collection capabilities for businesses to facilitate process automation aided by Artificial Intelligence (AI) but without adopting an appropriate Security-by-Design framework, threat detection and response are destined to fail. The emerging concept of Smart Workplace incorporates many CPS (e.g. Robots and Drones) to execute tasks alongside Employees both of which can be exploited as Insider Threats. We introduce and discuss forensic-readiness, liability attribution and the ability to track moving Smart SPS Objects to support modern Digital Forensics and Incident Response (DFIR) within a defence-in-depth strategy. We present a framework to facilitate the tracking of object behaviour within Smart Controlled Business Environments (SCBE) to support resilience by enabling proactive insider threat detection. Several components of the framework were piloted in a company to discuss a real-life case study and demonstrate anomaly detection and the emerging of behavioural patterns according to objects' movement with relation to their job role, workspace position and nearest entry or exit. The empirical data was collected from a Bluetooth-based Proximity Monitoring Solution. Furthermore, a key strength of the framework is a federated Blockchain (BC) model to achieve forensic-readiness by establishing a digital Chain-of-Custody (CoC) and a collaborative environment for CPS to qualify as Digital Witnesses (DW) to support post-incident investigations

A Systematic Literature Review on Automotive Digital Forensics: Challenges, Technical Solutions and Data Collection

A modern vehicle has a complex internal architecture and is wirelessly connected to the Internet, other vehicles, and the infrastructure. The risk of cyber attacks and other criminal incidents along with recent road accidents caused by autonomous vehicles calls for more research on automotive digital forensics. Failures in automated driving functions can be caused by hardware and software failures and cyber security issues. Thus, it is imperative to be able to determine and investigate the cause of these failures, something which requires trustable data. However, automotive digital forensics is a relatively new field for the automotive where most existing self-monitoring and diagnostic systems in vehicles only monitor safety-related events. To the best of our knowledge, our work is the first systematic literature review on the current research within this field. We identify and assess over 300 papers published between 2006 - 2021 and further map the relevant papers to different categories based on identified focus areas to give a comprehensive overview of the forensics field and the related research activities. Moreover, we identify forensically relevant data from the literature, link the data to categories, and further map them to required security properties and potential stakeholders. Our categorization makes it easy for practitioners and researchers to quickly find relevant work within a particular sub-field of digital forensics. We believe our contributions can guide digital forensic investigations in automotive and similar areas, such as cyber-physical systems and smart cities, facilitate further research, and serve as a guideline for engineers implementing forensics mechanisms

IoT, Intelligent Transport Systems and MaaS (Mobility as a Service)

IoT (Internet of Things) applications are crucial in Intelligent Transport Systems (ITS). MaaS (Mobility as a Service) is an advanced model of ITS in which public institutions, private operators and citizens are deeply connected since means of transport are virtualized in mobility resources and provided to users through the Internet. This contribution, after a short introduction, addresses legal concerns focusing on three aspects: (1) security of technological platforms and infrastructures, (2) protection of user\u2019s personal data, (3) communication among devices and in the IoT ecosystem

Co-creating a smart tourism local service system in rural areas: a case study from south

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe most recent trends show an increase in the urbanization of cities, and, consequently, inner territories become more depopulated, business activities get closed, services get reduced and the overall services become poor and not able to offer quality offers to visitors (Bolay, 2020). According to (United Nations, 2019), by 2050 more than three out of four people will be living in urban areas. Nowadays, many studies have addressed the evolution and features of Smart Cities (Van Dijk & Teuben, 2015) and tourism is also one of those spheres that got digitally transformed by Smart Cities (Khan, Woo, Nam, & Chathoth, 2017). One of the features of smart applications is the possibility to let the user be a driver of value in creating and sharing contents (Kontogianni & Alepis, 2020). However, the explosion of smart solutions enabled by the latest technological innovations has been mostly contextualized in urban environments while fewer solutions have been developed in less urbanized rural areas (Steyn & Johanson, 2010). The methodology used employs the merging of two of the core contemporary service research approaches: Service Science and Service-Dominant logic; the first offers an organizational framework to generate and integrate value co-creation in terms of a smart service systems (Polese, Botti, Grimaldi, Monta & Vesci, 2018). For the same purpose, but differently, the second proposes a different layout called service ecosystems (Vargo & Lusch, 2016). This combination of approaches overcomes individual model limitations by setting an integrated model that can be employed to hypercompetitive and experience-based sectors (Polese, Botti, Grimaldi, Monta & Vesci, 2018), and that was adopted by using a case study methodology, relying on semi-structured interviews

Linking circular economy and digitalisation technologies : A systematic literature review of past achievements and future promises

The circular economy (CE) has the potential to capitalise upon emerging digital technologies, such as big data, artificial intelligence (AI), blockchain and the Internet of things (IoT), amongst others. These digital technologies combined with business model innovation are deemed to provide solutions to myriad problems in the world, including those related to circular economy transformation. Given the societal and practical importance of CE and digitalisation, last decade has witnessed a significant increase in academic publication on these topics. Therefore, this study aims to capture the essence of the scholarly work at the intersection of the CE and digital technologies. A detailed analysis of the literature based on emerging themes was conducted with a focus on illuminating the path of CE implementation. The results reveal that IoT and AI play a key role in the transition towards the CE. A multitude of studies focus on barriers to digitalisation-led CE transition and highlight policy-related issues, the lack of predictability, psychological issues and information vulnerability as some important barriers. In addition, product-service system (PSS) has been acknowledged as an important business model innovation for achieving the digitalisation enabled CE. Through a detailed assessment of the existing literature, a viable systems-based framework for digitalisation enabled CE has been developed which show the literature linkages amongst the emerging research streams and provide novel insights regarding the realisation of CE benefits.© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed