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

Fog computing-based approximate spatial keyword queries with numeric attributes in IoV

Due to the popularity of on-board geographic devices, a large number of spatial-textual objects are generated in Internet of Vehicles (IoV). This development calls for Approximate Spatial Keyword Queries with numeric Attributes in IoV (ASKIV), which takes into account the locations, textual descriptions, and numeric attributes of spatial-textual objects. Considering huge amounts of objects involved in the query processing, this paper comes up with the ideal of utilizing vehicles as fog-computing resource, and proposes the network structure called FCV, and based on which the fog-based Top-k ASKIV query is explored and formulated. In order to effectively support network distance pruning, textual semantic pruning, and numerical attribute pruning simultaneously, a two-level spatial-textual hybrid index STAG-tree is designed. Based on STAG-tree, an efficient Top-k ASKIV query processing algorithm is presented. Simulation results show that, our STAG-based approach is about 1.87x (17.1x, resp.) faster in search time than the compared ILM (DBM, resp.) method, and our approach is scalable.University of Derb

Digital Twins for Logistics and Supply Chain Systems: Literature Review, Conceptual Framework, Research Potential, and Practical Challenges

To facilitate an effective, efficient, transparent, and timely decision-making process as well as to provide guidelines for industry planning and public policy development, a conceptual framework of digital twins (DTs) for logistics and supply chain systems (LSCS) is needed. This paper first introduces the background of the logistics and supply chain industry, the DT and its potential benefits, and the motivations and scope of this research. The literature review indicates research and practice gaps and needs that motivate proposing a new conceptual DT framework for LSCS. As each element of the new framework has different requirements and goals, it initiates new research opportunities and creates practical implementation challenges. As such, the future of DT computation involves advanced analytics and modeling techniques to address the new agenda's requirements. Finally, ideas on the next steps to deploy a transparent, trustworthy, and resilient DT for LSCS are presented.Comment: 45 page

Human-Intelligence and Machine-Intelligence Decision Governance Formal Ontology

Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational systems including healthcare and medical diagnosis, automated stock trading, robotic production, telecommunications, space explorations, and homeland security. Self-driving cars and drones are just the latest extensions of AI. This thrust of AI into organizations and daily life rests on the AI community’s unstated assumption of its ability to completely replicate human learning and intelligence in AI. Unfortunately, even today the AI community is not close to completely coding and emulating human intelligence into machines. Despite the revolution of digital and technology in the applications level, there has been little to no research in addressing the question of decision making governance in human-intelligent and machine-intelligent (HI-MI) systems. There also exists no foundational, core reference, or domain ontologies for HI-MI decision governance systems. Further, in absence of an expert reference base or body of knowledge (BoK) integrated with an ontological framework, decision makers must rely on best practices or standards that differ from organization to organization and government to government, contributing to systems failure in complex mission critical situations. It is still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence and machine-intelligence (HI-MI) decision capacity is required in any given decision situation. To address this deficiency, this research establishes a formal, top level foundational ontology of HI-MI decision governance in parallel with a grounded theory based body of knowledge which forms the theoretical foundation of a systemic HI-MI decision governance framework

The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

Waste Management in the Smart City: Current Practices and Future Directions

The discourse surrounding sustainability, particularly in the urban environment, has gained considerable momentum in recent years. The concept of a smart city epitomises the integration of innovative technological solutions with community-centred approaches, thereby laying the ground- work for a sustainable lifestyle. One of the crucial components of this integration is the effective and innovative management of waste. The aim of this article was to classify scientific research pertaining to waste management within the context of smart city issues, and to identify emerging directions for future research. A systematic literature review, based on a bibliometric analysis of articles included in the Scopus and Web of Science databases, was conducted for this study. The purpose of such a systematic review is to identify, integrate, and evaluate research on a selected topic, using clearly defined criteria. The research query included: TITLE-ABS-KEY (“smart city” AND (waste OR garbage OR trash OR rubbish)) in the case of Scopus, and TS = (“smart city” AND (waste OR garbage OR trash OR rubbish)) in the case of the Web of Science database. A total of 1768 publication records qualified for the analysis. This study presents an investigation into the current and forthcoming directions of waste management in smart cities, synthesising the latest advancements and methods. The findings outline specific future research directions encompassing technological advancement, special waste challenges, digitisation, energy recovery, transportation, community engagement, pol- icy development, security, novel frameworks, economic and environmental impact assessment, and global implications. These insights reflect a multifaceted approach, advocating a technology-driven perspective that is integral to urban sustainability and quality of life. The study’s findings provide practical avenues for cities to enhance waste management through modern technologies, promoting efficient systems and contributing to sustainable urban living and the circular economy. The insights are vital for policymakers and industry leaders globally, supporting the creation of universal stan- dards and policies, thereby fostering comprehensive waste management systems aligned with global sustainability objectives

Human-machine interaction towards Industry 5.0: Human-centric smart manufacturing

Since the concept of Industry 5.0 was proposed, the emphasis on human–machine​ interaction (HMI) in industrial scenarios has continued to increase. HMI is part of the factory’s development towards Industry 5.0, mainly because HMI can help realise the human-centric vision. At the same time, to achieve the sustainable and resilient goals proposed by Industry 5.0, green, smart, and more advanced technologies are also considered important driving factors for factories to achieve Industry 5.0. Human-centric smart manufacturing (HCSM) factories that integrate HMI with advanced technologies are expected to become the paradigm of future manufacturing. Therefore, it is necessary to discuss technologies and research directions that may promote the implementation of HCSM in the future. In a smart factory, HMI signals will go through the process of being collected by sensors, processed, transmitted to the data analysis centre and output to complete the interaction. Based on this process, we divide HMI into four parts: sensor and hardware, data processing, transmission mechanism, and interaction and collaboration. Through a systematic literature review process, this article evaluates and summarises the current research and technologies in the HMI field and categorises them into four parts of the HMI process. Since the current usage scenarios of some technologies are relatively limited, the introduction focuses on the possible applications and problems they face. Finally, the opportunities and challenges of HMI for Industry 5.0 and HCSM are revealed and discussed