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Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

The food supply chain has faced many challenges due to its complex and complicated nature. Blockchain technology is one of the mechanisms used to improve agri-food supply chain processes by evolving organization capabilities. A study is being conducted to scrutinize the adoption of blockchain technology in the agri-food supply chain through the lens of the operational capability approach. It further makes an attempt to identify the capabilities of blockchain to improve supply chain processes.

The qualitative research method with semi-structured interviews was used to gather information from experts and professionals in the food supply chain and blockchain technology. The authors have adopted a systematic approach of coding using open, axial and selective methods to depict and identify the themes that represent the blockchain-enabled agri-food supply chain. The data were collected from 32 interviews of selected participants.

The result shows five critical areas where blockchain can come up to enhance the agri-food supply chain performance by providing traceability, transparency, information security, transactions, and trust and quality. Further, the study reveals that blockchain will provide safety, lower the cost of transactions and can create trust among users to communicate within the whole supply chain without the intervention of a third party. This study demonstrated that the capabilities need to be considered when introducing technology into the practice.

The study implies thought-provoking implications for bridging the theory-practice gap by examining the empirical data to demonstrate how the operational capabilities of blockchain technology further strengthen the agri-food supply chain. Additionally, this study provides some suggestions for utilizing the results and proposes a framework to understand more about blockchain use cases in the agri-food supply chain as well as extend the application of blockchain using an operational capability approach for future academic researchers in this area.

This study presented some more important managerial implications which reveal that the majority of organisations were in the initial stages of adoption process of blockchain technology. Further, the positive influence of managers and IT experts can help the information technology companies (IT) and stakeholders for developing and promoting blockchain solutions in the agri-food supply chain. The important implication of blockchain enabled agri-food supply chain is to maintain information security and incresae supply chain performance.

The study shows the operational capabilities of agri-food supply chain using blockchain technology. Blockchain can contribute in enhancing the agri-food supply chain to increase traceability and transparency and helps to reduce the risk of disruptions.

This paper aims to review in-memory computing (IMC) for machine learning (ML) applications from history, architectures and options aspects. In this review, the authors investigate different architectural aspects and collect and provide our comparative evaluations.

Collecting over 40 IMC papers related to hardware design and optimization techniques of recent years, then classify them into three optimization option categories: optimization through graphic processing unit (GPU), optimization through reduced precision and optimization through hardware accelerator. Then, the authors brief those techniques in aspects such as what kind of data set it applied, how it is designed and what is the contribution of this design.

ML algorithms are potent tools accommodated on IMC architecture. Although general-purpose hardware (central processing units and GPUs) can supply explicit solutions, their energy efficiencies have limitations because of their excessive flexibility support. On the other hand, hardware accelerators (field programmable gate arrays and application-specific integrated circuits) win on the energy efficiency aspect, but individual accelerator often adapts exclusively to ax single ML approach (family). From a long hardware evolution perspective, hardware/software collaboration heterogeneity design from hybrid platforms is an option for the researcher.

IMC’s optimization enables high-speed processing, increases performance and analyzes massive volumes of data in real-time. This work reviews IMC and its evolution. Then, the authors categorize three optimization paths for the IMC architecture to improve performance metrics.

Banking traces back to 2000 BC in Assyria, India and Sumeria. Merchants used to give grain loans to farmers and traders to carry goods between cities. In ancient Greece and Roman Empire, lenders in temples, provided loans, and accepted deposits while performed change of money. The archaeological evidence uncovered in India and China corroborates this. The major development in banking came predominantly in the mediaeval, Renaissance Italy, with the major cities Florence, Venice and Genoa being the financial centres. Technology has become an inherent and integral part of our lives. We are generating a huge amount of data in transfer, storage and usage, with greater demands of ubiquitous accessibility, inducing an enormous impact on industry and society. With the emergence of smarter cities and societies, the security challenges pertinent to data become greater, impending impact on the consumer protection and security. The aim of this chapter is to highlight if SSI and passwordless authentication using FIDO-2 protocol assuage security concerns such as authentication and authorisation while preserving the individual's privacy.

The purpose of this study is to increase awareness of current supply chain (SC) security-related issues by providing an extensive analysis of existing SC security solutions and their limitations. The security of SCs has received increasing attention from researchers, due to the emerging risks associated with their distributed nature. The increase in risk in SCs comes from threats that are inherently similar regardless of the type of SC, thus, requiring similar defence mechanisms. Being able to identify the types of threats will help developers to build effective defences.

In this work, we provide an analysis of the threats, possible attacks and traceability solutions for SCs, and highlight outstanding problems. Through a comprehensive literature review (2015–2021), we analysed various SC security solutions, focussing on tracking solutions. In particular, we focus on three types of SCs: digital, food and pharmaceutical that are considered prime targets for cyberattacks. We introduce a systematic categorization of threats and discuss emerging solutions for prevention and mitigation.

Our study shows that the current traceability solutions for SC systems do not offer a broadened security analysis and fail to provide extensive protection against cyberattacks. Furthermore, global SCs face common challenges, as there are still unresolved issues, especially those related to the increasing SC complexity and interconnectivity, where cyberattacks are spread across suppliers.

This is the first time that a systematic categorization of general threats for SC is made based on an existing threat model for hardware SC.

This paper examines why individuals start their hospitality careers by becoming temporary lifeguards while aspiring to later promotion. It reports data from young people working in one major upmarket hotel chain that operates in the Gulf but has a global reach. This study aims to address issues regarding this often-overlooked career path for young staff.

A qualitative questionnaire was distributed to lifeguards working in a luxury hotel chain in a Gulf country to explore their reasons for working as a lifeguard and their experiences in this role. Data were analysed using QDA Miner and WordStat to generate coherence and similarity indices.

It is found that the attractions include good training with a well-established company operating in an upmarket location, but other important factors include career prospects, skill enhancement, self-development, monetary savings and experience working in one of the world’s most exciting tourist locations.

The results offer implications for management looking to recruit expatriate labour without considering long-term employment or residence. Nonetheless, while this offers opportunities to assess many potential long-term employees, there are costs to the practice.

Little research has been conducted on lifeguards and how employment in such roles can develop careers in hospitality. The study contributes to understanding motives and career development and conceptually suggests that liminal status complements those drawn to protean career development. The results shed light on how new employees, including management trainees, undertake liminal, protean careers, to care for family, develop their careers and enter markets that may otherwise be difficult to enter.

The years following the 9/11/2001 terrorists attacks saw a marked increase in community and hospital emergency preparedness, from communications across community networks, development of policies and procedures, to attainment and training in the use of biological warfare resources. Regular drills ensured emergency and health care personnel were trained and prepared to address the next large-scale crisis, especially from terrorist and bioterrorist attacks. This chapter looks at some of the more familiar global health issues over the past two decades and the lessons learned from hospital responses to inform hospital management in preparation for future incidents.

This study is a narrative review of the literature related to lessons learned from four major events in the time period from 2002 to 2023 – SARS, MERS, Ebola, and COVID-19.

The initial search yielded 25,913 articles; 57 articles were selected for inclusion in the study.

Comparison of key issues and lessons learned among the four major events described in this article – SARS, MERS, Ebola, and COVID-19 – highlight that several lessons are “relearned” with each event. Other key issues, such as supply shortages, staffing availability, and hospital capacity to simultaneously provide care to noninfectious patients came to the forefront during the COVID-19 pandemic. A primary, ongoing concern for hospitals is how to maintain their preparedness given competing priorities, resources, and staff time. This concern remains post-COVID-19.

Privacy is a sensitive issue in business because it involves how a platform uses consumer personal data. In terms of consumer rights, personal information needs to be protected in the privacy policy (PP). This study describes several aspects of the PP that consumers need to pay attention to, especially points prone to misuse of personal information.

This research used a taxonomy of consumer privacy concerns in e-commerce to reveal general and specific privacy concerns. The privacy calculus theory was also applied to explore consumer rationalization using (1) consumer knowledge about PP, (2) subjective perception, and (3) proximity to the PP features. Furthermore, the netnographic approach was used to combine the interrelation between technology and social construction. A sample of 378 young consumers in several major cities in Indonesia participated online and offline. Semi-structured interviews were also conducted to gain more in-depth comprehension.

The results showed that most young consumers have sufficient basic knowledge of the important points of PP. Furthermore, they tend not to read the PP because it is long and cumbersome, and therefore do not wish to expend much cognitive effort on it.

This study provides several results that can be utilized by policymakers or e-commerce companies to pay more attention to PPs for young groups. In addition, e-commerce companies can increase the knowledge of the privacy situation of Internet users in general.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0740

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.