Introduction
Technology is one of the pillars of contemporary society, ensuring its stable development and growth. Thus, the scope of innovation becomes critical nowadays, and many outdated activities are replaced with new ones that ensure better outcomes while the money and effort remain the same. Computing is one example of how innovation plays a more significant role across industries. The ability to process enormous amounts of data quickly and deliver structured, accurate results is critical across multiple spheres.
Furthermore, innovations such as the Internet of Things (IoT), cloud storage, artificial intelligence (AI), and machine learning (ML) are potent facilitators of change and further digitalization of society. They also help address challenges that might limit community development. Thus, smart, green, and integrated transport is one of the Grand Social Challenges that can be resolved using new approaches and pervasive computing.
Overview of the Social Challenge and Pervasive Computing
The problem of public transport has always been a critical concern for developed societies. The growth of cities, rapid urbanization, and the growing demand for transportation services are creating new challenges. For instance, the current task for European transport systems is to create a resource-efficient, climate- and environmentally friendly, safe, and accessible framework that benefits all citizens and meets their needs [1].
However, environmental pollution and changing demographic needs make the challenge more complex. Achieving high sustainability levels and resource efficiency is difficult as it requires advances in science and new developments [1]. For this reason, there is a search for new, more effective solutions, driven by innovation and creativity. The challenge of smart, green, and integrated transport might also be addressed through computer technology.
Thus, the extensive use of technology to address current challenges is a modern trend. Pervasive computing is the use of devices across various spheres and their interactions, making the technology omnipresent [2]. The advantages of the given technology include higher data exchange speed, the ability to resolve multiple problems simultaneously, and enhanced cooperation among various parties [2].
For this reason, using the approach to address the social challenge of transportation might be beneficial. It would help to design smart transport systems, enhance planning, reduce the number of errors caused by human factors, and establish the basis for future development [2]. For this reason, there is a need for a deeper understanding of the possible solutions and approaches for integrating technology into this sphere.
Literature Review
The existing literature on the topic evidences the efficacy of computing technology in resolving the transport problem. For instance, there are efforts to create smart transportation networks that will improve safety and reduce time and costs. Therefore, the IoT provides drivers in a smart city with better opportunities for traffic management, enhanced logistics, and effective parking systems [3]. Online coordination and the consideration of various aspects would minimize the risks of accidents and traffic jams [3].
Furthermore, a set of traffic models helps analyze traffic problems and reduce environmental pollution by managing traffic congestion [4]. This means that the concept of a smart city is the central instrument in transforming living environments and facing social challenges [5]. It helps increase operational efficiency, share information with the public and potential participants, and avoid knowledge gaps related to the current situation on the streets [5]. As a result, pervasive computing, which underpins smart transportation and related technologies, has become one of the most promising approaches to addressing the problem.
Current Examples and Future Prospects
Currently, several examples demonstrate how pervasive computing is used to address societal challenges. For instance, the concept of smart pedestrian safety involves using AI and computing to predict changes in public transport and to create more detailed pedestrian and cycle plans [6]. The incentive was launched in Portland, Oregon, with the local authority partnering with a startup called Rapid Flow [6].
The system uses AI to reduce pedestrian accidents by automatically optimizing traffic conditions [6]. The intelligent vehicles connected to the system interact with public transport and passengers to minimize the risk of accidents and increase safety [6]. The AI-powered system helps track data changes in real time and avoid new crashes caused by a poor understanding of transport movements.
Furthermore, there are future projects focused on addressing current challenges and building safe, smart, and effective transport systems. For instance, electric scooters are becoming one of the most popular types of transport, especially in big cities [7]. At the same time, the number of crashes involving these vehicles remains high. For this reason, there is an incentive to implement an AI safety monitoring system based on the number of riders and road types [7].
Smartphones, as collection terminals, gather and process data on a scooter’s movement, location, and start line. As a result, it is possible to reduce the number of accidents and crashes [7]. Furthermore, the application will control the scooter’s movement sideways, which is critical for safety [7]. In this way, the planned intervention will benefit the transportation systems in a smart city.
Challenges and Risks
However, the successful integration of computing across various spheres is possible only if specific risks and challenges are taken into account. First of all, safety is the primary concern when thinking about smart transport and the omnipresent nature of innovation [8]. Individuals may want to avoid sharing their position and location, as this violates their human rights.
Second, most pervasive computing systems require nonstandard components, which can lead to high costs and the need for prototypes [9]. It can significantly slow the pace of technology integration and lead to failure. Furthermore, edge servers and fog computing systems might lack physical protection, making them vulnerable to attacks [10]. This means there are still some issues to consider to ensure no significant data leaks occur.
Conclusion
The existing research shows that pervasive computing is one of the most important technologies of the modern age. It helps address numerous global challenges. For instance, for smart, green, and integrated transport, innovation might be highly beneficial. It would help improve planning, traffic schemes, and movement, and avoid congestion and higher rates of environmental harm. Existing cases of using technology show that, by leveraging AI, enhanced communication, and interaction, it is possible to achieve significant success in addressing the problematic issue.
References
[1] European Commission. “SOCIETAL CHALLENGES – Smart, green, and integrated transport,” Cordis, 2014.
[2] A. Narayanan et al., “Key advances in pervasive edge computing for industrial Internet of Things in 5G and beyond,” in IEEE Access, vol. 8, pp. 206734-206754, 2020.
[3] D. Oladimeji et al., “Smart transportation: An overview of Technologies and Applications,” in Sensors, vol. 23, no. 8, pp. 3880, 2023.
[4] A. Feizi, et al., “A pervasive framework toward sustainability and smart-growth: Assessing multifaceted transportation performance measures for smart cities,” in Journal of Transport & Health, vol. 19, 2020.
[5] C. Can and D. Zhigang, “The effect of pervasive computing and driver’s memory on connected and autonomous vehicles,” in IEEE Access, vol. 11, pp. 45774-45781, 2023.
[6] NEC. “5 examples of smart city transportation solutions,” nec.nz, 2022.
[7] W. Jang et al. “An AI safety monitoring system for electric scooters based on the number of riders and road types,” in Sensors, vol. 23, no. 22, 2023.
[8] A. Strohmayer and R. Bellini “Safety as a grand challenge in pervasive computing: Using feminist epistemologies to shift the paradigm from security to safety,” in IEEE Pervasive Computing, vol. 21, pp. 61-69, 2022.
[9] E. Peltonen et al., “Perspectives on negative research results in pervasive computing,” in IEEE Pervasive Computing, vol. 22, no. 3, pp. 63-72, 2023.
[10] M. A. Aleisa, A. Abuhussein and F. T. Sheldon, “Access control in fog computing: Challenges and research agenda,” in IEEE Access, vol. 8, pp. 83986-83999, 2020.