Walk this way! An IoT-based urban routing system for smart cities

Pimpinella, Andrea; Redondi, Alessandro E. C.; Cesana, Matteo

doi:10.1016/j.comnet.2019.07.013

Cited by 11 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A thorough review of the selected articles revealed that digital technologies, including artificial intelligence, big data, IoT, social media analytics, cloud computing, and mobile technologies, cause transformations in the environmental sustainability domains (Tables 1-4). We observed that digital technologies enable transformations in different areas of environmental sustainability, such as pollution control [51][52][53][54][55], waste management [27,[56][57][58][59][60][61], sustainable production [62][63][64][65][66][67][68], and urban sustainability [69][70][71][72][73][74]. All these studies show how digital technologies are transforming the different aspects of environmental sustainability.…”

Section: Resultsmentioning

confidence: 65%

Digital Transformation and Environmental Sustainability: A Review and Research Agenda

2021

View full text Add to dashboard Cite

Digital transformation refers to the unprecedented disruptions in society, industry, and organizations stimulated by advances in digital technologies such as artificial intelligence, big data analytics, cloud computing, and the Internet of Things (IoT). Presently, there is a lack of studies to map digital transformation in the environmental sustainability domain. This paper identifies the disruptions driven by digital transformation in the environmental sustainability domain through a systematic literature review. The results present a framework that outlines the transformations in four key areas: pollution control, waste management, sustainable production, and urban sustainability. The transformations in each key area are divided into further sub-categories. This study proposes an agenda for future research in terms of organizational capabilities, performance, and digital transformation strategy regarding environmental sustainability.

show abstract

Section: Resultsmentioning

confidence: 65%

Digital Transformation and Environmental Sustainability: A Review and Research Agenda

2021

View full text Add to dashboard Cite

show abstract

“…Not only that, IoT and navigation in the smart city have been utilized for applications such as finding the paths where the cyclist is trying to find clean air areas based on aggregated data of air quality and providing this information to the user [22]. The pedestrians in a smart city can also benefit from the sensors distributed with user-defined functions that aim to reduce the exposure to pollution and get the low average temperature routes for the user [23]. An example that has been widely adopted starting from 2020 is applying biometric sensing machines that utilize computer vision to identify symptomatic people and prevent them from getting to crowded public places and spreading the virus [24].…”

Section: Related Workmentioning

confidence: 99%

An End-to-End Smart IoT-Driven Navigation for Social Distancing Enforcement

et al. 2022

View full text Add to dashboard Cite

The unprecedented global spread of the coronavirus pandemic COVID-19 has significantly promoted novel Internet-of-things (IoT)-based solutions to prevent, combat, monitor, or predict virus spread in the population. The proliferation of these technologies has fostered their utilization for different practical use-cases to offer reinforced control, discipline, and safety. This paper proposes an end-to-end smart navigation framework that uses Social IoT (SIoT) and Artificial Intelligence (AI) techniques to ensure pedestrians' navigation safety through a given geographical area. The aim is to mitigate the risks of exposure to the virus and impose social distancing practices while avoiding high-risk areas identified from the SIoT data. First, we create weighted graphs representing the social relations connecting the different IoT devices in the area of interest. Second, we regroup the devices into communities according to their SIoT relations that consider their locations and owners' friendship levels. Next, we extract CCTV recorded videos to estimate the level of social distancing practice on different roads using a computer vision model. Accordingly, the road segments are assigned weights representing their safety levels based on the extracted data. Afterward, a graph-based routing algorithm is executed to recommend the route to follow while achieving a trade-off between speed and safety. Finally, the proposed framework is generalized to enable multi-user coordinated navigation. The feasibility of the proposed approach on real-world maps and IoT datasets is corroborated in our simulation results showing an ability to balance safety and travel distance, which can be adjusted according to the user's preferences.

show abstract

“…Real-time Navigation: For modern smart cities, interconnected IoT devices can be exploited for a variety of navigation problems such as autonomous unmanned vehicle navigation and pedestrian routing [64]. Several applications can be considered in this context including pedestrians and cyclists in path segments with better air quality [65]- [67] or employing the IoT data to assist in practicing social distancing and avoiding crowded areas based on mobile IoT devices [68], [69]. Social network information can also help avoid contact with friends during an outbreak such as COVID-19 [70].…”

Section: Practical Applicationsmentioning

confidence: 99%

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

et al. 2022

View full text Add to dashboard Cite

Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises compatibility and trustworthiness challenges, hindering conventional service discovery and network navigability processes. The IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices' status, such as battery capacity and computational power); traditional methods fail to learn and understand the evolving behavior of the network to enable real-time and contextaware service discovery in such diverse and large-scale topologies of IoT networks. The Social IoT (SIoT) concept, which defines the relationships among the connected objects, can be exploited to extract established relationships between devices and enable trustworthy and context-aware services. In fact, SIoT expresses the possible connections that devices can establish in the network and reflect compatibility, trustworthiness, and so on. In this paper, we investigate the service discovery process in SIoT networks by proposing a lowcomplexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery. Unlike the conventional graph-based techniques, the proposed approach simultaneously embeds the devices' features and their SIoT relations. Our simulations on a real-world IoT dataset show that the proposed GNN-based approach can provide more concise clusters compared to traditional techniques, namely the Louvain and Leiden algorithms. This allows a better IoT network learning and understanding and also, speeds up the service lookup search space. Finally, we discuss implementing the GNN-assisted context-service discovery processes in novel smart city IoT-enabled applications.

show abstract

Walk this way! An IoT-based urban routing system for smart cities

Cited by 11 publications

References 12 publications

Digital Transformation and Environmental Sustainability: A Review and Research Agenda

Digital Transformation and Environmental Sustainability: A Review and Research Agenda

An End-to-End Smart IoT-Driven Navigation for Social Distancing Enforcement

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

Contact Info

Product

Resources

About