A vehicular network–based intelligent transport system for smart cities. (November 2019)

• Record Type: Journal Article
• Authors: Zaheer, Tayyaba; Malik, Asad Waqar; Rahman, Anis Ur; Zahir, Ayesha; Fraz, Muhammad Moazam
• Is Part Of: International journal of distributed sensor networks. Volume 15:Number 11(2019)
• Publication Date: 2019-11
• Subjects: Internet of Things -- intelligent transport system -- route selection -- vehicular ad hoc networks; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147719888845
• Languages: English
• ISSNs: 1550-1329

Abstract

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. In this article, we propose an intelligent vehicular network framework for smart cities that enables route selection based on real-time data received from neighboring vehicles in an ad hoc fashion. We used Wi-Fi Direct–enabled Android-based smartphones as embedded devices in vehicles. We used a vehicular ad hoc network to implement an intelligent transportation system. Data gathering and preprocessing were carried on different routes between two metropolitan cities of a developing country. The framework was evaluated on different fixed route-selection and dynamic route-selection algorithms in terms of resource usage, transmission delay, packet loss, and overall travel time. Our results show reduced travel times of up to 33.3% when compared to a traditional fixed route-selection algorithm.