Abstract
Abstract. Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs); moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM) and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.
Highlights
Nowadays, transportation is a subject that people are associated with it
For the dataset used in this study, the Fuzzy Subtractive algorithm provided better results than those achieved by Fuzzy C-Means (FCM) algorithm by about 1.50% progressive
Traffic monitoring and management in urban intelligent transportation systems can be well carried out using vehicular sensor networks
Summary
By the development of cities, the need for public services and facilities has been increased This issue draws attentions to the subjects such as urban transportation (Seredynski and Bouvry, 2011). Due to the use of wireless sensor networks to develop traffic information systems, paying attention to the general aspects of these networks is required. Wireless sensor networks consist of hundreds of thousands of sensor nodes that are responsible to collect important data including temperature, location, and etc. These networks are used in different fields such as health monitoring, military applications, and etc. They investigate various clustering algorithms, pointed out each one’s goals, characteristics, advantages, and limitations
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