Data analysis and information security of an Internet of Things (IoT) intelligent transit system

Public transportation around midsize educational cities has become increasingly vital as residential and commuter populations continue to grow every year. Our research team proposes a cyber-physical system that monitors the quality of service of the transit bus system around James Madison University (JMU), located in Harrisonburg, Virginia, USA. By utilizing the power of Internet of Things (IoT) devices, such as Raspberry Pi computing devices, it is possible to create a network of smart nodes that collect data on the bus routing efficiency and ridership. Using the collected data and using big data analysis, improvements can be made to bus route efficiency and traffic congestion in Harrisonburg, as well as similar college towns. This paper presents data analysis for the first deployment of the IoT nodes at seven JMU bus shelters, during the Spring 2017. Data stations compile and submit the Media Access Control (MAC) addresses and timestamps of wireless devices surrounding the bus stations. This information is stored in a Cloud Storage database that allows for big data analysis and convenient access. In this paper we present the usage of the data collected, modifications to improve both the transit system as well as the data collection methods for future node deployments. Our results show the average passenger waiting times at a sample of the seven bus stations. Route optimization and dynamic changes for the route are discussed. Also, we discuss several concerns on security and privacy related to the collection, transmission, and storage of data in the Cloud (e.g., privacy of the ridership MAC addresses, tracking of a specific bus rider in the system ...etc.). Additional security implementation has been suggested to emphasize security and privacy protection.