Clustering spatio-temporal bi-partite graphs for finding crowdsourcing communities in IoMT networks

Abstract

ABSTRACT The Internet of Moving Things is rapidly becoming a reality where intelligent devices and infrastructures are fostering real-time data sustainability in smart cities and advancing crowdsourced tasks to improve energy consumption, waste management, and traffic operations. These intelligent devices create a complex network scenario in which they often move together or in conjunction with one another to complete crowdsourced tasks. Our research premise is that mobility relationships matter when performing these tasks, and therefore, a graph model based on representing the changes in mobility relationships is needed to help identify the neighbour devices that are moving close to one another in our physical world but also seamlessly connected in their virtual world. We propose a bi-partite community mobility graph model for linking intelligent devices in both virtual and physical worlds, as well as reaching a trade-off between crowdsourced tasks designed with explicit and implicit citizen participation. This paper aims to explore a bi-partite graph as a promising spatio-temporal representation of IoMT networks since changes in mobility relationships over time can indicate volunteer organisation at the device and community levels. The Louvain community detection method is proposed to find communities of intelligent devices to reveal a value conscious participation of citizens. The proposed bi-partite graph model is evaluated using a real-world scenario in transportation, confirming the main role of evolving communities in developing crowdsourcing IoMT networks.