Sohini Roy

Sean Elliott, Sohini Roy

Public transit is a critical component of urban mobility and equity, yet mobility and air-quality linkages are rarely operationalized in reproducible smart-city analytics workflows. This study develops a transparent, multi-source monitoring dataset that integrates agency-reported transit ridership with ambient fine particulate matter PM2.5 from the U.S. EPA Air Quality System (AQS) for four U.S. metropolitan areas - New York City, Chicago, Las Vegas, and Phoenix, using two seasonal snapshots (March and October 2024). We harmonize heterogeneous ridership feeds (daily and stop-level) to monthly system totals and pair them with monthly mean PM2.5 , reporting both absolute and per-capita metrics to enable cross-city comparability. Results show pronounced structural differences in transit scale and intensity, with consistent seasonal shifts in both ridership and PM2.5 that vary by urban context. A set of lightweight regression specifications is used as a descriptive sensitivity analysis, indicating that apparent mobility-PM2.5 relationships are not uniform across cities or seasons and are strongly shaped by baseline city effects. Overall, the paper positions integrated mobility and environment monitoring as a practical smart-city capability, offering a scalable framework for tracking infrastructure utilization alongside exposure-relevant air-quality indicators to support sustainable communities and public-health-aware urban resilience.

Sohini Roy, Arunabha Sen

Smart grid monitoring, automation and control will completely rely on PMU based sensor data soon. Accordingly, a high throughput, low latency Information and Communication Technology (ICT) infrastructure should be opted in this regard. Due to the low cost, low power profile, dynamic nature, improved accuracy and scalability, wireless sensor networks (WSNs) can be a good choice. Yet, the efficiency of a WSN depends a lot on the network design and the routing technique. In this paper a new design of the ICT network for smart grid using WSN is proposed. In order to understand the interactions between different entities, detect their operational levels, design the routing scheme and identify false data injection by particular ICT entities, a new model of interdependency called the Multi State Implicative Interdependency Model (MSIIM) is proposed in this paper, which is an updated version of the Modified Implicative Interdependency Model (MIIM) [1]. MSIIM considers the data dependency and operational accuracy of entities together with structural and functional dependencies between them. A multi-path secure routing technique is also proposed in this paper which relies on the MSIIM model for its functioning. Simulation results prove that MSIIM based False Data Injection (FDI) detection and mitigation works better and faster than existing methods.