TMSA: Participatory Sensing Based on Mobile Phones in Urban Spaces

Abstract

A design for a novel mobile sensing system, called Temperature Measurement System Architecture (TMSA), that uses people as mobile sensing nodes in a network to capture spatiotemporal properties of pedestrians in urban environments is presented in this paper. In this dynamic, microservices approach, real-time data and an open-source IoT platform are combined to provide weather conditions based on information generated by a fleet of mobile sensing platforms. TMSA also offers several advantages over traditional methods using participatory sensing or more recently crowd-sourced data from mobile devices, as it provides a framework in which citizens can bring to light data relevant to urban planning services or learn human behaviour patterns, aiming to change users' attitudes or behaviors through social influence. In this paper, we motivate the need for and demonstrate the potential of such a sensing paradigm, which supports a host of new research and application developments, and illustrate this with a practical urban sensing example.

[Retracted] Urban Road Landscape Design and Digital Twin Simulation Modeling Analysis

“…It describes how to design, develop, and test an autonomously driven truck and a virtual simulation environment (VSE) in a distribution center [11]. is research focuses on traffic flow optimization using self-organizing microlevel control and reinforcement learning [12]. Long-range navigation in urban areas using learning and rule-based agents, in which vehicles represented by agents change their decision-making for rerouting depending on local environmental sensors [13].…”

“…Long-range navigation in urban areas using learning and rule-based agents, in which vehicles represented by agents change their decision-making for rerouting depending on local environmental sensors [13]. e temperature measurement system architecture (TMSA) is a design for a unique mobile sensing system that leverages individuals as mobile sensing nodes in a network to gather spatiotemporal features of pedestrians in urban areas [12].…”