Related papers: Exploring the sensing power of mixed vehicle fleet…
Drive-by sensing (i.e. vehicle-based mobile sensing) is an emerging data collection paradigm that leverages vehicle mobilities to scan a city at low costs. It represents a positive social externality of urban transport activities. Bus…
Vehicle-based mobile sensing (a.k.a drive-by sensing) is an important means of surveying urban environment by leveraging the mobility of public or private transport vehicles. Buses, for their extensive spatial coverage and reliable…
Pervasive and mobile sensing is an integral part of smart transport and smart city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is gaining popularity in both academic research and field practice. The DS paradigm has…
Urban sensing is essential for the development of smart cities, enabling monitoring, computing, and decision-making for urban management.Thanks to the advent of vehicle technologies, modern vehicles are transforming from solely mobility…
Sensors can measure air quality, traffic congestion, and other aspects of urban environments. The fine-grained diagnostic information they provide could help urban managers to monitor a city's health. Recently, a `drive-by' paradigm has…
Drive-by sensing is gaining popularity as an inexpensive way to perform fine-grained, city-scale, spatiotemporal monitoring of physical phenomena. Prior work explores several challenges in the design of low-cost sensors, the reliability of…
Drive-by sensing is a promising data collection paradigm that leverages the mobilities of vehicles to survey urban environments at low costs, contributing to the positive externality of urban transport activities. Focusing on e-hailing…
In recent years, the drive-by sensing paradigm has become increasingly popular for cost-effective monitoring of urban areas. Drive-by sensing is a form of crowdsensing wherein sensor-equipped vehicles (aka, mobile agents) are the primary…
Currently, fixed static sensing is a primary way to monitor environmental data like air quality in cities. However, to obtain a dense spatial coverage, a large number of static monitors are required, thereby making it a costly option. Dense…
A critical requirement for automated driving systems is enabling situational awareness in dynamically changing environments. To that end vehicles will be equipped with diverse sensors, e.g., LIDAR, cameras, mmWave radar, etc. Unfortunately…
The sensing and monitoring of the urban road network contribute to the efficient operation of the urban transportation system and the functionality of urban systems. However, traditional sensing methods, such as inductive loop sensors,…
Collaborative perception in multi-robot fleets is a way to incorporate the power of unity in robotic fleets. Collaborative perception refers to the collective ability of multiple entities or agents to share and integrate their sensory…
Monitoring urban air quality with high spatiotemporal resolution continues to pose significant challenges. We investigate the use of taxi fleets as mobile sensing platforms, analyzing over 100 million PM2.5 readings from more than 3,000…
Vehicles provide an ideal platform for urban sensing applications, as they can be equipped with all kinds of sensing devices that can continuously monitor the environment around the travelling vehicle. In this work we are particularly…
Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous cars, driver assistance systems, and in-vehicle entertainment, many vehicles have plentiful computational and communication…
We propose a mobile crowdsourced sensors selection approach to improve the journey planning service especially in areas where no wireless or vehicular sensors are available. We develop a location estimation model of journey services based…
Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine…
Finding an available on-street parking spot is a relevant problem of day-to-day life. In recent years, cities such as Melbourne and San Francisco deployed sensors that provide real-time information about the occupation of parking spots.…
Real-time traffic volume inference is key to an intelligent city. It is a challenging task because accurate traffic volumes on the roads can only be measured at certain locations where sensors are installed. Moreover, the traffic evolves…
Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces…