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Crowdsourcing has emerged as an important data collection paradigm in participatory and human-centric sensing applications. While many crowdsourcing studies focus on sensing and recovering the status of the physical world, this paper investigates the problem of profiling the crowd sensors (i.e., humans). In particular, we study the problem of accurately inferring the home locations of people from...
This article addresses one of the key challenges of engaging a massive ad hoc crowd by providing sustainable incentives. The incentive model is based on a context-aware cyber-physical spatio-temporal serious game with the help of a mobile crowd sensing mechanism. To this end, this article describes a framework that can create an ad hoc social network of millions of people and provide context-aware...
Bus transports in the cities of many developing countries are marred with severe problems, like information unavailability, bad road and bus conditions, lack of proper scheduling and timing, and so on. An information service can become extremely handy for the travelers in countries with emerging economy, where public traffic systems are generally riddled with uncertainty. We have developed CrowdMap...
This study is about the disastrous flooding of an Indian metropolitan area of Chennai when the rain had nearly broken the record of 100-years with 374 mm rain falling on December 1, 2015, virtually breaking the November monthly average of 407.4 mm in a day. This city with a population of approximately 6.7 million people came to a standstill. Astonishingly, one of the biggest software development hubs...
Robust and prompt emergency response is a crucial service that smart cities should provide to citizens, communities, and corporations. Emergency management strategies that are currently supported by cities yield pre-determined protocols that can only handle well-understood incidents. However, there are incidents whose nature, shape, scale, and timing are not as predictable. The lack of adequate data...
This paper presents an unsupervised approach toaccurately discover interesting places in a city from location-basedsocial sensing applications, a new sensing applicationparadigm that collects observations of physical world fromLocation-based Social Networks (LBSN). While there are alarge amount of prior works on personalized Point of Interests(POI) recommendation systems, they used supervised learningapproaches...
In recent years crowdsourcing systems have shown to provide important benefits to Smartcities, where ubiquitous citizens, acting as mobile human sensors, assist in responding to signals and providing real-time information about city events, to improve the quality of life for businesses and citizens. In this paper we present REquEST, our approach to selecting a small subset of human sensors to perform...
This paper focuses on the trustworthiness of data gathered from different sources, including crowdsensing and crowdsourcing, in pervasive systems. The specific focus is on mPASS (mobile Pervasive Accessibility Social Sensing), a system devoted to support mobile users with accessibility needs in a smart city context. mPASS is in charge of collecting data about urban and architectural barriers and facilities,...
This work presents a novel geospatial mapping service, based on OpenStreetMap, which has been designed and developed in order to provide personalized path to users with special needs. This system gathers data related to barriers and facilities of the urban environment via crowd sourcing and sensing done by users. It also considers open data provided by bus operating companies to identify the actual...
This work presents mPASS (mobile Pervasive Accessibility Social Sensing), a social and ubiquitous context aware system to provide users with personalized and accessible pedestrian paths and maps. In order to collect a complete data set, our system gathers information from different sources: sensing, crowdsourcing and data produced by local authors and disability organizations. Gathered information...
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