Participatory Sensing for City-Scale Applications

Participatory Sensing for City-Scale Applications

Tridib Mukherjee (Xerox Research Center, India), Deepthi Chander (Xerox Research Center, India), Sharanya Eswaran (Xerox Research Center, India) and Koustuv Dasgupta (Xerox Research Center, India)
Copyright: © 2017 |Pages: 21
DOI: 10.4018/978-1-5225-0945-5.ch010
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Abstract

The rapid advancements in sensing, computation and communications have led to the proliferation of smart phones. People-centric sensing is a scientific paradigm which empowers citizens with sensor-embedded smartphones, to contribute to micro and macro-scale urban sensing applications – either implicitly (in an opportunistic manner) or explicitly (in a participatory manner). Community-based urban sensing applications, are typically participatory in nature. For instance, commuters reporting on a transit overload may explicitly need to provide an input through an app to report on the overload. This chapter will focus on the trends, challenges and applications of participatory sensing systems. Additionally, they will understand the solution requirements for effective deployments of such systems in real scenarios.
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Introduction

In recent years, participatory sensing has evolved into a scientific paradigm that empowers citizens with sensor-embedded hand-held devices to contribute to micro and macro-scale urban sensing applications. Additionally, with the proliferation of social media and online blogs, city related issues are actively discussed by residents in open public forums on the web. It has thus become imperative for city agencies to properly analyze the information available through participatory sensing towards effective city planning. For example, a city agency may need to know which parts of the city has pollution issue because of garbage and how is it impacting the people. Similarly, a city transportation agency may want to get insights on whether the public transport services are commensurate with the demands in the city. The traffic department may want to know the spatio-temporal distribution of regular traffic problems and their potential causes. Insights on crime-infested areas may aid the police department to prioritize the most affected areas accordingly.

Many mobile crowdsourcing, crowdsensing, and human participatory sensing systems have explored the possibility of collecting implicit and explicit feedback from the residents on urban issues. Examples of such systems include: Moovit (Schwartz, 2015), Waze, Ushahidi (Ushahidi, n.d.), ParkNet (Mathur, 2010), Nericell (Mohan, 2008), PEIR (Mun, 2009) – to name a few. Many emerging cities are further experimenting strategies to engage with residents (citizens) to be the “catalysts” of change. A broad array of platforms, like dedicated Facebook pages, Twitter handles, and hashtags are being used to discuss local issues. Solutions like the Social Networking and Planning Project in Austin, and apps like SeeClickFix, allow citizens to express opinions about city planning, report problems like potholes or broken roads, or simply provide feedback to the agencies.

With the ready availability of Internet connections and smart phones, citizens are increasingly discussing their challenges in open public forums. Yet, while many of these initiatives give people a voice, and generate a lot of valuable data – there seems to be an inherent challenge in converting this data into actions. To worsen the situation, existing practices by many agencies are limited to manual surveys or call centers – that not only make the process slow and cumbersome, but are hardly scalable to growing scenarios. On one hand, it is important for the agencies to properly incentivize the residents to participate in providing meaningful feedback. From another perspective, it is also required for civic agencies to not only be aware of the problems, but also to possess the necessary capabilities to analyze the severity of the problems, often judge the reliability of the informants (reporters), and act upon the identified problems in a timely, accountable manner.

The basic challenge in such city-scale participatory sensing applications stems from the scale of the solution. This chapter will focus on a novel urban sensing platform (USP) that aggregates data from an eco-system of modern data sources (e.g., mobile sensing data, social media, web-based public forums, as well as the civic agencies’ internal data) to derive valuable insights. Figure 1 depicts this vision. Challenges in terms of architecting the platform to gather and aggregate data in a scalable manner will be discussed. The aggregated data is then categorized to create meaningful summaries of reports gathered by the platform. In this context, this chapter will describe text-based categorization techniques which facilitate in determining events and to subsequently summarize them.

Figure 1.

Urban Sensing Platform (USP) vision – a vehicle towards participatory sensing for city-scale applications

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