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Analytic Challenges in Social Sensing

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The Art of Wireless Sensor Networks

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Social sensing applications refer to those where individuals play an important role in data collection. They can act as sensor carriers (e.g., carrying GPS devices that share location data), sensor operators (e.g., taking pictures with smart phones), or as sensors themselves (e.g., sharing their observations on Twitter). The proliferation of sensors in the possession of the average individual, together with the popularity of social networks that allow massive information dissemination, heralds an era of social sensing that brings about new research challenges reviewed in this chapter.

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Notes

  1. 1.

    Regression is a technique from estimation theory, applied to continuous or inherently ordered parameters to predict continuous or ordered values. In contrast, prediction uses machine learning to predict unordered class labels.

  2. 2.

    To make error values meaningful, we have normalized fuel consumption values to be zero mean and between \(-1\) and \(1\).

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  1. University of Illinois Urbana-Champaign, Urbana, USA

    Tarek Abdelzaher & Dong Wang

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Correspondence to Tarek Abdelzaher .

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  1. College of Engineering and Computer Science, Department of Computer and Information Science, University of Michigan-Dearborn, Dearborn, Michigan, USA

    Habib M. Ammari

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Abdelzaher, T., Wang, D. (2014). Analytic Challenges in Social Sensing. In: Ammari, H. (eds) The Art of Wireless Sensor Networks. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40066-7_16

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