Developing an Integration Framework for Crowdsourcing and Internet of Things with Applications for Disaster Response

Introduction

With the advent of computers (in1970’s), World Wide Web (www) (in 1990’s) and internet of things (IoT) (present), the global society has moved to the “information age”. The information age offers huge information created due to convergence of internet of things and crowdsourcing which can offers huge opportunities in terms of predictive analytics and forecasting events which are highly unpredictable in nature. The proliferation of semiconductor devices and their applications coupled with the ease and speed of online dissemination enables people to both witness and documents the accidental or unexpected and has permanently changed the way we understand and respond to events around us (Tierney, 2014). The Internet of Things (IoT) and Machine-to-Machine (M2M) communications connect millions of devices to generate data which is voluminous, high variety and high velocity. Johnson (2014) in one of his blogs has argued that to achieve high level of interconnectivity, the enterprises that depend on those machines need them to work reliably, securely, and cost-effectively – without human intervention. Thus in such case crowdsourcing could offer most efficient and reliable solutions. The word crowdsourcing has become quite popular lexicon in business dictionary. However the word crowdsourcing has been extensively used in various contexts in these days. There are multiple explanations to offer shape to the emerging context. However the common understanding related to crowd sourcing is deriving data from crowd who are quite active in digital space to get broader picture. Afuah and Tuci (2012) have attempted to provide an explanation related to crowdsourcing as a tool for solving complex firm related problems rather than relying on its own internal resources or contract to external partners like suppliers. Franzoni and Sauermann (2014) have further attempted to investigate the role of growing crowd science in open collaborative projects. Bloodgood (2013) in response to Afuah and Tuci (2012) work further suggested the need for capturing the value from crowdsourcing. Luttgens et al. (2014) have further attempted to identify the success factors behinds implementation of crowdsourcing and the barriers of the crowdsourcing initiatives. Chiu et al. (2014) have further to decision support systems literature by proposing a framework in which they have argued the role of crowdsourcing at various phases of decision making process.

Gao et al. (2011) have argued that how crowdsourcing applications based on social media applications such as Twitter and Ushahidi provide a powerful capability for collecting information from disaster scenes and visualizing data for relief decision making. Narvaez (2012) has argued that crowdsourcing can be effective in disaster preparedness if properly supported by better usage of technology. Sievers (2015) has further argued for embracing crowdsourcing as a strategy to address emergency planning by state and local governments. As we have noted that there is a rich body of literature on crowdsourcing and its application in disaster relief activities, research on crowdsourcing and IoT integration is scant. Santos et al. (2013) have argued that how IoT support responses to urban disasters. Tierney (2014) has further argued in the favour of crowdsourcing using social media platform to impact the disaster response. However, it is less understood from review of existing literature that how crowdsourcing can further support IoT to generate more reliable data which improve the efforts of disaster relief workers (i.e. disaster response). Hence in an attempt to answer our research objective, we posit some specific research questions as: