Sell Your Experiences: A Market Mechanism based Incentive for Participatory Sensing - 14 views

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  #1 Jarrad Long on 24 Mar 12

   

  http://www.csee.usf.edu/~labrador/Share/PapersToRead/GameTheory/Incentive%20participation.pdf
  
  (I'm doing mobile phone crowd-sourcing)
  
  Beyond the technological challenges that face participatory sensing (a term that describes crowd-sourcing reliant on the 'sensors' in smartphones rather than thoughtfully created content) is a more fundamental human dilemma: how to motivate the required number of people to participate. Regardless of the application, participatory sensing (and any type of crowd-sourcing for that matter) requires large numbers of participants in order to deliver quality data, so providing an incentive for those people and minimising the dropout rate is crucial.
  
  Authors Lee and Hoh explain that participatory sensing applications often suffer from low participation rates because participants soon lose interest when they're not rewarded for their efforts. To address this, they propose a new incentive mechanism called Reverse Auction based Dynamic Price (RADP) which "focuses on minimising and stabilising incentive cost while maintaining adequate number of participants by preventing users from dropping out of participatory sensing applications".
  
  For readers lacking a degree in economics, the article becomes rather less accessible as it presents a series of complicated formulas, but essentially the idea behind RADP is simple: participants can offer to sell their sensing data to service providers at a price they choose (called their bid), and the service provider selects multiple participants who have offered the lowest bids. Of course, that means the higher bidders are excluded, but they are given virtual credit just for their participation, which aims to maintain a constant number of active bidders.
  
  The second half of the article presents the findings of a study that compared their model to existing ones, with the conclusion that it reduces incentive cost by more than 60% and maintains the desired number of participants.
  
  This model is universal enough to be applied to all types of participatory sensing and crowd-sourcing applications, including the weather and noisemap apps used as case studies in my chosen article. Indeed, the authors of that article acknowledged that an incentive model is a question that remains to be answered regarding the viability of their vision of mobile crowd-sourcing, so it would be interesting to apply the RADP model to that application.
  
  Lee, J. and Hoh, B. (2010) Sell your experiences: A market mechanism based incentive for participatory sensing. Pervasive Computing and Communications (PerCom), 2010 IEEE International Conference on, pages 60-68: USA

   

  <div class="cArrow"> </div><div class="cContentInner"><a href="http://www.csee.usf.edu/~labrador/Share/PapersToRead/GameTheory/Incentive%20participation.pdf" rel="nofollow" target="_blank">http://www.csee.usf.edu/~labrador/Share/PapersToRead/GameTheory/Incentive%20participation.pdf</a> <br /> <br /> (I'm doing mobile phone crowd-sourcing) <br /> <br /> Beyond the technological challenges that face participatory sensing (a term that describes crowd-sourcing reliant on the 'sensors' in smartphones rather than thoughtfully created content) is a more fundamental human dilemma: how to motivate the required number of people to participate. Regardless of the application, participatory sensing (and any type of crowd-sourcing for that matter) requires large numbers of participants in order to deliver quality data, so providing an incentive for those people and minimising the dropout rate is crucial. <br /> <br /> Authors Lee and Hoh explain that participatory sensing applications often suffer from low participation rates because participants soon lose interest when they're not rewarded for their efforts. To address this, they propose a new incentive mechanism called Reverse Auction based Dynamic Price (RADP) which "focuses on minimising and stabilising incentive cost while maintaining adequate number of participants by preventing users from dropping out of participatory sensing applications". <br /> <br /> For readers lacking a degree in economics, the article becomes rather less accessible as it presents a series of complicated formulas, but essentially the idea behind RADP is simple: participants can offer to sell their sensing data to service providers at a price they choose (called their bid), and the service provider selects multiple participants who have offered the lowest bids. Of course, that means the higher bidders are excluded, but they are given virtual credit just for their participation, which aims to maintain a constant number of active bidders. <br /> <br /> The second half of the article presents the findings of a study that compared their model to existing ones, with the conclusion that it reduces incentive cost by more than 60% and maintains the desired number of participants. <br /> <br /> This model is universal enough to be applied to all types of participatory sensing and crowd-sourcing applications, including the weather and noisemap apps used as case studies in my chosen article. Indeed, the authors of that article acknowledged that an incentive model is a question that remains to be answered regarding the viability of their vision of mobile crowd-sourcing, so it would be interesting to apply the RADP model to that application. <br /> <br /> Lee, J. and Hoh, B. (2010) Sell your experiences: A market mechanism based incentive for participatory sensing. Pervasive Computing and Communications (PerCom), 2010 IEEE International Conference on, pages 60-68: USA</div>

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