scholarly journals Motivating contribution in a participatory sensing system via quid-pro-quo

2014 ◽  
Author(s):  
Anthony Tomasic ◽  
John Zimmerman ◽  
Aaron Steinfeld ◽  
Yun Huang
Keyword(s):  
Participatory Sensing ◽  
Sensing System

Lab-on-Phone: A Participatory Sensing system

2014 ◽  
Author(s):  
Jorge Mario Garzon Rey ◽  
Juan Manuel Soto Valencia ◽  
Antonio Garcia Rozo ◽  
Fredy Segura-Quijano
Keyword(s):  
Participatory Sensing ◽  
Sensing System

Lab-on-Phone: a participatory sensing system

2015 ◽  
Vol 85 (1) ◽  
pp. 159-165
Author(s):  
Jorge Mario Garzón Rey ◽  
Juan Manuel Soto Valencia ◽  
Antonio Garcia-Rozo ◽  
Fredy Segura-Quijano
Keyword(s):  
Participatory Sensing ◽  
Sensing System

scholarly journals Adaptive Reward Allocation for Participatory Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Martin Connolly ◽  
Ivana Dusparic ◽  
Georgios Iosifidis ◽  
Melanie Bouroche
Keyword(s):  
Service Provider ◽  
Response Rate ◽  
Optimization Method ◽  
Participatory Sensing ◽  
Participation Rates ◽  
Dynamic Changes ◽  
Pollution Levels ◽  
Sensing System ◽  
Share Data ◽  
Reward Allocation

Participatory sensing is a paradigm through which mobile device users (or participants) collect and share data about their environments. The data captured by participants is typically submitted to an intermediary (the service provider) who will build a service based upon this data. For a participatory sensing system to attract the data submissions it requires, its users often need to be incentivized. However, as an environment is constantly changing (for example, an accident causing a buildup of traffic and elevated pollution levels), the value of a given data item to the service provider is likely to change significantly over time, and therefore an incentivization scheme must be able to adapt the rewards it offers in real-time to match the environmental conditions and current participation rates, thereby optimizing the consumption of the service provider’s budget. This paper presents adaptive reward allocation (ARA), which uses the Lyapunov Optimization method to provide adaptive reward allocation that optimizes the consumption of the service provider’s budget. ARA is evaluated using a simulated participatory sensing environment with experimental results showing that the rewards offered to participants are adjusted so as to ensure that the data captured matches the dynamic changes occurring in the sensing environment and takes the response rate into account while also seeking to optimize budget consumption.

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