Adaptive Reward Allocation for Participatory Sensing

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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Privacy Aware Incentivization for Participatory Sensing

Sensors ◽

10.3390/s19184049 ◽

2019 ◽

Vol 19 (18) ◽

pp. 4049

Author(s):

Connolly ◽

Dusparic ◽

Iosifidis ◽

Bouroche

Keyword(s):

Real Time ◽

Mobile Device ◽

Service Provider ◽

Environmental Conditions ◽

Incentive Compatibility ◽

Environmental Data ◽

Participatory Sensing ◽

Data Submission ◽

Incentive Compatible ◽

Reward Allocation

Abstract: Participatory sensing is a process whereby mobile device users (or participants) collect environmental data on behalf of a service provider who can then build a service based upon these data. To attract submissions of such data, the service provider will often need to incentivize potential participants by offering a reward. However, for the privacy conscious, the attractiveness of such rewards may be offset by the fact that the receipt of a reward requires users to either divulge their real identity or provide a traceable pseudonym. An incentivization mechanism must therefore facilitate data submission and rewarding in a way that does not violate participant privacy. This paper presents Privacy-Aware Incentivization (PAI), a decentralized peer-to-peer exchange platform that enables the following: (i) Anonymous, unlinkable and protected data submission; (ii) Adaptive, tunable and incentive-compatible reward computation; (iii) Anonymous and untraceable reward allocation and spending. PAI makes rewards allocated to a participant untraceable and unlinkable and incorporates an adaptive and tunable incentivization mechanism which ensures that real-time rewards reflect current environmental conditions and the importance of the data being sought. The allocation of rewards to data submissions only if they are truthful (i.e., incentive compatibility) is also facilitated in a privacy-preserving manner. The approach is evaluated using proofs and experiments.

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Paper, E-mail, or Both? Effects of Contact Mode on Participation in a Web Survey of Establishments

Social Science Computer Review ◽

10.1177/0894439318805160 ◽

2018 ◽

Vol 37 (6) ◽

pp. 750-765 ◽

Cited By ~ 2

Author(s):

Joseph W. Sakshaug ◽

Basha Vicari ◽

Mick P. Couper

Keyword(s):

Response Rate ◽

Population Based ◽

High Response Rate ◽

Nonresponse Bias ◽

Web Survey ◽

Participation Rates ◽

Web Surveys ◽

Contact Mode ◽

Mail Address ◽

E Mail

Identifying strategies that maximize participation rates in population-based web surveys is of critical interest to survey researchers. While much of this interest has focused on surveys of persons and households, there is a growing interest in surveys of establishments. However, there is a lack of experimental evidence on strategies for optimizing participation rates in web surveys of establishments. To address this research gap, we conducted a contact mode experiment in which establishments selected to participate in a web survey were randomized to receive the survey invitation with login details and subsequent reminder using a fully crossed sequence of paper and e-mail contacts. We find that a paper invitation followed by a paper reminder achieves the highest response rate and smallest aggregate nonresponse bias across all-possible paper/e-mail contact sequences, but a close runner-up was the e-mail invitation and paper reminder sequence which achieved a similarly high response rate and low aggregate nonresponse bias at about half the per-respondent cost. Following up undeliverable e-mail invitations with supplementary paper contacts yielded further reductions in nonresponse bias and costs. Finally, for establishments without an available e-mail address, we show that enclosing an e-mail address request form with a prenotification letter is not effective from a response rate, nonresponse bias, and cost perspective.

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Parameter Tuning in the Application of Stochastic Resonance to Redundant Sensor Systems

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2015.p0251 ◽

2015 ◽

Vol 27 (3) ◽

pp. 251-258

Author(s):

Nagisa Koyama ◽

◽

Shuhei Ikemoto ◽

Koh Hosoda

Keyword(s):

Stochastic Resonance ◽

Random Noise ◽

Parameter Tuning ◽

Underlying Mechanism ◽

Optimization Method ◽

Tactile Sensing ◽

Engineering Research ◽

Sensing System ◽

Actual Application ◽

Higher Sensitivity

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270003/04.jpg"" width=""340"" />Basic concept of proposed method</div> Stochastic resonance (SR) is a phenomenon by which the addition of random noise improves the detection of weak signals. Thus far, this phenomenon has been extensively studied with the aim of improving sensor sensitivity in various fields of engineering research. However, the possibility of actual application of SR has not been explored because the target signal has to be known in order to confirm the occurrence of SR. In this paper, we propose an optimization method for making SR usable in engineering applications. The underlying mechanism of the proposed method is investigated using information theory and numerical simulation. We developed a tactile sensing system based on the simulation results. The proposed method is applied to this system in order to optimize its parameters for exploiting SR. Results of the experiment show that the developed tactile sensing system successfully achieved higher sensitivity than a conventional system.

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