Limiting the Influence of Low Quality Information in Community Sensing

We consider a community of private sensors that collect measurements of a physical phenomenon, such as air pollution, and report it to a center. The center should be able to prevent low quality reports from degrading the quality of the aggregated information, as there are numerous reasons for operators to inject false sensor data. Hence, it is necessary to track the quality of the sensors over time in order to filter out low quality and malicious reports. To achieve this, we construct a reputation system with a guaranteed bounds on negative impact that malicious sensors can cause, and we evaluate its performance on a realistic dataset.

[1]  Jean-Yves Le Boudec,et al.  Performance analysis of the CONFIDANT protocol , 2002, MobiHoc '02.

[2]  Avinash Srinivasan,et al.  iTrust: an integrated trust framework for wireless sensor networks , 2010, SAC '10.

[3]  Nicholas R. Jennings,et al.  Trust-based fusion of untrustworthy information in crowdsourcing applications , 2013, AAMAS.

[4]  Wen Hu,et al.  On the need for a reputation system in mobile phone based sensing , 2014, Ad Hoc Networks.

[5]  Lothar Thiele,et al.  OpenSense: open community driven sensing of environment , 2010, IWGS '10.

[6]  Paul Resnick,et al.  The influence limiter: provably manipulation-resistant recommender systems , 2007, RecSys '07.

[7]  S. Buchegger,et al.  A Robust Reputation System for Mobile Ad-hoc Networks , 2003 .

[8]  Zhu Han,et al.  CatchIt: Detect Malicious Nodes in Collaborative Spectrum Sensing , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[9]  M. Hansen,et al.  Participatory Sensing , 2019, Internet of Things.

[10]  Tim Roughgarden,et al.  Algorithmic Game Theory , 2007 .

[11]  Mani B. Srivastava,et al.  Reputation-based framework for high integrity sensor networks , 2004, SASN '04.

[12]  Haiguang Chen,et al.  Task-based Trust Management for Wireless Sensor Networks , 2009 .

[13]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[14]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[15]  Audun Jøsang,et al.  AIS Electronic Library (AISeL) , 2017 .

[16]  Steven Reece,et al.  Multi-sensor fault recovery in the presence of known and unknown fault types , 2009, 2009 12th International Conference on Information Fusion.

[17]  Nicholas R. Jennings,et al.  Mechanism design for the truthful elicitation of costly probabilistic estimates in distributed information systems , 2011, Artif. Intell..

[18]  Roy N. Colvile,et al.  Uncertainty in dispersion modelling and urban air quality mapping , 2002 .

[19]  Refik Molva,et al.  Core: a collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks , 2002, Communications and Multimedia Security.

[20]  Boi Faltings,et al.  Incentive Schemes for Participatory Sensing , 2015, AAMAS.

[21]  Landon P. Cox,et al.  YouProve: authenticity and fidelity in mobile sensing , 2011, SenSys.

[22]  R. Hanson.  LOGARITHMIC MARKETS CORING RULES FOR MODULAR COMBINATORIAL INFORMATION AGGREGATION , 2012 .

[23]  Alec Wolman,et al.  I am a sensor, and I approve this message , 2010, HotMobile '10.