A probabilistic approach for filtering out spam reports in a vehicular participatory sensing system

Abstract

Participatory sensing (PS) is an emerging socio-technological paradigm by which citizens voluntarily participate and contribute to a distributed information management system using applications installed in their hand-held devices. However, a common problem in participatory sensing is that some users generate spam (false) reports to increase their degree of participation to maliciously gain undue incentives. Such spam reports make the usage of the PS system unreliable and vulnerable to illusion attacks. This work proposes a novel approach to make PS applications authentic by identifying and filtering out spam reports through automated confidence assignment based on a probabilistic model. Waze traffic alerts have been used to validate the proposed report filtering mechanism. Results have been found to be satisfactory and useful in enhancing reliability of the PS system.