Impact of Misbehaving Devices in Mobile Crowd Sourcing Systems

With the tremendous advances in ubiquitous computing, and more specifically with mobile phones, mobile crowd sensing (MCS) has become an appealing part of IoT. However, MCS is vulnerable to multiple types of attacks which could be caused by external or internal adversaries. While external attacks, such as spoofing and jamming are addressed using network's security measures, internal attacks such as maliciously degrading the Quality of Service (QoS) of the tasks by intentionally submitting false reports, should be handled by the MCS systems. The current solutions aim to maximize the completion of tasks in selection based on the reputation or the credibility of the workers, but without consideration of internal attacks threat. This paper focuses on internal misbehaving act, similar to Sybil attacks, where users impersonate multiple identities to change the majority-voting result of the task or to selfishly maximize their profit with minimal costs, using multiple devices. These workers take advantage of the anonymity of MCS for privacy protection to pose the attack. This paper studies the need for a resilient approach which detects and eliminates misbehaving devices during workers' selection in MCS.