A Reputationaware Mobile Crowd Sensing Scheme for Emergency Detection The unforeseen proliferation of smart devices has set in motion research efforts aimed at building Smart Cities (SCs) that improve the wellbeing of their citizens. One of the key technologies to achieve a SC is Mobile Crowd Sensing (MCS). In MCS, data is collected from the environment surrounding the smart device owners and utilized in the provision of a wide array of SC services. A prevalent class of services which is attracting increasing attention is smart emergency services, where MCS is leveraged to facilitate the detection and mitigation operations of crises. In this paper, we study the problem of an emergency situation detection based on MCSprovided data from heterogeneous participants. Specifically, we formulate our problem based on Detection Theory and underline its computational complexity. We present a greedy algorithm that aims to balance the tradeoff between the decision time and the quality of the final decision. We perform extensive simulation experiments that show how our scheme improves the correct detection rate compared to a naive reputationunaware baseline.

Trustworthiness and ComfortAware Participant Recruitment for Mobile CrowdSensing in Smart Environments Mobile crowdsensing (MCS) has gained significant momentum in recent years for sensory data acquisition through nondedicated sensors. Even though most of the participants are assumed to be willing to participate in the sensing campaigns, some smartphone users may be reluctant to grant access to particular sensors in their devices. Besides this, the presence of adversaries in the participant pool makes the participant selection problem further challenging. With these in mind, we introduce Reputation and Comfort Level Aware Participant Selection (RACLAPS)which allows participants to modify their available set of sensors that can be accessed by central platformtask publisher i.e. participants can turnoff sensors according to their comfort. To demonstrate the effect of RACLAPS, we utilize Selective and Reputationaware Recruitment (SRR) in which participants are selective in choosing their task to sense solely to improve income. To enrich the discussion, NonSelective and Reputationaware Recruitment (NSR) is also considered in which participants are given no choice regarding selectiveness, sensor configuration. Simulation results show that RACLAPS improves average user utility by 7.6% compared to its predecessor, SRR. We also mention that average discomfort per participant is reduced by 6.4% under RACLAPS when compared to nonselective and selective recruitment approaches.