Tutorial Enabling Technologies for Crowd Sensing and Management In this tutorial we will start with a literature review on the chronological order for IoT, crowd sensing and crowd management, with the objective to set a common underlying reference models. We will tackle and present recent efforts in data collection techniques, where the quality of the reported data highly faces tangible interoperability issues because it has been collected over heterogeneous sources. Such issues include inaccuracies, datalabelling inconsistencies, timesensitivities and different reporting granularities and finally; how to infer information about the actual situations. Along the whole tutorial, we will highlight the different technologies that enable crowd sensing and management, and their availability in the market. In the last part of the tutorial we will comment on privacy issues, and how privacy is jeopardized in emergency situations where the accuracy level highly depends on the collected data. An interesting tradeoff privacyaccuracy will be described.

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.