{'fraction_non_alphanumeric': 0.01823486506199854, 'fraction_numerical': 0.0, 'mean_word_length': 5.894472361809045, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
CrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments

Smart cities take advantage of recent Information and Communication Technology (ICT) developments to provide added value to existing public services and improve quality of life for the citizens. The Internet of Things (IoT) paradigm makes the Internet more pervasive where objects equipped with computing, storage, and sensing capabilities are interconnected with communication technologies. Because of the widespread diffusion of IoT devices, applying the IoT paradigm to smart cities is an excellent solution to build sustainable ICT platforms. Having citizens involved in the process through mobile crowdsensing (MCS) techniques augments capabilities of these ICT platforms without additional costs. For proper operation, MCS systems require the contribution from a large number of participants. Simulations are therefore a candidate tool to assess the performance of MCS systems. In this paper, we illustrate the design of CrowdSenSim, a simulator for mobile crowdsensing. CrowdSenSim is designed specifically for realistic urban environments and smart cities services. We demonstrate the effectiveness of CrowdSenSim for the most popular MCS sensing paradigms (participatory and opportunistic), and we present its applicability using a smart public street lighting scenario.