{'fraction_non_alphanumeric': 0.026033690658499236, 'fraction_numerical': 0.0, 'mean_word_length': 5.438423645320197, '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}}
A Security Trust Mechanism for Data Collection with Mobile Vehicles in Smart City

Smart city includes all kinds of advanced technologies and solutions, and its maintenance is achieved through massive data collection. In Delay Tolerant Network (DTN), sensors that lack connectivity can store data temporarily and wait for the mobile vehicles in the city to forward, which significantly improves the efficiency of data collection in the city. However, mobile vehicles are not always credible, since the attack from malicious vehicles may be detrimental to data quality. Therefore, there is an urgent need for a secure data collection strategy with mobile vehicles. In this paper, a consistency trust verification strategy for mobile vehicles (CTV-MV) is proposed, including three stages: opportunistic routing, recruitment, and trust verification. Specifically, an average-distance-based outlier detection (ADOD) algorithm is designed to verify heterogeneous data. Then a baseline mechanism is used to trust reasoning for evaluating the trust value. Furthermore, a recruitment strategy that considers trust and data coverage ratio is proposed to maximize data quality. Finally, the performance of CTV-MV is analyzed through experiments in terms of the excellent ratio of data and recruit cost, respectively.