{'fraction_non_alphanumeric': 0.01977401129943503, 'fraction_numerical': 0.0, 'mean_word_length': 5.58364312267658, '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}}
Distributed Real-Time IoT for Autonomous Vehicles

Real-time Internet of Things (IoT) applications have stringent delay requirements when implemented over distributed sensing and communication networks in smart traffic control. They require the system to reach a permissible neighbourhood of an optimum solution with a tolerable delay. The performance of such applications mostly depends on the delay introduced by the underlying optimization algorithms, with the localized computational capability. In this paper, we study a smart traffic control scenario—a real-time IoT application, where a group of autonomous vehicles independently decide on their lane velocities, in collaboration with road-side units to efficiently utilize intersections with minimal environmental impact. We decompose this problem as an unconstrained network utility maximization problem. A consensus-based, constant step-size gradient descent algorithm is proposed to obtain a near-optimal solution. We analyze the delay–accuracy tradeoff in reaching a near-optimal velocity. Delay is measured in terms of the number of iterations required before the scheduling operation can be done for a particular tolerance. The operation of the algorithm under quantized message passing is also studied. On contrary to the existing methods to intersection management problems, our approach studies the limit at which an optimization algorithm fails to cater for the requirements of a real-time application and must fall back for a pareto-optimal solution, due to the communication constraints. We used simulation of urban mobility to incorporate the microscopic behavior of traffic flows to our simulations and compared our solution with traditional and state-of-the-art intersection management techniques.