{'fraction_non_alphanumeric': 0.019165727170236752, 'fraction_numerical': 0.0, 'mean_word_length': 5.5777777777777775, '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}}
Resource-Aware Optimization of Modular, Cascade Detection Systems

Detection systems in the emerging Internet of Things (IoT) paradigm, unlike existing stovepipe ones, are envisioned to be a platform for multiple applications. As a result, the design for IoT systems often places strong emphasis on modularity, where components can be composed to form various applications. This modular constraint creates new challenges in finding the optimal trade-off between resource consumption and performance of detection systems. This work focuses on the problem of optimizing modular detection systems whose modules are cascade. The solution results in a dynamic-programming-based algorithm that can optimize cascade detection systems with an arbitrary number of modules. Simulation results on both synthetic and real data confirm the superiority of an optimized system against unoptimized ones.