{'fraction_non_alphanumeric': 0.01730103806228374, 'fraction_numerical': 0.0, 'mean_word_length': 5.888888888888889, '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}}
Underwater sensor deployment using an evolutionary algorithm

Underwater sensor deployment is a significant challenge due to the inherent difficulties posed by the underwater channel in terms of sensing and communications between sensors. In addition, monetary constraints arising from the cost of sensors and deploying them, limit the number of available sensors. As a result, these sensors must be deployed as efficiently as possible. This work presents the Underwater Sensor Deployment Evolutionary Algorithm (USDEA), which uses environmental data and several factors not simultaneously considered, due to the ensuing complexity, including sensing and communications range, to generate highly capable underwater sensor fields. Tradeoffs in field sensing capabilities are shown through a simulation study for two popular sensor network topologies, mesh and cluster.