{'fraction_non_alphanumeric': 0.02551984877126654, 'fraction_numerical': 0.0, 'mean_word_length': 5.418181818181818, '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}}
Maximizing target coverage rate in wireless sensor networks based on clone adaptive glowworm swarm optimization

Wireless sensor networks (WSNs) have potentials of communications, detecting, processing as well as storage abilities. Maximizing target coverage rate has always been important aspects of the research of WSNs. In this paper, a clone adaptive glowworm swarm optimization (CAGSO) is given to obtain the maximum number of monitored target in WSNs. In the proposed CAGSO, a glowworm swarm optimization, which combines the merits of a clone generator and adaptive adjuster, is developed. Simulations are conducted to show a comparison of CAGSO with the other three heuristics. In the experiments, the CAGSO method maintains a higher target coverage percentage than shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO) and simulated annealing (SA), and its complexity is lower than that of previous methods. It is more powerful and simpler than available heuristics, and can avoid local optima while searching for a better result.