{'fraction_non_alphanumeric': 0.01684717208182912, 'fraction_numerical': 0.0, 'mean_word_length': 5.5, '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}}
Quantum inspired genetic algorithm for energy efficient clustering in wireless sensor networks

Clustering has been one of the most commonly used strategies for maximizing the lifetime of wireless sensor networks (WSNs). Clustering in WSNs is the process of grouping the sensors based on some criteria and optimal clustering in WSNs is known to be a NP-Hard problem. Evolutionary algorithms (e.g. genetic algorithm) have been extensively utilized for addressing this problem. In this paper, energy efficient clustering problem has been dealt with using a relatively new meta-heuristic technique known as quantum inspired genetic algorithm. The simulation results and analysis clearly indicate that the proposed approach outperforms genetic algorithm based clustering technique and leads to significant increase in network lifetime.