{'fraction_non_alphanumeric': 0.027803521779425393, 'fraction_numerical': 0.0, 'mean_word_length': 5.352941176470588, '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}}
PHGWO: A Duty Cycle Design Method for High-density Wireless Sensor Networks

High-density wireless sensor networks (HDWSNs) have many abilities such as computing, wireless communication, information acquisition, and free-infrastructure capabilities. In HDWSNs, the duty cycle design method is crucial because the energy of a battery is limited. To have a longer network lifetime, duty cycle scheme should be designed properly. Hence, a new parallel hybrid grey wolf optimization (PHGWO) is proposed in this paper for solving the duty cycle design problem. In the experiments, we compare the network lifetime of PHGWO with genetic algorithm (GA), shuffled frog leaping algorithm (SFLA) and particle swarm optimization (PSO). Simulation results show that the PHGWO for the duty cycle design problem in HDWSN enjoys an optimizing the system efficiency compared to the conventional GA, SFLA and PSO methods while maintaining lifetime optimization. PHGWO has displayed strong capabilities to obtain a better convergence as well as prevents local optima by means of visiting the space.