{'fraction_non_alphanumeric': 0.01818181818181818, 'fraction_numerical': 0.0, 'mean_word_length': 5.393548387096774, '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}}
An Energy-Efficient Task Offloading Decision in Electric Power IoT Based on Edge Computing

With the increasing number of the devices in electric power IoT, the cloud center cannot meet the needs of massive data transmission, storage and processing. In order to solve this problem and extend the battery lifetime of smart equipment at the same time, a task offloading model base on edge computing is proposed to minimize system energy consumption under delay constraints. Then the model is formulated as a mixed integer nonlinear programming (MNLIP) problem. Subsequently a hybrid algorithm which combines the advantages of grey wolf optimizer (GWO) and genetic algorithm (GA) is designed to solve the MNLIP problem. Finally, the performance of the algorithm is studied through simulation experiments. The simulation results show that the proposed hybrid algorithm has good convergence performance, and the proposed task offloading decision can effectively reduce system energy consumption.