{'fraction_non_alphanumeric': 0.025167785234899327, 'fraction_numerical': 0.0041946308724832215, 'mean_word_length': 4.876847290640394, '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}}
Predictive-TDMA: A Markov Chain Based MAC Protocol for Mesh Networks

One of the most frequently concerned metrics for evaluating the performance of MAC protocol is throughput. In this paper, we propose a novel Markov Chain based TDMA MAC protocol which is called Predictive-TDMA (P-TDMA) to achieve high performance of throughput for Mesh Networks. In order to give a more effective time slot assignment strategy rather than even assignment, we use Markov Chain to predict the possibility of an end-user device's future working condition, whether it's more likely to be idle or busy. Then based on the ratio of all end-user devices' possibilities of working condition, we assign a proper number of time slots to each end-user device to maximize throughput. Through simulation, the results show that under the scenario that the amount of data is randomly requested by each end-user device, throughput of our design is around 10% better than even assignment strategy. And under the scenario that some end-user devices request much more amount of data than others, which happens more frequently in realistic situations, throughput of our design can be 200% better than even assignment strategy.