{'fraction_non_alphanumeric': 0.020917678812415654, 'fraction_numerical': 0.001349527665317139, 'mean_word_length': 5.337606837606837, '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}}
Cognitive Radio Based State Estimation in Cyber-Physical Systems

We investigate the state estimation problem in cyber-physical systems (CPS) where the dynamical physical process is measured by a wireless sensor and the measurements are transmitted to a remote state estimator. It has been shown that the estimation performance strongly depends on the wireless communication quality. To enhance the estimation performance, we apply the cognitive radio technique to the system and propose a CHAnnel seNsing and switChing mEchanism (CHANCE) to explore opportunistic accessibility of multiple channels. We consider two types of wireless channels, i.e., one unlicensed channel which can be accessed freely and several licensed channels which have been pre-assigned to primary users. For the single-licensed-channel case, we develop a necessary condition for the estimation stability based on the physical process dynamics, channel quality and the channel sensing accuracy. This condition becomes also sufficient under certain conditions. We also derive the conditions under which the estimation performance is guaranteed to be improved by CHANCE. The above results are then extended to multi-licensed-channel cases. Simulations based on a particular linear system show that, the long-run mean estimation error covariance with CHANCE is at least 63% less than that without CHANCE. It is also shown that CHANCE outperforms the existing RANDOM mechanism in terms of estimation performance.