Scientists have explored the human body for hundreds of years, and yet more relationships between the behaviors and health are still to be discovered. With the development of data mining, artificial intelligence technology, and human posture detection, it is much more possible to figure out how behaviors and movements influence people’s health and life and how to adjust the relationship between work and rest, which is needed urgently for modern people against this high-speed lifestyle. Using smart technology and daily behaviors to supervise or predict people’s health is a key part of a smart city. In a smart city, these applications involve large groups and high-frequency use, so the system must have low energy consumption, a portable system, and a low cost for long-term detection. To meet these requirements, this paper proposes a posture recognition method based on multisensor and using LoRa technology to build a long-term posture detection system. LoRa WAN technology has the advantages of low cost and long transmission distances. Combining the LoRa transmitting module and sensors, this paper designs wearable clothing to make people comfortable in any given posture. Aiming at LoRa’s low transmitting frequency and small size of data transmission, this paper proposes a multiprocessing method, including data denoising, data enlarging based on sliding windows, feature extraction, and feature selection using Random Forest, to make 4 values retain the most information about 125 data from 9 axes of sensors. The result shows an accuracy of 99.38% of extracted features and 95.06% of selected features with the training of 3239 groups of datasets. To verify the performance of the proposed algorithm, three testers created 500 groups of datasets and the results showed good performance. Hence, due to the energy sustainability of LoRa and the accuracy of recognition, this proposed posture recognition using multisensor and LoRa can work well when facing long-term detection and LoRa fits smart city well when facing long-distance transmission.

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There exists an electromagnetic shielding effect on radio signals in a tunnel, which results in no satellite positioning signal in the tunnel scenario. Moreover, because vehicles always drive at a high speed on the highway, the real-time localization system (RTLS) has a bottleneck in a highway scenario. Thus, the navigation and positioning service in tunnel and highway is an important technology difficulty in the construction of a smart transportation system. In this paper, a new technology combined downlink time difference of arrival (DL-TDoA) is proposed to realize precise and automated RTLS in tunnel and highway scenarios. The DL-TDoA inherits ultra-wideband (UWB) technology to measure the time difference of radio signal propagation between the location tag and four different location base stations, to obtain the distance differences between the location tag and four groups of location base stations. The proposed solution achieves a higher positioning efficiency and positioning capacity to achieve dynamic RTLS. The DL-TDoA technology based on UWB has several advantages in precise positioning and navigation, such as positioning accuracy, security, anti-interference, and power consumption. In the final experiments on both static and dynamic tests, DL-TDoA represents high accuracy and the mean errors of 11.96 cm, 37.11 cm, 50.06 cm, and 87.03 cm in the scenarios of static tests and 30 km/h, 60 km/h, and 80 km/h in dynamic tests, respectively, which satisfy the requirements of RTLS.