The need for an accurate indoor positioning system has rapidly increased with the development of large complex malls and underground spaces. As signals from the global positioning system cannot be received inside buildings, only approximate locations can be estimated using Wi-Fi routers or cellular base station information, and exact locations cannot be determined. Therefore, a pedestrian dead reckoning (PDR) scheme using several sensors is suggested in this work. However, this scheme requires users to hold their smartphones in a specific manner; furthermore, user-dependent parameters, such as height and step length, are necessary because the sensor parameters vary. This study uses deep-learning algorithms to overcome the limitations of the existing smartphone-based PDR scheme. A convolutional neural network algorithm is used to classify the smartphone positions; then, appropriate sensor data are selected and adjusted. The long shortterm memory algorithm is used to estimate the user step length. Although the PDR performance is enhanced using the deep-learning algorithm, accumulated error is unavoidable because the algorithm traces the relative position with reference to the original location. Therefore, optical camera communication is introduced to provide the reference location and periodically compensate for the accumulated PDR error. The proposed algorithm is experimentally demonstrated, and its results are obtained and analyzed. INDEX TERMSDeep learning, indoor positioning, optical camera communication, pedestrian dead reckoning Soyoung Jeong received her B.S. in Electronic Engineering from Kookmin University, Korea, in 2020. Currently, she is working toward her M.S. in Electronic Engineering at Kookmin University, Korea. Her research interests include deep learning, indoor positioning, optical camera communications, and sensor networks.
Following the appearance of electrical vehicles and autonomous driving, a new in-vehicle network architecture is required that should be able to process substantial sensor data and communicate with other vehicles or infrastructure. Ethernet is considered a promising technology for replacing existing communication networks due to its stability and large bandwidth. Among various types of Ethernet, 10Base-T1S can play a significant role in connecting multiple nodes in a bus structure at each zone of the zone-based network architecture. Although its latency is reduced using the physical layer collision avoidance (PLCA) algorithm, it is not small enough to be adopted in safety and powertrain domains, which require a very small delay of less than a few hundred microseconds. Therefore, this study uses node prioritization and packet segmentation to overcome the limitations of the existing PLCA algorithm. The former changes the transmission sequence of nodes while the latter reduces the waiting time for a packet. This paper suggests the algorithms of these schemes and analyzes the performance.
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