ASP Transactions on Internet of Things <p>The <em>ASP Transactions on Internet of Things</em> (ISSN: 2788-8401) publishes articles on the latest advances, as well as review articles, on the various aspects of IoT. Topics include IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Examples are IoT demands, impacts, and implications on sensors technologies, big data management, and future Internet design for various IoT use cases, such as smart cities, smart environments, smart homes, etc. The fields of interest include: IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.</p> Advancing Science Press Limited en-US ASP Transactions on Internet of Things 2788-8401 A Multi-parameter Video Quality Assessment Model Based on 3D Convolutional Neural Network on the Cloud <p><strong>As the rapid development of</strong><strong>&nbsp;big </strong><strong>data and the artificial intelligence technology, users </strong><strong>prefer</strong><strong>&nbsp;</strong><strong>uploading </strong><strong>more and more local files to the </strong><strong>cloud </strong><strong>server</strong><strong>&nbsp;</strong><strong>to reduce the pressure of </strong><strong>local </strong><strong>storage, </strong><strong>b</strong><strong>ut when users upload more and more </strong><strong>duplicate </strong><strong>files</strong><strong>&nbsp;,</strong><strong>&nbsp;not only wast</strong><strong>ing</strong><strong>&nbsp;</strong><strong>the </strong><strong>network bandwidth, </strong><strong>but also bringing much more </strong><strong>inconvenience </strong><strong>to </strong><strong>the server management,</strong><strong>&nbsp;</strong><strong>especially </strong><strong>images</strong><strong>&nbsp;and video</strong><strong>s</strong><strong>. </strong><strong>To solve the problems above, we </strong><strong>design </strong><strong>a multi-parameter video quality assessment model</strong><strong>&nbsp;based on </strong><strong>3D </strong><strong>convolutional neural network</strong><strong>&nbsp;i</strong><strong>n the video deduplication system, </strong><strong>w</strong><strong>e use a method similar to analytic hierarchy process to comprehensively evaluate the impact of packet loss rate, codec, frame rate, bit rate, resolution on video quality, and build a </strong><strong>two</strong><strong>-stream 3D convolutional neural network from </strong><strong>the spatial flow and timing flow to c</strong><strong>apture the details of video distortion</strong><strong>,</strong><strong>&nbsp;set the coding layer to remove redundant distortion information. Finally, the LIVE and CSIQ data sets are used for experimental verification, </strong><strong>we compare </strong><strong>the performance of the proposed scheme </strong><strong>with</strong><strong>&nbsp;the V-BLIINDS scheme and VIDEO scheme under different packet loss rates. </strong><strong>We also use the p</strong><strong>art of data set to simulate the interaction process between the client and the server</strong><strong>, then test the time cost of the scheme</strong><strong>.</strong><strong>&nbsp;</strong><strong>On the whole, the scheme proposed in this </strong><strong>paper </strong><strong>has a high quality assessment efficiency.</strong></p> Xue Li Jiali Qiu Copyright (c) 2021 ASP Transactions on Internet of Things 2021-09-04 2021-09-04 1 2 14 22 10.52810/TIOT.2021.100063 Arctic sea ice detection based on microwave radiometer in 89GHz channels <p>Sea ice is a very important part of the frozen circle. In these years of research, a lot of research on sea ice is realized through microwave remote sensing. Because microwave remote sensing is not easily affected by clouds and light, it is widely used in this field. The design of this paper will classify Arctic sea ice based on microwave remote sensing data. Based on the characteristics of high stability of microwave radiometer, this paper studies the characteristics of sea ice types in the whole Arctic region in January 2017, and uses high-resolution AMSR-E / AMSR2 data in 89Hz frequency band. Data in this frequency band are not easily affected by clouds and water vapor, while data in low frequency band are less affected by clouds and water vapor. Over the years, a lot of research has been done on the use of low frequency band at home and abroad. For this design, the research on the 89GHz frequency band we used is of great significance. We used the high resolution characteristics of multi-year ice and one-year ice in the 89GHz frequency band, the brightness temperature difference of one-year ice, multi-year ice and sea water is analyzed, and the method of using brightness temperature difference to distinguish sea ice species is determined. The fine tree theory in MATALAB decision tree is used to generate classification results. </p> Yue Zhao Xingdong Wang Changfeng Luo Copyright (c) 2021 ASP Transactions on Internet of Things 2021-09-04 2021-09-04 1 2 23 29 10.52810/TIOT.2021.100065 MPC Based Trajectory Tracking for An Automonous Deep-Sea Tracked Mining Vehicle <p class="Abstract"><span lang="EN-US">Model predictive control (MPC) has been successfully used in trajectory tracking for autonomous vehicles based on certain kinematic model under low external disturbance conditions, but when there are model uncertainties and external disturbances, autonomous vehicles will fail to follow the pre-set trajectory. This paper studies trajectory tracking control based on MPC for an autonomous deep-sea tracked mining vehicle in polymetallic nodule mines with model uncertainty and external disturbances. A MPC algorithm is designed for trajectory tracking. To address model uncertainties caused by vehicle body subsidence and track slippage, a drive wheel speed correction controller is designed by experimental data fitting, and Kalman filtering (KF) and adaptive Kalman filtering (AKF) are introduced to improve tracking performance by rejecting external disturbances especially during curve tracking. To handle dead zones and obstacles during actual operation, an obstacle avoidance strategy is proposed that uses the tri-circular arc obstacle avoidance trajectory with an equal curvature for path re-planning. Finally, Simulink&amp;Recurdyn co-simulations validate the performance of the proposed MPC controller through a comparison with nonlinear MPC(NMPC).</span></p> Hongyun Wu Yuheng Chen Hongmao Qin Copyright (c) 2021 ASP Transactions on Internet of Things 2021-08-23 2021-08-23 1 2 1 13 10.52810/TIOT.2021.100062