Optimized Design of Mechanical Chain Drive Based on a Wireless Sensor Network Data Algorithm

In this paper, we use a wireless sensor network data algorithm to optimize the design of mechanical chain drive by conducting an in-depth study of the mechanical chain drive optimization. We utilize the crowdsourcing feature of the swarm-wise sensing network for assisted wireless sensor networking to achieve crowdsourcing-assisted localization. We consider a framework for crowdsourcing-assisted GPS localization of wireless sensor networks and propose two recruitment participant optimization objectives, namely, minimum participants and time efficiency, respectively. A model and theoretical basis are provided for the subsequent trusted data-driven participant selection problem in swarm-wise sensing networks. The sprocket-chain engagement frequency has the greatest influence on the horizontal bending-vertical bending composite in different terrain conditions. The dynamic characteristics under working conditions are most influenced, while the scraping of the scraper and the central groove significantly influenced horizontal bending and vertical bending. Under load conditions, the amplitude of the scraper and central groove scraping increases significantly, which harm the dynamics of the scraper conveyor. By monitoring the speed difference between the head and tail sprockets and the overhang of the scraper, the tensioning status of the scraper conveyor chain can be effectively monitored to avoid chain jamming and chain breakage caused by the loose chain, thus improving the reliability and stability of the scraper conveyor.

Performance Analysis and Architectures for a MEMS-SINS/GPS Ultratight Integration System

In order to analyze the performance of strapdown inertial navigation system/global position system (SINS/GPS) ultratight integration system with low-precision microelectromechanical system (MEMS) under challenging environments, a new MEMS-SINS/GPS ultratight integration scheme is designed. The time-space difference carrier phase velocity (TSDCP-v) is used to assist the carrier tracking loop, the measurement model including nonlinear term is established, and the corresponding filtering algorithm is designed. A simulation and verification platform is established to analyze and verify the performance of the MEMS-SINS/GPS ultratight integration system designed in this paper. Compared with the SINS/GPS tight integration navigation system, the MEMS-SINS/GPS ultratight integration system has higher dynamic performance, anti-interference capability, and navigation performance. At the same time, the MEMS-SINS/GPS ultratight integration system improves the carrier tracking performance of SINS-assisted GPS ultratight integration system when using low-precision MEMS and in high dynamics, strong interference environments.

Intelligent Agent Technology for Cellular-Assisted GPS Positioning using Bayesian and Self-Organizing Map

This paper presents the use of intelligent agent technology, cellular-assisted Global Positioning System (GPS) and data mining for positioning purpose. Due to overlapping coverage areas of cell towers, conventional cell-based positioning techniques have been reported to be inaccurate. Current cell-assisted GPS positioning setup with high accuracy is costly as it requires huge investments on hardware deployments. A new solution of using intelligent agent technology was proposed by the authors for an economical and satisfactory cell-assisted GPS positioning system. Location information in the form of cell identity (ID) and GPS coordinates pairs can be acquired via devices such as smart phones and GPS trackers. The cell ID-GPS coordinates pairs are then grouped by each individual cell ID. An intelligent agent equipped with data mining capabilities is then deployed to computer the optimal GPS coordinates of the cell ID to provide more precise location information. The proposed solution was evaluated via a prototype system. The system was built to collect raw data of cell-ID and GPS coordinate pairs from trackers and mobile phone applications. Using the reference GPS coordinate that was calculated by taking the mean of longitude and mean of latitude for all the GPS coordinates clustered in the same group, the geographical distance between each GPS coordinate and the reference GPS coordinate in the same group was computed to evaluate the performance of the proposed solution.. Experimental results showed that the proposed solution based on intelligent agent equipped with data mining capability helped in improving the prediction of location with sub-kilometer accuracy, in contract to the conventional cell-assisted GPS positioning system which have low accuracy with distance rate various in kilometers.

PSO-LSSVR Assisted GPS/INS Positioning in Occlusion Region

Satellite signals are easily lost in complex observation environments and high dynamic motion states, and the position and posture errors of pure inertial navigation quickly diverges with time. This paper therefore proposes a scheme of occlusion region navigation based on least squares support vector regression (LSSVR), and particle swarm optimization (PSO), used to seek the global optimal parameters. Firstly, the scheme uses the incremental output of GPS (Global Positioning System) and Inertial Navigation System (INS) when the observation is normal as the training output and the training input sample, and then uses PSO to optimize the regression parameters of LSSVR. When the satellite signal is unavailable, the trained mapping model is used to predict the GPS pseudo position. Secondly, the observed anomaly is detected by the test statistic in the integrated navigation solution filtering estimation, and the exponential fading adaptive factor is introduced to suppress the influence of the abnormal pseudo observation value. The results indicate that the algorithm can predict the higher precision GPS position increment, and can effectively judge some abnormal observations that may occur in the predicted value, and adjust the observed noise covariance to suppress the anomaly observation, which can effectively improve the continuity and reliability of the integrated navigation system in the occlusion region.

Internet of Things for a Smart Transportation System

Today, school buses transport millions of students to and from schools. Therefore, safety of school students is still a hot topic and the most imperative issue. The evolution of wireless location-based services has created consumer requests for availability of global positioning systems (GPS) in urban and indoor environments. Nowadays, there is a requirement to deliver a system for the school bus, that monitors its location and speed. This work investigates two different ways to track the vehicle through ordinary GPS, Assisted-GPS (AGPS). A-GPS improves startup performance (time-to-first-fix (TTFF)), of a GPS satellite-based positioning system in the downtown of big cities depending on 4G/LTE cellular networks. The proposed system is basically built using Raspberry Pi (3) with 4G model shield and important telematics technologies like Representational state transfer (RESTful API) which is the vital key for the Internet of Things (IoT) field.