scholarly journals Cloud-Based Single-Frequency Snapshot RTK Positioning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3688
Author(s):  
Xiao Liu ◽  
Miguel Ángel Ribot ◽  
Adrià Gusi-Amigó ◽  
Adria Rovira-Garcia ◽  
Jaume Sanz Subirana ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Experimental Tests ◽  
Single Frequency ◽  
Integration Time ◽  
Positioning Accuracy ◽  
Timing Accuracy ◽  
Iot Applications ◽  
Zero Baseline

With great potential for being applied to Internet of Things (IoT) applications, the concept of cloud-based Snapshot Real Time Kinematics (SRTK) was proposed and its feasibility under zero-baseline configuration was confirmed recently by the authors. This article first introduces the general workflow of the SRTK engine, as well as a discussion on the challenges of achieving an SRTK fix using actual snapshot data. This work also describes a novel solution to ensure a nanosecond level absolute timing accuracy in order to compute highly precise satellite coordinates, which is required for SRTK. Parameters such as signal bandwidth, integration time and baseline distances have an impact on the SRTK performance. To characterize this impact, different combinations of these settings are analyzed through experimental tests. The results show that the use of higher signal bandwidths and longer integration times result in higher SRTK fix rates, while the more significant impact on the performance comes from the baseline distance. The results also show that the SRTK fix rate can reach more than 93% by using snapshots with a data size as small as 255 kB. The positioning accuracy is at centimeter level when phase ambiguities are resolved at a baseline distance less or equal to 15 km.

scholarly journals Real-time single-frequency precise positioning with Galileo satellites

2021 ◽  
pp. 1-17
Author(s):  
Berkay Bahadur
Keyword(s):  
Real Time ◽  
Experimental Tests ◽  
Convergence Time ◽  
Single Frequency ◽  
Positioning Accuracy ◽  
Precise Positioning ◽  
Frequency Code ◽  
Substantial Progress ◽  
Phase Combination ◽  
Gps Observations

Abstract Following substantial progress achieved recently, the Galileo constellation provides a considerable satellite resource for the GNSS applications. In this regard, the performance assessment of real-time single-frequency precise positioning with Galileo satellites is the main objective of this research. For this purpose, several experimental tests were conducted in this study with two single-frequency positioning models, namely single-frequency code-based positioning and code-phase combination. The results show that Galileo presents an adequate number of visible satellites sufficient for single-frequency positioning. Also, the study demonstrates that, in comparison to GPS observations, Galileo observations have a significantly lower noise level. For the single-frequency code-based positioning, Galileo presents a better positioning accuracy than GPS by 25⋅8% on average. When compared with GPS, a 9⋅4% better positioning accuracy is acquired from Galileo for the single-frequency code-phase combination, with its average convergence time shorter than GPS by a ratio of 24⋅4%.

Real-time force monitoring of smart grippers for Internet of Things (IoT) applications

2018 ◽  
Vol 11 ◽  
pp. 19-28 ◽  
Author(s):  
Zhuming Bi ◽  
Yanfei Liu ◽  
Jeremiah Krider ◽  
Joshua Buckland ◽  
Andrew Whiteman ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Iot Applications ◽  
Force Monitoring

scholarly journals Internet of Things (IoT) Platform for Multi-Topic Messaging

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3346
Author(s):  
Mahmoud Hussein ◽  
Ahmed I. Galal ◽  
Emad Abd-Elrahman ◽  
Mohamed Zorkany
Keyword(s):  
Operating System ◽  
Internet Of Things ◽  
Real Time ◽  
Communication Protocol ◽  
Real Time Systems ◽  
Real Time System ◽  
Client Server ◽  
Iot Applications ◽  
Iot Platforms ◽  
Time Systems

IoT-based applications operate in a client–server architecture, which requires a specific communication protocol. This protocol is used to establish the client–server communication model, allowing all clients of the system to perform specific tasks through internet communications. Many data communication protocols for the Internet of Things are used by IoT platforms, including message queuing telemetry transport (MQTT), advanced message queuing protocol (AMQP), MQTT for sensor networks (MQTT-SN), data distribution service (DDS), constrained application protocol (CoAP), and simple object access protocol (SOAP). These protocols only support single-topic messaging. Thus, in this work, an IoT message protocol that supports multi-topic messaging is proposed. This protocol will add a simple “brain” for IoT platforms in order to realize an intelligent IoT architecture. Moreover, it will enhance the traffic throughput by reducing the overheads of messages and the delay of multi-topic messaging. Most current IoT applications depend on real-time systems. Therefore, an RTOS (real-time operating system) as a famous OS (operating system) is used for the embedded systems to provide the constraints of real-time features, as required by these real-time systems. Using RTOS for IoT applications adds important features to the system, including reliability. Many of the undertaken research works into IoT platforms have only focused on specific applications; they did not deal with the real-time constraints under a real-time system umbrella. In this work, the design of the multi-topic IoT protocol and platform is implemented for real-time systems and also for general-purpose applications; this platform depends on the proposed multi-topic communication protocol, which is implemented here to show its functionality and effectiveness over similar protocols.

Directly Georeferenced Ground Feature Points with UAV Borne Photogrammetric Platform without Ground Control

2013 ◽  
Vol 284-287 ◽  
pp. 1523-1527
Author(s):  
Meng Lun Tsai ◽  
Kai Wei Chiang ◽  
Cheng Fang Lo ◽  
Jiann Yeou Rau
Keyword(s):  
Real Time ◽  
Spatial Information ◽  
Disaster Relief ◽  
Low Cost ◽  
Control Point ◽  
Integrated System ◽  
Single Frequency ◽  
Ground Control ◽  
Differential Gps ◽  
Positioning Accuracy

In order to facilitate applications such as environment detection or disaster monitoring, developing a quickly and low cost system to collect near real time spatial information is very important. Such a rapid spatial information collection capability has become an emerging trend in the technology of remote sensing and mapping application. In this study, a fixed-wing UAV based spatial information acquisition platform is developed and evaluated. The proposed UAV based platform has a direct georeferencing module including an low cost INS/GPS integrated system, low cost digital camera as well as other general UAV modules including immediately video monitoring communication system. This direct georeferencing module is able to provide differential GPS processing with single frequency carrier phase measurements to obtain sufficient positioning accuracy. All those necessary calibration procedures including interior orientation parameters, the lever arm and boresight angle are implemented. In addition, a flight test is performed to verify the positioning accuracy in direct georeferencing mode without using any ground control point that is required for most of current UAV based photogrammetric platforms. In other word, this is one of the pilot studies concerning direct georeferenced based UAV photogrammetric platform. The preliminary results in term of positioning accuracy in direct georeferenced mode without using any GCP illustrate horizontal positioning accuracies in x and y axes are both less than 20 meters, respectively. On the contrary, the positioning accuracy of z axis is less than 50 meters with 600 meters flight height above ground. Such accuracy is good for near real time disaster relief. Therefore, it is a relatively safe and cheap platform to collect critical spatial information for urgent response such as disaster relief and assessment applications where ground control points are not available.

Processing IoT Data

2021 ◽  
pp. 920-943
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Aditya Brahmachari ◽  
Prasenjit Choudhury
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Fog Computing ◽  
Batch Processing ◽  
Time Response ◽  
Iot Applications

This chapter describes how traditionally, Cloud Computing has been used for processing Internet of Things (IoT) data. This works fine for the analytical and batch processing jobs. But most of the IoT applications demand real-time response which cannot be achieved through Cloud Computing mainly because of inherent latency. Fog Computing solves this problem by offering cloud-like services at the edge of the network. The computationally powerful edge devices have enabled realising this idea. Witnessing the exponential rise of IoT applications, Fog Computing deserves an in-depth exploration. This chapter establishes the need for Fog Computing for processing IoT data. Readers will be able to gain a fair comprehension of the various aspects of Fog Computing. The benefits, challenges and applications of Fog Computing with respect to IoT have been mentioned elaboratively. An architecture for IoT data processing is presented. A thorough comparison between Cloud and Fog has been portrayed. Also, a detailed discussion has been depicted on how the IoT, Fog, and Cloud interact among them.

scholarly journals An SVM Based Weight Scheme for Improving Kinematic GNSS Positioning Accuracy with Low-Cost GNSS Receiver in Urban Environments

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7265
Author(s):  
Zhitao Lyu ◽  
Yang Gao
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Urban Environments ◽  
Least Square ◽  
Single Frequency ◽  
Line Of Sight ◽  
Support Vector ◽  
Positioning Accuracy ◽  
Gnss Receiver ◽  
Gnss Positioning

High-precision positioning with low-cost global navigation satellite systems (GNSS) in urban environments remains a significant challenge due to the significant multipath effects, non-line-of-sight (NLOS) errors, as well as poor satellite visibility and geometry. A GNSS system is typically implemented with a least-square (LS) or a Kalman-filter (KF) estimator, and a proper weight scheme is vital for achieving reliable navigation solutions. The traditional weight schemes are based on the signal-in-space ranging errors (SISRE), elevation and C/N0 values, which would be less effective in urban environments since the observation quality cannot be fully manifested by those values. In this paper, we propose a new multi-feature support vector machine (SVM) signal classifier-based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The proposed new weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. To validate the performance of the newly proposed weight scheme, we have implemented it into a real-time single-frequency precise point positioning (SFPPP) system. The dynamic vehicle-based tests with a low-cost single-frequency u-blox M8T GNSS receiver demonstrate that the positioning accuracy using the new weight scheme outperforms the traditional C/N0 based weight model by 65.4% and 85.0% in the horizontal and up direction, and most position error spikes at overcrossing and short tunnels can be eliminated by the new weight scheme compared to the traditional method. It also surpasses the built-in satellite-based augmentation systems (SBAS) solutions of the u-blox M8T and is even better than the built-in real-time-kinematic (RTK) solutions of multi-frequency receivers like the u-blox F9P and Trimble BD982.

scholarly journals Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications

2021 ◽  
Vol 7 ◽  
pp. e500
Author(s):  
Mina Younan ◽  
Essam H. Houssein ◽  
Mohamed Elhoseny ◽  
Abd El-mageid Ali
Keyword(s):  
Time Series ◽  
Performance Analysis ◽  
Internet Of Things ◽  
Real Time ◽  
The Internet ◽  
Fusion Model ◽  
Time Data ◽  
Iot Applications ◽  
Real Time Data ◽  
Smart Things

The Internet of Things (IoT) has penetrating all things and objects around us giving them the ability to interact with the Internet, i.e., things become Smart Things (SThs). As a result, SThs produce massive real-time data (i.e., big IoT data). Smartness of IoT applications bases mainly on services such as automatic control, events handling, and decision making. Consumers of the IoT services are not only human users, but also SThs. Consequently, the potential of IoT applications relies on supporting services such as searching, retrieving, mining, analyzing, and sharing real-time data. For enhancing search service in the IoT, our previous work presents a promising solution, called Cluster Representative (ClRe), for indexing similar SThs in IoT applications. ClRe algorithms could reduce similar indexing by O(K − 1), where K is number of Time Series (TS) in a cluster. Multiple extensions for ClRe algorithms were presented in another work for enhancing accuracy of indexed data. In this theme, this paper studies performance analysis of ClRe algorithms, proposes two novel execution methods: (a) Linear execution (LE) and (b) Pair-merge execution (PME), and studies sorting impact on TS execution for enhancing similarity rate for some ClRe extensions. The proposed execution methods are evaluated with real examples and proved using Szeged-weather dataset on ClRe 3.0 and its extensions; where they produce representatives with higher similarities compared to the other extensions. Evaluation results indicate that PME could improve performance of ClRe 3.0 by = 20.5%, ClRe 3.1 by = 17.7%, and ClRe 3.2 by = 6.4% in average.

scholarly journals Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mohammad Kamrul Hasan ◽  
Muhammad Shafiq ◽  
Shayla Islam ◽  
Bishwajeet Pandey ◽  
Yousef A. Baker El-Ebiary ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Encryption Algorithm ◽  
Support Vector ◽  
Iot Applications ◽  
Encryption And Decryption ◽  
Cryptographic Algorithms ◽  
Encryption Algorithms ◽  
Guessing Attack

As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.

scholarly journals Internet of Things for Smart Cities: Current Issues and Research Challenges

2020 ◽  
Vol 9 (3) ◽  
pp. 3817-3822
Keyword(s):  
Internet Of Things ◽  
Literature Review ◽  
Comparative Study ◽  
Real Time ◽  
Smart City ◽  
Smart Cities ◽  
Urban Systems ◽  
Iot Applications ◽  
Research Challenges ◽  
Smart Mobility

Every day, we are stepping towards to lead a smart life within a smart world, thanks of IoT smart applications. The continually need for new urban systems including smart infrastructures, smart energy grids and smart mobility systems makes appear of a new concept, named: “Smart City”. This concept represents one of the most promising challenges of IoT applications since it involves the enhancement of our lifestyle. Among its promising advantage we can cites: the reducing resource consumption, the real-time guidance for citizens, the transportation facilities, etc. In this paper, we propose, first, a literature review on researches addressing many aspects of Smart City. Second, we provide a comparative study between these researches on the basic of multiple criteria like interoperability, scalability, security, etc.

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