scholarly journals Heracles: A Context-Based Multisensor Sensor Data Fusion Algorithm for the Internet of Things

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 517
Author(s):  
Flávia C. Delicato ◽  
Tayssa Vandelli ◽  
Mario Bonicea ◽  
Claudio M. de Farias
Keyword(s):  
Internet Of Things ◽  
Data Fusion ◽  
Smart Cities ◽  
Sampling Rate ◽  
Value Added ◽  
Sensor Data ◽  
The Internet ◽  
Fusion Algorithm ◽  
The Internet Of Things ◽  
Analyze Data

In the Internet of Things (IoT), extending the average battery duration of devices is of paramount importance, since it promotes uptime without intervention in the environment, which can be undesirable or costly. In the IoT, the system’s functionalities are distributed among devices that (i) collect, (ii) transmit and (iii) apply algorithms to process and analyze data. A widely adopted technique for increasing the lifetime of an IoT system is using data fusion on the devices that process and analyze data. There are already several works proposing data fusion algorithms for the context of wireless sensor networks and IoT. However, most of them consider that application requirements (such as the data sampling rate and the data range of the events of interest) are previously known, and the solutions are tailored for a single target application. In the context of a smart city, we envision that the IoT will provide a sensing and communication infrastructure to be shared by multiple applications, that will make use of this infrastructure in an opportunistic and dynamic way, with no previous knowledge about its requirements. In this work, we present Heracles, a new data fusion algorithm tailored to meet the demands of the IoT for smart cities. Heracles considers the context of the application, adapting to the features of the dataset to perform the data analysis. Heracles aims at minimizing data transmission to save energy while generating value-added information, which will serve as input for decision-making processes. Results of the performed evaluation show that Heracles is feasible, enhances the performance of decision methods and extends the system lifetime.

scholarly journals Motion Detection to Preserve Personal Privacy from Surveillance Data using Contrary Motion

2020 ◽  
Vol 8 (6) ◽  
pp. 3892-3895
Keyword(s):  
Internet Of Things ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Smart Cities ◽  
Value Added ◽  
The Internet ◽  
Sufficient Information ◽  
Personal Privacy ◽  
Privacy And Security ◽  
The Internet Of Things

Internet of Things network today naturally is one of the huge quantities of devices from sensors linked through the communication framework to give value added service to the society and mankind. That allows equipment to be connected at anytime with anything rather using network and service. By 2020 there will be 50 to 100 billion devices connected to Internet and will generate heavy data that is to be analyzed for knowledge mining is a forecast. The data collected from individual devices of IoT is not going to give sufficient information to perform any type of analysis like disaster management, sentiment analysis, and smart cities and on surveillance. Privacy and Security related research increasing from last few years. IoT generated data is very huge, and the existing mechanisms like k- anonymity, l-diversity and differential privacy were not able to address these personal privacy issues because the Internet of Things Era is more vulnerable than the Internet Era [10][20]. To solve the personal privacy related problems researchers and IT professionals have to pay more attention to derive policies and to address the key issues of personal privacy preservation, so the utility and trade off will be increased to the Internet of Things applications. Personal Privacy Preserving Data Publication (PPPDP) is the area where the problems are identified and fixed in this IoT Era to ensure better personal privacy.

scholarly journals Security Technology of Wireless Sensor Internet of Things Based on Data Fusion

2017 ◽  
Vol 13 (11) ◽  
pp. 25
Author(s):  
Jie Zhang
Keyword(s):  
Internet Of Things ◽  
Data Fusion ◽  
Original Data ◽  
Wireless Sensor ◽  
The Internet ◽  
Fusion Technology ◽  
Security Technology ◽  
Fusion Algorithm ◽  
The Impact ◽  
The Internet Of Things

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">In order to prove the effect of data fusion technology in the Internet of things, a wireless sensor Internet of things security technology based on data fusion is designed, and the impact of data fusion in the field of communication technology is studied. Therefore, two security fusion algorithms are designed on the basis of analyzing and comparing the advantages and disadvantages of various security fusion algorithms, namely, data security fusion algorithm EDCSDA and approximate fusion algorithm PADSA. By analyzing the probability distribution model of the data collected by the nodes, the disturbance data is superimposed on the original data to hide the effect of the original data. A test bed system for perception layer of the Internet of things is designed and implemented. The test results prove the feasibility of the two algorithms. Meanwhile, it shows that the two algorithms can reduce the transmission overhead of the network while guaranteeing the security. Based on the above finding, it is concluded that data fusion technology is very effective for improving network efficiency and prolonging the network life cycle as one of the key technologies in the perception layer of Internet of things.</span>

Application in Data Fusion of Internet of Things Based on the KL Distance of Reputation Model

2013 ◽  
Vol 760-762 ◽  
pp. 587-591 ◽  
Author(s):  
Xu Ping Zhu
Keyword(s):  
Internet Of Things ◽  
Data Fusion ◽  
Interconnection Network ◽  
Limited Resource ◽  
The Internet ◽  
User Privacy ◽  
Fusion Algorithm ◽  
Other Information ◽  
Reputation Model ◽  
The Internet Of Things

The Internet of Things is a bearer network based on the Internet, the traditional telecommunications network, wireless self-organizing networks etc, so that all can be individually addressable ordinary physical objects to achieve the interconnection network. The Internet of Things is evolved from the wireless sensor network, which has limited resource in energy, memory, calculation, bandwidth and etc,. In addition, many applications in the Internet of Things are related to user privacy. In the process of data collection and transmission, the data may be forged , tampered, and a variety of other information security threats. In order to extend the network lifetime, to protect the authenticity and reliability of data fusion, this paper presents a reputation model of data fusion algorithm. The algorithm is verified by simulation, the experimental results show that the proposed algorithm is effective and the result of data aggregation is reliable.

scholarly journals Convergence of Blockchain and IoT for Secure Transportation Systems in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Khizar Abbas ◽  
Lo’Ai A. Tawalbeh ◽  
Ahsan Rafiq ◽  
Ammar Muthanna ◽  
Ibrahim A. Elgendy ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Smart City ◽  
Smart Cities ◽  
Transportation System ◽  
Heterogeneous Data ◽  
Sensor Data ◽  
The Internet ◽  
Transit System ◽  
Smart Transportation ◽  
The Internet Of Things

Smart cities provide citizens with smart and advanced services to improve their quality of life. However, it has been observed that the collection, storage, processing, and analysis of heterogeneous data that are usually borne by citizens will bear certain difficulties. The development of the Internet of Things, cloud computing, social media, and other Industry 4.0 influencers pushed technology into a smart society’s framework, bringing potential vulnerabilities to sensor data, services, and smart city applications. These vulnerabilities lead to data security problems. We propose a decentralized data management system for smart and secure transportation that uses blockchain and the Internet of Things in a sustainable smart city environment to solve the data vulnerability problem. A smart transportation mobility system demands creating an interconnected transit system to ensure flexibility and efficiency. This article introduces prior knowledge and then provides a Hyperledger Fabric-based data architecture that supports a secure, trusted, smart transportation system. The simulation results show the balance between the blockchain mining time and the number of blocks created. We also use the average transaction delay evaluation model to evaluate the model and to test the proposed system’s performance. The system will address residents’ and authorities’ security challenges of the transportation system in smart, sustainable cities and lead to better governance.

scholarly journals Intelligent Building Data Fusion Algorithm by Using the Internet of Things Technology

2021 ◽  
Vol 2143 (1) ◽  
pp. 012030
Author(s):  
Duo Peng ◽  
Jingqiang Zhao ◽  
Tongtong Xu
Keyword(s):  
Internet Of Things ◽  
Data Fusion ◽  
Minimum Variance ◽  
The Internet ◽  
Fusion Algorithm ◽  
Intelligent Building ◽  
Variance Matrix ◽  
Optimal Information ◽  
Internet Of Things Technology ◽  
The Internet Of Things

Abstract Analyzed in this paper based on the Internet of things technology for intelligent building data, redundancy of data fusion are pointed out, based on the dynamic Kalman filter algorithm of multi-sensor fusion, first using the theory of fuzzy and covariance matching technique to adjust the noise covariance of traditional algorithm, combined with weighted minimum variance matrix under the optimal information fusion algorithm of data fusion, Finally, the simulation results show that this algorithm can effectively reduce the redundancy of intelligent data and make the estimated value of data fusion more close to the actual value.

scholarly journals Streaming Data Fusion for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1955 ◽  
Author(s):  
Klemen Kenda ◽  
Blaž Kažič ◽  
Erik Novak ◽  
Dunja Mladenić
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Data Fusion ◽  
Predictive Analytics ◽  
Heterogeneous Data ◽  
Streaming Data ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
The Internet ◽  
The Internet Of Things

To achieve the full analytical potential of the streaming data from the internet of things, the interconnection of various data sources is needed. By definition, those sources are heterogeneous and their integration is not a trivial task. A common approach to exploit streaming sensor data potential is to use machine learning techniques for predictive analytics in a way that is agnostic to the domain knowledge. Such an approach can be easily integrated in various use cases. In this paper, we propose a novel framework for data fusion of a set of heterogeneous data streams. The proposed framework enriches streaming sensor data with the contextual and historical information relevant for describing the underlying processes. The final result of the framework is a feature vector, ready to be used in a machine learning algorithm. The framework has been applied to a cloud and to an edge device. In the latter case, incremental learning capabilities have been demonstrated. The reported results illustrate a significant improvement of data-driven models, applied to sensor streams. Beside higher accuracy of the models the platform offers easy setup and thus fast prototyping capabilities in real-world applications.

scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

The Internet of Things: Making Cities — AndThe Way They Use Technology — Smarter

2016 ◽  
Author(s):  
Wendy W. Fok ◽  
Keyword(s):  
New York ◽  
Internet Of Things ◽  
Energy Use ◽  
Smart Cities ◽  
Traffic Monitoring ◽  
Urban Life ◽  
The Internet ◽  
Time Data ◽  
Heating And Cooling ◽  
The Internet Of Things

Minerva Tantoco was named New York City’s first chief technology officer last year, charged with developing a coordinated citywide strategy on technology and innovation. We’re likely to see more of that as cities around the country, and around the world, consider how best to use innovation and technology to operate as “smart cities.”The work has major implications for energy use and sustainability, as cities take advantage of available, real-time data – from ‘smart’ phones, computers, traffic monitoring, and even weather patterns — to shift the way in which heating and cooling systems, landscaping, flow of people through cities, and other pieces of urban life are controlled. But harnessing Open Innovation and the Internet of Things can promote sustainability on a much broader and deeper scale. The question is, how do you use all the available data to create a more environmentally sound future? The term “Internet of Things” was coined in 1999 by Kevin Ashton, who at the time was a brand manager trying to find a better way to track inventory. His idea? Put a microchip on the packaging to let stores know what was on the shelves.

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