Vehicular data acquisition and analytics system for real-time driver behavior monitoring and anomaly detection

A cloud based, vehicular data acquisition and analytics system for real-time driver behavior monitoring, trip analysis, and vehicle diagnostics is presented. The system consists of an On Board Diagnostics (OBD) port to Bluetooth dongle, an app running on a smartphone, and a cloud-based backend. Based on OBD data, Complex Event Processors (CEPs) at both the smartphone and backend detects and notifies unsafe and anomalous events in real time. For example, CEP at the smartphone alerts drivers about rising coolant temperature and rapid fuel drops. It also provides a trip log and filter out messages to be sent to the backend, saving both the bandwidth and power. Backend CEP detects reckless driving in real time. Backend also uses historical data to detect driving anomalies and predict impending sensor failures. The system is tested on actual vehicles and tests demonstrate that the computing, bandwidth, and power consumption of the smartphone are reasonable.

Download Full-text

Improved real-time data anomaly detection using context classification

Journal of Hydroinformatics ◽

10.2166/hydro.2011.042 ◽

2011 ◽

Vol 13 (3) ◽

pp. 307-323 ◽

Cited By ~ 18

Author(s):

Nemanja Branisavljević ◽

Zoran Kapelan ◽

Dušan Prodanović

Keyword(s):

Anomaly Detection ◽

Data Acquisition ◽

Real Time ◽

Water Distribution ◽

Real Life ◽

Detection Methods ◽

Time Data ◽

Detection Techniques ◽

Real Time Data ◽

Processing Techniques

The number of automated measuring and reporting systems used in water distribution and sewer systems is dramatically increasing and, as a consequence, so is the volume of data acquired. Since real-time data is likely to contain a certain amount of anomalous values and data acquisition equipment is not perfect, it is essential to equip the SCADA (Supervisory Control and Data Acquisition) system with automatic procedures that can detect the related problems and assist the user in monitoring and managing the incoming data. A number of different anomaly detection techniques and methods exist and can be used with varying success. To improve the performance, these methods must be fine tuned according to crucial aspects of the process monitored and the contexts in which the data are classified. The aim of this paper is to explore if the data context classification and pre-processing techniques can be used to improve the anomaly detection methods, especially in fully automated systems. The methodology developed is tested on sets of real-life data, using different standard and experimental anomaly detection procedures including statistical, model-based and data-mining approaches. The results obtained clearly demonstrate the effectiveness of the suggested anomaly detection methodology.

Download Full-text

Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

Discover Internet of Things ◽

10.1007/s43926-021-00009-4 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

E. Bertino ◽

M. R. Jahanshahi ◽

A. Singla ◽

R.-T. Wu

Keyword(s):

Data Acquisition ◽

Real Time ◽

Original Data ◽

Local Analysis ◽

Civil Infrastructure ◽

Time Requirements ◽

Iot Devices ◽

Learning Reinforcement ◽

The Times ◽

Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

Download Full-text

ReRe: A Lightweight Real-Time Ready-to-Go Anomaly Detection Approach for Time Series

2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) ◽

10.1109/compsac48688.2020.0-226 ◽

2020 ◽

Author(s):

Ming-Chang Lee ◽

Jia-Chun Lin ◽

Ernst Gunner Gan

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Real Time ◽

Detection Approach

Download Full-text

Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽

10.3390/s21124237 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4237

Author(s):

Hoon Ko ◽

Kwangcheol Rim ◽

Isabel Praça

Keyword(s):

Anomaly Detection ◽

Real Time ◽

Feature Detection ◽

Trading System ◽

Judgment Accuracy ◽

Energy Trading ◽

Real Time Processing ◽

Detection Model ◽

Time Required ◽

The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

Download Full-text

The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽

10.3390/j4020012 ◽

2021 ◽

Vol 4 (2) ◽

pp. 147-153

Author(s):

Paula Morella ◽

María Pilar Lambán ◽

Jesús Antonio Royo ◽

Juan Carlos Sánchez

Keyword(s):

Decision Making ◽

Data Acquisition ◽

Real Time ◽

Failure Detection ◽

Cyber Physical Systems ◽

Future Research ◽

Time Data ◽

Physical Systems ◽

The Real ◽

Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

Download Full-text