Related Concept Videos
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device
Tagging and Fusion Proteins
Types of Global Positioning System Surveys
Distribution Reliability and Automation
Errors in Global Positioning System
Short-distance Transport of Resources
You might also read
Related Articles
Articles linked to this work by shared authors, journal, and citation graph.
Correction: He et al. An Edge-Computing-Based Emotion-Aware Adaptive Lighting System for Intelligent Cockpits. <i>Sensors</i> 2026, <i>26</i>, 3489.
Related Experiment Video
Updated: May 25, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
Published on: February 3, 2021
RCoD: Reputation-Based Context-Aware Data Fusion for Mobile IoT.
Samia Tasnim1, Niki Pissinou2, S Sitharama Iyengar2
1Department of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH 43606, USA.
This study introduces a novel Reputation-Based Context-Aware Data-Fusion (RCoD) mechanism to enhance data accuracy in mobile Internet of Things (IoT) systems. RCoD effectively identifies malicious participants and recovers corrupted data, ensuring reliable sensor information.
More Related Videos
06:49Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
Published on: December 11, 2015
11:21Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Area of Science:
- Computer Science
- Data Science
- Sensor Networks
Background:
- Mobile sensing technologies and the Internet of Things (IoT) are rapidly expanding.
- Ensuring data reliability and accuracy in mobile IoT systems is challenging due to inherent vulnerabilities.
- People-centric architectures in mobile IoT can lead to inaccurate or corrupted data, especially with malicious participants.
Purpose of the Study:
- To address the challenge of inaccurate data prediction in mobile IoT systems.
- To develop a mechanism resilient against malicious data injection and corruption attacks.
- To recover missing or imprecise data values from correlated data streams.
Main Methods:
- Proposing a Reputation-Based Context-Aware Data-Fusion (RCoD) mechanism.
- Utilizing a Contextual Hidden Markov Model for accurate real-time data prediction.
- Evaluating RCoD's resilience against on-off and data-corruption attacks.
Main Results:
- RCoD accurately identified honest participants even when the majority were malicious.
- The mechanism demonstrated resilience against various malicious data injection rates.
- Empirical evaluation using Beijing's air-quality dataset confirmed RCoD's superiority over state-of-the-art methods.
Conclusions:
- The proposed RCoD mechanism significantly improves data accuracy and reliability in mobile IoT environments.
- RCoD offers a robust solution for data fusion and prediction in the presence of adversarial participants.
- Context-aware data fusion combined with reputation systems is effective for secure and dependable IoT data.