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Recent Advances in IoT Multi Sensors
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Recent Advances in IoT Multi Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 8 December 2024 | Viewed by 1638

Special Issue Editor

Prof. Dr. Hassan Chizari

E-Mail Website
Guest Editor
Department of Technical Computing, School of Business and Technology, University of Gloucestershire, Cheltenham GL50 2RH, UK
Interests: security in IoT devices; wireless sensor networks; smart grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

IoT multi-sensor platforms are becoming increasingly popular, and as such have recently received a massive amount of attention from researchers. Several research dimensions are associated with this topic, including (but not limited to): 

(1) Advances in sensor technologies, such as LIDAR and hyperspectral imaging, enable IoT multi-sensors to collect more accurate and detailed data.

(2) Sensor fusion, which is the process of combining data from multiple sensors to create a more complete picture of the environment being monitored.

(3) Machine learning, which takes advantage of the multiple sensor data on IoT devices to interpret the data for the device's mission,

(4) Edge computing, where IoT multi-sensors process and analyse data at the edge of the network, closer to the sensors, rather than in the cloud,

(5) Integration: IoT multi-sensors are increasingly being integrated with other devices such as smartphones, smart homes, and industrial automation systems. This allows for more efficient data collection and analysis.

(6) Cybersecurity in IoT multi-sensor devices is another important area of research in the field. Recent advances in IoT security technologies, such as blockchain-based solutions and hardware-based security measures, are helping to address these concerns.

Dr. Hassan Chizari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • advances in sensor technologies
  • sensor fusion
  • machine learning
  • edge computing
  • integration
  • cybersecurity

Published Papers (1 paper)

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Research

18 pages, 1157 KiB  
Article
IoT Data Quality Assessment Framework Using Adaptive Weighted Estimation Fusion
by John Byabazaire, Gregory M. P. O’Hare, Rem Collier and Declan Delaney
Sensors 2023, 23(13), 5993; https://doi.org/10.3390/s23135993 - 28 Jun 2023
Cited by 1 | Viewed by 1383
Abstract
Timely data quality assessment has been shown to be crucial for the development of IoT-based applications. Different IoT applications’ varying data quality requirements pose a challenge, as each application requires a unique data quality process. This creates scalability issues as the number of [...] Read more.
Timely data quality assessment has been shown to be crucial for the development of IoT-based applications. Different IoT applications’ varying data quality requirements pose a challenge, as each application requires a unique data quality process. This creates scalability issues as the number of applications increases, and it also has financial implications, as it would require a separate data pipeline for each application. To address this challenge, this paper proposes a novel approach integrating fusion methods into end-to-end data quality assessment to cater to different applications within a single data pipeline. By using real-time and historical analytics, the study investigates the effects of each fusion method on the resulting data quality score and how this can be used to support different applications. The study results, based on two real-world datasets, indicate that Kalman fusion had a higher overall mean quality score than Adaptive weighted fusion and Naïve fusion. However, Kalman fusion also had a higher computational burden on the system. The proposed solution offers a flexible and efficient approach to addressing IoT applications’ diverse data quality needs within a single data pipeline. Full article
(This article belongs to the Special Issue Recent Advances in IoT Multi Sensors)
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