Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Advances, Methodologies and Practical Implementations in Fuzzy Sets and Sensor...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances, Methodologies and Practical Implementations in Fuzzy Sets and Sensor Technologies

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2210

Special Issue Editor

Dr. Muhammad Gulistan

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: fuzzy sets and their generalizations; applications; fuzzy neural network

Special Issue Information

Dear Colleagues,

Fuzzy sets provide a robust framework for dealing with sensor technologies' uncertainty, vagueness, and imprecision. This Special Issue, titled "Fuzzy Sets and Applications in Sensor Technologies: Advances, Methodologies and Practical Implementations", aims to explore the integration of fuzzy sets in the context of sensor technologies, showcasing the latest advancements, methodologies, and practical implementations. Fuzzy sets offer a flexible and robust approach to modeling and analyzing sensor data, enabling more accurate interpretation and decision-making processes. This Special Issue brings together researchers and experts to present their cutting-edge research, innovative methodologies, and successful applications of fuzzy sets in various sensor technologies.

This Special Issue focuses on integrating fuzzy sets within sensor technologies, addressing the challenges of uncertainty and imprecision in data interpretation and decision-making processes. This Special Issue provides a valuable resource for researchers, engineers, and practitioners in the field by showcasing the advancements, methodologies, and practical implementations of fuzzy sets in sensor technologies. It contributes to the ongoing discourse on sensor technologies by exploring the application of fuzzy sets, promoting intelligent sensing approaches, and advancing the understanding of how fuzzy sets can enhance the performance and capabilities of sensors.

Dr. Muhammad Gulistan
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

  • fuzzy sets
  • sensor technologies
  • uncertainty modeling
  • data interpretation
  • decision making
  • fuzzy logic
  • practical implementations
  • methodologies
  • intelligent sensors
  • signal processing

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 5812 KiB  
Article
Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics
by Asif Umer, Mushtaq Ali, Ali Imran Jehangiri, Muhammad Bilal and Junaid Shuja
Sensors 2024, 24(8), 2381; https://doi.org/10.3390/s24082381 - 09 Apr 2024
Viewed by 369
Abstract
IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient [...] Read more.
IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes’ priority for task allocation and scheduling. The AHP decides the fog nodes’ priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin. Full article
(This article belongs to the Special Issue Advances, Methodologies and Practical Implementations in Fuzzy Sets and Sensor Technologies)
►▼ Show Figures

Figure 1

22 pages, 10208 KiB  
Article
A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing
by Ioannis Tsitses, Paraskevi Zacharia, Elias Xidias and Michail Papoutsidakis
Sensors 2024, 24(2), 680; https://doi.org/10.3390/s24020680 - 21 Jan 2024
Viewed by 624
Abstract
This paper introduces a fuzzy logic-based autonomous ship deck landing system for fixed-wing unmanned aerial vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing [...] Read more.
This paper introduces a fuzzy logic-based autonomous ship deck landing system for fixed-wing unmanned aerial vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship deck landing model is based on a fuzzy logic system (FLS), which comprises three interconnected subsystems (speed, lateral motion, and altitude components). Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship deck landings. Specifically, the FLS receives five inputs: the range from the deck, the relative wind direction and speed, the airspeed, and the UAV’s flight altitude. The FLS outputs provide data on the speed of the UAV relative to the ship’s velocity, the bank angle (BA), and the angle of descent (AOD) of the UAV. The performance of the designed intelligent ship deck landing system was evaluated using the standard configuration of MATLAB Fuzzy Toolbox. Full article
(This article belongs to the Special Issue Advances, Methodologies and Practical Implementations in Fuzzy Sets and Sensor Technologies)
►▼ Show Figures

Figure 1

19 pages, 1881 KiB  
Article
A Novel Generalization of Q-Rung Orthopair Fuzzy Aczel Alsina Aggregation Operators and Their Application in Wireless Sensor Networks
by Wajid Ali, Tanzeela Shaheen, Iftikhar Ul Haq, Tmader Alballa, Alhanouf Alburaikan and Hamiden Abd El-Wahed Khalifa
Sensors 2023, 23(19), 8105; https://doi.org/10.3390/s23198105 - 27 Sep 2023
Cited by 1 | Viewed by 784
Abstract
Q-rung orthopair fuzzy sets have been proven to be highly effective at handling uncertain data and have gained importance in decision-making processes. Torra’s hesitant fuzzy model, on the other hand, offers a more generalized approach to fuzzy sets. Both of these frameworks have [...] Read more.
Q-rung orthopair fuzzy sets have been proven to be highly effective at handling uncertain data and have gained importance in decision-making processes. Torra’s hesitant fuzzy model, on the other hand, offers a more generalized approach to fuzzy sets. Both of these frameworks have demonstrated their efficiency in decision algorithms, with numerous scholars contributing established theories to this research domain. In this paper, recognizing the significance of these frameworks, we amalgamated their principles to create a novel model known as Q-rung orthopair hesitant fuzzy sets. Additionally, we undertook an exploration of Aczel–Alsina aggregation operators within this innovative context. This exploration resulted in the development of a series of aggregation operators, including Q-rung orthopair hesitant fuzzy Aczel–Alsina weighted average, Q-rung orthopair hesitant fuzzy Aczel–Alsina ordered weighted average, and Q-rung orthopair hesitant fuzzy Aczel–Alsina hybrid weighted average operators. Our research also involved a detailed analysis of the effects of two crucial parameters: λ, associated with Aczel–Alsina aggregation operators, and N, related to Q-rung orthopair hesitant fuzzy sets. These parameter variations were shown to have a profound impact on the ranking of alternatives, as visually depicted in the paper. Furthermore, we delved into the realm of Wireless Sensor Networks (WSN), a prominent and emerging network technology. Our paper comprehensively explored how our proposed model could be applied in the context of WSNs, particularly in the context of selecting the optimal gateway node, which holds significant importance for companies operating in this domain. In conclusion, we wrapped up the paper with the authors’ suggestions and a comprehensive summary of our findings. Full article
(This article belongs to the Special Issue Advances, Methodologies and Practical Implementations in Fuzzy Sets and Sensor Technologies)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop