FUZZY MULTI-CRITERIA DECISION-MAKING APPROAHCES FOR OPTIMAL SELECTION OF EMBEDDED SYSTEMS FOR AUTONOMOUS NAVIGATED LIGHT VEHICLES
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Selecting the best type of embedded system for an autonomous navigated light vehicle is complicated. The complexity arises from the need to consider criteria such as navigation accuracy, processing speed, power consumption, compatibility, and reliability when building an autonomous navigated light vehicle. This study evaluates three types of embedded systems: Arduino, Raspberry Pi, and NVIDIA Jetson. Therefore, this study aims to evaluate and select the best embedded system for an autonomous navigated light vehicle. The Fuzzy Analytic Hierarchy Process (FAHP), the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS), and the Fuzzy Simple Additive Weighting (FSAW) methods were used to test and choose from between the three types of embedded systems. Each method has its advantages. The FAHP has the advantage of determining the weight of the criteria hierarchically. The F-TOPSIS and FSAW methods were used to sort and evaluate alternatives based on the set criteria. The study’s results showed that of the three methods used, Raspberry Pi is the best alternative, followed by NVIDIA Jetson and Arduino. The findings showed that Raspberry Pi excelled in navigation accuracy and processing speed, NVIDIA Jetson excelled in reliability, and Arduino excelled in power consumption and compatibility. Fuzzy MCDM is an appropriate method to use to select embedded systems for autonomous navigated light vehicles.
How to Cite
Downloads
##plugins.themes.bootstrap3.article.details##
autonomous vehicle, fuzzy multi-criteria decision making, navigation accuracy, processing speed, power consumption, compatibility, reliability
Afrane, S., Ampah, J. D., Agyekum, E. B., Amoh, P. O., Yusuf, A. A., Fattah, I. M. R., Agbozo, E., Elgamli, E., Shouran, M., Mao, G., & Kamel, S. (2022). Integrated AHP-TOPSIS under a fuzzy environment for the selection of waste-to-energy technologies in Ghana: A performance analysis and socio-enviro-economic feasibility study. International Journal of Environmental Research and Public Health, 19(14), 8428. https://doi.org/10.3390/ijerph19148428
al-Sulbi, K., Chaurasia, P. K., Attaallah, A., Agrawal, A., Pandey, D., Verma, V. R., Kumar, V., & Ansari, M. T. J. (2023). A Fuzzy TOPSIS-based approach for comprehensive evaluation of bio-medical waste management: Advancing sustainability and decision-making sustainability, Sustainability, 15(16), 12565. https://doi.org/10.3390/su151612565
Anandhalli, M., & Baligar, V. P. (2018). A novel approach in real-time vehicle detection and tracking using Raspberry Pi. Alexandria Engineering Journal, 57(3), 1597–1607. https://doi.org/10.1016/j.aej.2017.06.008
Brown, N. E., Rojas, J. F., Goberville, N. A., Alzubi, H., AlRousan, Q., Wang, C., Huff, S., Rios-Torres, J., Ekti, A. R., LaClair, T. J., Meyer, R., & Asher, Z. D. (2022). Development of an energy efficient and cost effective autonomous vehicle research platform. Sensors, 22(16), 5999. https://doi.org/10.3390/s22165999
Chaudhary, S., Sharma, A., Khichar, S., Meng, Y., & Malhotra, J. (2024). Enhancing autonomous vehicle navigation using SVM-based multi-target detection with photonic radar in complex traffic scenarios. Scientific Reports, 14(1), 17339. https://doi.org/10.1038/s41598-024-66850-z
Chen, T.-Y. (2012). Comparative analysis of SAW and TOPSIS based on interval-valued fuzzy sets: Discussions on score functions and weight constraints. Expert Systems with Applications, 39(2), 1848–1861. https://doi.org/10.1016/j.eswa.2011.08.065
Chou, S.-Y., Chang, Y.-H., & Shen, C.-Y. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189(1), 132–145. https://doi.org/10.1016/j.ejor.2007.05.006
Dogan, O., Deveci, M., Canıtez, F., & Kahraman, C. (2020). A corridor selection for locating autonomous vehicles using an interval-valued intuitionistic fuzzy AHP and TOPSIS method. Soft Computing, 24(12), 8937–8953. https://doi.org/10.1007/s00500-019-04421-5
Fodor, J. & de Baets, B. (2008). Fuzzy preference modelling: Fundamentals and recent advances. In (H. Bustince, F. Herrera, & J. Montero, Eds.) Fuzzy sets and their extensions: Representation, aggregation and models, Vol. 220 (pp. 207-217). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-73723-0
Giannaros, A., Karras, A., Theodorakopoulos, L., Karras, C., Kranias, P., Schizas, N., Kalogeratos, G., & Tsolis, D. (2023). Autonomous vehicles: Sophisticated attacks, safety issues, challenges, open topics, blockchain, and future directions. Journal of Cybersecurity and Privacy, 3(3), 493–543. https://doi.org/10.3390/jcp3030025
Hamadneh, J., Duleba, S., & Esztergár-Kiss, D. (2022). Stakeholder viewpoints analysis of the autonomous vehicle industry by using multi-actors multi-criteria analysis. Transport Policy, 126, 65–84. https://doi.org/10.1016/j.tranpol.2022.07.005
Hosny, K. M., Magdi, A., Salah, A., El-Komy, O., & Lashin, N. A. (2023). Internet of things applications using Raspberry-Pi: a survey. International Journal of Electrical and Computer Engineering, 13(1), 902. https://doi.org/10.11591/ijece.v13i1.pp902-910
Hsieh, T.-Y., Lu, S.-T., & Tzeng, G.-H. (2004). Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International Journal of Project Management, 22(7), 573–584. https://doi.org/10.1016/j.ijproman.2004.01.002
Kabassi, K., Karydis, C., & Botonis, A. (2020). AHP, Fuzzy SAW, and Fuzzy WPM for the evaluation of cultural websites. Multimodal Technologies and Interaction, 4(1), 5. https://doi.org/10.3390/mti4010005
Kondaveeti, H. K., Kumaravelu, N. K., Vanambathina, S. D., Mathe, S. E., & Vappangi, S. (2021a). A systematic literature review on prototyping with Arduino: Applications, challenges, advantages, and limitations. Computer Science Review, 40, 100364. https://doi.org/10.1016/j.cosrev.2021.100364
Kondaveeti, H. K., Kumaravelu, N. K., Vanambathina, S. D., Mathe, S. E., & Vappangi, S. (2021b). A systematic literature review on prototyping with Arduino: Applications, challenges, advantages, and limitations. Computer Science Review, 40, 100364. https://doi.org/10.1016/j.cosrev.2021.100364
Kortli, Y., Gabsi, S., Voon, L. F. C. L. Y., Jridi, M., Merzougui, M., & Atri, M. (2022). Deep embedded hybrid CNN–LSTM network for lane detection on NVIDIA Jetson Xavier NX. Knowledge-Based Systems, 240, 107941. https://doi.org/10.1016/j.knosys.2021.107941
Lam Loong Man, C. K. Y., Koonjul, Y., & Nagowah, L. (2018). A low cost autonomous unmanned ground vehicle. Future Computing and Informatics Journal, 3(2), 304–320. https://doi.org/10.1016/j.fcij.2018.10.001
Lewis, A. J., Campbell, M., & Stavroulakis, P. (2016). Performance evaluation of a cheap, open source, digital environmental monitor based on the Raspberry Pi. Measurement, 87, 228–235. https://doi.org/10.1016/j.measurement.2016.03.023
Li, Y., & Zheng, Y. R. (2020). Profiling NVIDIA Jetson embedded GPU devices for autonomous machines. Computer Science & Information Technology, 133–144. https://doi.org/10.5121/csit.2020.101811
Massimo Banzi, & Shiloh, M. (2020). Getting started with ARDUINO: The open source electronics prototypying platform, 4th edition. Make Community, LLC.
Mudaliar, M. D., & Sivakumar, N. (2020). IoT based real time energy monitoring system using Raspberry Pi. Internet of Things, 12, 100292. https://doi.org/10.1016/j.iot.2020.100292
Oltean, S.-E. (2019). Mobile robot platform with Arduino Uno and Raspberry Pi for autonomous navigation. Procedia Manufacturing, 32, 572–577. https://doi.org/10.1016/j.promfg.2019.02.254
Patil, S. K., & Kant, R. (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of knowledge management adoption in supply chain to overcome its barriers. Expert Systems with Applications, 41(2), 679–693. https://doi.org/10.1016/j.eswa.2013.07.093
Peng, G., Han, L., Liu, Z., Guo, Y., Yan, J., & Jia, X. (2021). An application of fuzzy Analytic Hierarchy Process in risk evaluation model. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.715003
Roszkowska, E., & Kacprzak, D. (2016). The fuzzy saw and fuzzy TOPSIS procedures based on ordered fuzzy numbers. Information Sciences, 369, 564–584. https://doi.org/10.1016/j.ins.2016.07.044
Sethi, P., & Sarangi, S. R. (2017). Internet of things: Architectures, protocols, and applications. Journal of Electrical and Computer Engineering, 2017, 1–25. https://doi.org/10.1155/2017/9324035
Shahedi, A., Dadashpour, I., & Rezaei, M. (2023). Barriers to the sustainable adoption of autonomous vehicles in developing countries: A multi-criteria decision-making approach. Heliyon, 9(5), e15975. https://doi.org/10.1016/j.heliyon.2023.e15975
Singh, R., Gehlot, A., Raj Gupta, L., Singh, B., & Swain, M. (2020). Internet of things with Raspberry Pi and Arduino. Routledge.
Suder, J., Podbucki, K., & Marciniak, T. (2023). Power requirements evaluation of embedded devices for real-time video line detection. Energies, 16(18), 6677. https://doi.org/10.3390/en16186677
Tran, N.-T., Trinh, V.-L., & Chung, C.-K. (2024). An integrated approach of fuzzy AHP-TOPSIS for multi-criteria decision-making in industrial robot selection. Processes, 12(8), 1723. https://doi.org/10.3390/pr12081723
Veeramanickam, M. R. M., Venkatesh, B., Bewoor, L. A., Bhowte, Y. W., Moholkar, K., & Bangare, J. L. (2022). IoT based smart parking model using Arduino UNO with FCFS priority scheduling. Measurement: Sensors, 24, 100524. https://doi.org/10.1016/j.measen.2022.100524
Copyright of all articles published in IJAHP is transferred to Creative Decisions Foundation (CDF). However, the author(s) reserve the following:
- All proprietary rights other than copyright, such as patent rights.
- The right to grant or refuse permission to third parties to republish all or part of the article or translations thereof. In case of whole articles, such third parties must obtain permission from CDF as well. However, CDF may grant rights with respect to journal issues as a whole.
- The right to use all or parts of this article in future works of their own, such as lectures, press releases, reviews, textbooks, or reprint books.
- The authors affirm that the article has been neither copyrighted nor published, that it is not being submitted for publication elsewhere, and that if the work is officially sponsored, it has been released for open publication.
The only exception to the statements in the paragraph above is the following: If an article published in IJAHP contains copyrighted material, such as a teaching case, as an appendix, then the copyright (and all commercial rights) of such material remains with the original copyright holder.
CDF will receive permission for publication of copyrighted material in IJAHP. This permission is not transferable to third parties. Permission to make electronic and paper copies of part or all of the articles, including all computer files that are linked to the articles, for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage.
This permission does not apply to previously copyrighted material, such as teaching cases. In paper copies of the article, the copyright notice and the title of the publication and its date should be visible. To copy otherwise is permitted provided that a per-copy fee is paid.
To republish, to post on servers, or redistribute to lists requires that you post a link to the IJAHP article, which is available in open access delivery mode. Do not upload the article itself.
Authors are permitted to present a talk, based on a paper submitted to or accepted by IJAHP, at a conference where the paper would not be published in a copyrighted publication either before or after the conference and where the author did not assign copyright to the conference or related publisher.