Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/6/2115 |
| _version_ | 1797442414935277568 |
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| author | Khalid Haseeb Amjad Rehman Tanzila Saba Saeed Ali Bahaj Jaime Lloret |
| author_facet | Khalid Haseeb Amjad Rehman Tanzila Saba Saeed Ali Bahaj Jaime Lloret |
| author_sort | Khalid Haseeb |
| collection | DOAJ |
| description | Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT devices are cooperative and allow the collection of unpredictable factors from the observing field. However, the constraint resources of distributed battery-powered sensors decrease the energy efficiency of the IoT network and increase the delay in receiving the network data on users’ devices. It is observed that many solutions are proposed to overcome the energy deficiency in smart applications; though, due to the mobility of the nodes, lots of communication incurs frequent data discontinuity, compromising the data trust. Therefore, this work introduces a D2D multi-criteria learning algorithm for IoT networks using secured sensors, which aims to improve the data exchange without imposing additional costs and data diverting for mobile sensors. Moreover, it reduces the compromising threats in the presence of anonymous devices and increases the trustworthiness of the IoT-enabled communication system with the support of machine learning. The proposed work was tested and analyzed using broad simulation-based experiments and demonstrated the significantly improved performance of the packet delivery ratio by 17%, packet disturbances by 31%, data delay by 22%, energy consumption by 24%, and computational complexity by 37% for realistic network configurations. |
| first_indexed | 2024-03-09T12:42:29Z |
| format | Article |
| id | doaj.art-0dea1f5530bb4ad5954a304f7ed3c955 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:42:29Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0dea1f5530bb4ad5954a304f7ed3c9552023-11-30T22:16:26ZengMDPI AGSensors1424-82202022-03-01226211510.3390/s22062115Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured SensorsKhalid Haseeb0Amjad Rehman1Tanzila Saba2Saeed Ali Bahaj3Jaime Lloret4Department of Computer Science, Islamia College Peshawar, Peshawar 25000, PakistanArtificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi ArabiaArtificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi ArabiaMIS Department College of Business Administration, Prince Sattam Bin Abdulaziz University, Alkharj 16278, Saudi ArabiaInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politenica de Valencia, 46379 Gandia, València, SpainWireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT devices are cooperative and allow the collection of unpredictable factors from the observing field. However, the constraint resources of distributed battery-powered sensors decrease the energy efficiency of the IoT network and increase the delay in receiving the network data on users’ devices. It is observed that many solutions are proposed to overcome the energy deficiency in smart applications; though, due to the mobility of the nodes, lots of communication incurs frequent data discontinuity, compromising the data trust. Therefore, this work introduces a D2D multi-criteria learning algorithm for IoT networks using secured sensors, which aims to improve the data exchange without imposing additional costs and data diverting for mobile sensors. Moreover, it reduces the compromising threats in the presence of anonymous devices and increases the trustworthiness of the IoT-enabled communication system with the support of machine learning. The proposed work was tested and analyzed using broad simulation-based experiments and demonstrated the significantly improved performance of the packet delivery ratio by 17%, packet disturbances by 31%, data delay by 22%, energy consumption by 24%, and computational complexity by 37% for realistic network configurations.https://www.mdpi.com/1424-8220/22/6/2115wireless systemsmobile sensorsD2Dtechnological developmentInternet of things |
| spellingShingle | Khalid Haseeb Amjad Rehman Tanzila Saba Saeed Ali Bahaj Jaime Lloret Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors Sensors wireless systems mobile sensors D2D technological development Internet of things |
| title | Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors |
| title_full | Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors |
| title_fullStr | Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors |
| title_full_unstemmed | Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors |
| title_short | Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors |
| title_sort | device to device d2d multi criteria learning algorithm using secured sensors |
| topic | wireless systems mobile sensors D2D technological development Internet of things |
| url | https://www.mdpi.com/1424-8220/22/6/2115 |
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