Dingo Optimization Based Cluster Based Routing in Internet of Things
The Wireless Sensor Network (WSN) is a collection of distinct, geographically distributed, Internet-connected sensors, which is capable of processing, analyzing, storing, and exchanging collected information. However, the Internet of Things (IoT) devices in the network are equipped with limited reso...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/20/8064 |
| _version_ | 1797469937380360192 |
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| author | Kalavagunta Aravind Praveen Kumar Reddy Maddikunta |
| author_facet | Kalavagunta Aravind Praveen Kumar Reddy Maddikunta |
| author_sort | Kalavagunta Aravind |
| collection | DOAJ |
| description | The Wireless Sensor Network (WSN) is a collection of distinct, geographically distributed, Internet-connected sensors, which is capable of processing, analyzing, storing, and exchanging collected information. However, the Internet of Things (IoT) devices in the network are equipped with limited resources and minimal computing capability, resulting in energy conservation problems. Although clustering is an efficient method for energy saving in network nodes, the existing clustering algorithms are not effective due to the short lifespan of a network, an unbalanced load among the network nodes, and increased end-to-end delays. Hence, this paper proposes a novel cluster-based approach for IoT using a Self-Adaptive Dingo Optimizer with Brownian Motion (SDO-BM) technique to choose the optimal cluster head (CH) considering the various constraints such as energy, distance, delay, overhead, trust, Quality of Service (QoS), and security (high risk, low risk, and medium risk). If the chosen optimal CH is defective, then fault tolerance and energy hole mitigation techniques are used to stabilize the network. Eventually, analysis is done to ensure the progression of the SADO-BM model. The proposed model provides optimal results compared to existing models. |
| first_indexed | 2024-03-09T19:30:00Z |
| format | Article |
| id | doaj.art-b28790262bd14fa4a044f5bf042e8045 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:30:00Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b28790262bd14fa4a044f5bf042e80452023-11-24T02:31:19ZengMDPI AGSensors1424-82202022-10-012220806410.3390/s22208064Dingo Optimization Based Cluster Based Routing in Internet of ThingsKalavagunta Aravind0Praveen Kumar Reddy Maddikunta1School of Information Technology and Engineering, Vellore Institute of Technology and Engineering, Vellore 632014, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology and Engineering, Vellore 632014, IndiaThe Wireless Sensor Network (WSN) is a collection of distinct, geographically distributed, Internet-connected sensors, which is capable of processing, analyzing, storing, and exchanging collected information. However, the Internet of Things (IoT) devices in the network are equipped with limited resources and minimal computing capability, resulting in energy conservation problems. Although clustering is an efficient method for energy saving in network nodes, the existing clustering algorithms are not effective due to the short lifespan of a network, an unbalanced load among the network nodes, and increased end-to-end delays. Hence, this paper proposes a novel cluster-based approach for IoT using a Self-Adaptive Dingo Optimizer with Brownian Motion (SDO-BM) technique to choose the optimal cluster head (CH) considering the various constraints such as energy, distance, delay, overhead, trust, Quality of Service (QoS), and security (high risk, low risk, and medium risk). If the chosen optimal CH is defective, then fault tolerance and energy hole mitigation techniques are used to stabilize the network. Eventually, analysis is done to ensure the progression of the SADO-BM model. The proposed model provides optimal results compared to existing models.https://www.mdpi.com/1424-8220/22/20/8064IoThealthcarefault toleranceenergy holeSADO-BM scheme |
| spellingShingle | Kalavagunta Aravind Praveen Kumar Reddy Maddikunta Dingo Optimization Based Cluster Based Routing in Internet of Things Sensors IoT healthcare fault tolerance energy hole SADO-BM scheme |
| title | Dingo Optimization Based Cluster Based Routing in Internet of Things |
| title_full | Dingo Optimization Based Cluster Based Routing in Internet of Things |
| title_fullStr | Dingo Optimization Based Cluster Based Routing in Internet of Things |
| title_full_unstemmed | Dingo Optimization Based Cluster Based Routing in Internet of Things |
| title_short | Dingo Optimization Based Cluster Based Routing in Internet of Things |
| title_sort | dingo optimization based cluster based routing in internet of things |
| topic | IoT healthcare fault tolerance energy hole SADO-BM scheme |
| url | https://www.mdpi.com/1424-8220/22/20/8064 |
| work_keys_str_mv | AT kalavaguntaaravind dingooptimizationbasedclusterbasedroutingininternetofthings AT praveenkumarreddymaddikunta dingooptimizationbasedclusterbasedroutingininternetofthings |