Multiobjectives for Optimal Geographic Routing in IoT Health Care System
In numerous internet of things (IoT) appliances, messages might require to be distributed to certain specified nodes or objects with the multicast transmission. “The multicast routing protocol can be divided into nongeographic based and geographic based.” As locations of device are roughly extracted...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/7568804 |
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| author | K. Aravind Praveen Kumar Reddy Maddikunta |
| author_facet | K. Aravind Praveen Kumar Reddy Maddikunta |
| author_sort | K. Aravind |
| collection | DOAJ |
| description | In numerous internet of things (IoT) appliances, messages might require to be distributed to certain specified nodes or objects with the multicast transmission. “The multicast routing protocol can be divided into nongeographic based and geographic based.” As locations of device are roughly extracted by GPS devices, geographic-oriented multicast routing schemes were chosen, because it induces lesser overheads. Nevertheless, the extant geographic-oriented routing models are found to have particular disadvantages. After the advent of the IoT systems for remote healthcare, medical services can be rapidly provided to patients in rural areas. The IoT network encapsulates flexible sensors in the environment to collect environmental information. This gathered sensor information is sent to the nursing stations for timely medical assistance. The IoT network is wireless, which leads to security breaches. Therefore, there is a necessity to have a secured data transmission in the context of healthcare. Hence, this study intends to propose a novel optimal route selection model in IoT healthcare by deploying optimized ANFIS. Here, the optimal routes for medical data are selected using a new self-adaptive jellyfish search optimizer (SA-JSO) that is the enhanced edition of the extant JSO model. Accordingly, the optimal route selection for medical data is performed under the consideration of “energy, distance, delay, overhead, trust, quality of service (QoS), and security (high risk, low risk, and medium risk).” In the end, the performances of adopted work are compared and proved over other extant schemes. |
| format | Article |
| id | doaj-art-9529db955c314250af6d9b71f1b9e417 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9529db955c314250af6d9b71f1b9e4172025-02-03T01:08:45ZengWileyComplexity1099-05262022-01-01202210.1155/2022/7568804Multiobjectives for Optimal Geographic Routing in IoT Health Care SystemK. Aravind0Praveen Kumar Reddy Maddikunta1School of Information Technology and EngineeringSchool of Information Technology and EngineeringIn numerous internet of things (IoT) appliances, messages might require to be distributed to certain specified nodes or objects with the multicast transmission. “The multicast routing protocol can be divided into nongeographic based and geographic based.” As locations of device are roughly extracted by GPS devices, geographic-oriented multicast routing schemes were chosen, because it induces lesser overheads. Nevertheless, the extant geographic-oriented routing models are found to have particular disadvantages. After the advent of the IoT systems for remote healthcare, medical services can be rapidly provided to patients in rural areas. The IoT network encapsulates flexible sensors in the environment to collect environmental information. This gathered sensor information is sent to the nursing stations for timely medical assistance. The IoT network is wireless, which leads to security breaches. Therefore, there is a necessity to have a secured data transmission in the context of healthcare. Hence, this study intends to propose a novel optimal route selection model in IoT healthcare by deploying optimized ANFIS. Here, the optimal routes for medical data are selected using a new self-adaptive jellyfish search optimizer (SA-JSO) that is the enhanced edition of the extant JSO model. Accordingly, the optimal route selection for medical data is performed under the consideration of “energy, distance, delay, overhead, trust, quality of service (QoS), and security (high risk, low risk, and medium risk).” In the end, the performances of adopted work are compared and proved over other extant schemes.http://dx.doi.org/10.1155/2022/7568804 |
| spellingShingle | K. Aravind Praveen Kumar Reddy Maddikunta Multiobjectives for Optimal Geographic Routing in IoT Health Care System Complexity |
| title | Multiobjectives for Optimal Geographic Routing in IoT Health Care System |
| title_full | Multiobjectives for Optimal Geographic Routing in IoT Health Care System |
| title_fullStr | Multiobjectives for Optimal Geographic Routing in IoT Health Care System |
| title_full_unstemmed | Multiobjectives for Optimal Geographic Routing in IoT Health Care System |
| title_short | Multiobjectives for Optimal Geographic Routing in IoT Health Care System |
| title_sort | multiobjectives for optimal geographic routing in iot health care system |
| url | http://dx.doi.org/10.1155/2022/7568804 |
| work_keys_str_mv | AT karavind multiobjectivesforoptimalgeographicroutinginiothealthcaresystem AT praveenkumarreddymaddikunta multiobjectivesforoptimalgeographicroutinginiothealthcaresystem |