| Title |
Relationship LSTM Network for Prediction in Social Internet of Things |
| ID_Doc |
44140 |
| Authors |
Mohana, SD; Prakash, SPS; Krinkin, K |
| Title |
Relationship LSTM Network for Prediction in Social Internet of Things |
| Year |
2023 |
| Published |
|
| DOI |
10.1007/978-981-19-6581-4_11 |
| Abstract |
Hyperscaling of IoT sensors leads to the connected device segments like smart phone, smart watches, smart home, smart city, and many more. These hyperscaling devices which are in a social relationship form a social IoT. The social relationship is based on object relationship in a smart environment. There are many challenges in social IoT, the major being object mobility, scalability, and pattern analysis in a smart environment. The smart objects have limited intelligence to take decision to predict the corresponding data. This work focuses on data of smart city environment to provide the services to the user in a given environment. The intelligence in the model using R-LSTM network is to determine the right data and predicting responding objects and relationship between the objects. The proposed work provides accuracy of 98.75% and loss of 0.37% to the SIoT smart environment. |
| Author Keywords |
SIoT-social internet of things; LSTM-long short-time memory network; Ml-machine learning; IoT-internet of things |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000925099300011 |
| WoS Category |
Computer Science, Artificial Intelligence |
| Research Area |
Computer Science |
| PDF |
|