Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks
With the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population’s demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent “social nodes”, and a large number of social nodes are combined to form...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/10/1669 |
| _version_ | 1797498149718196224 |
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| author | Yedong Shen Fangfang Gou Jia Wu |
| author_facet | Yedong Shen Fangfang Gou Jia Wu |
| author_sort | Yedong Shen |
| collection | DOAJ |
| description | With the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population’s demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent “social nodes”, and a large number of social nodes are combined to form a new “opportunistic social network”. In this network, a large amount of data will be transmitted and the efficiency of data transmission is low. At the same time, the existence of “malicious nodes” in the opportunistic social network will cause problems of unstable data transmission and leakage of user privacy. In the information society, these problems will have a great impact on data transmission and data security; therefore, in order to solve the above problems, this paper first divides the nodes into “community divisions”, and then proposes a more effective node selection algorithm, i.e., the FL node selection algorithm based on Distributed Proximal Policy Optimization in IoT (FABD) algorithm, based on Federated Learning (FL). The algorithm is mainly divided into two processes: multi-threaded interaction and a global network update. The device node selection problem in federated learning is constructed as a Markov decision process. It takes into account the training quality and efficiency of heterogeneous nodes and optimizes it according to the distributed near-end strategy. At the same time, malicious nodes are screened to ensure the reliability of data, prevent data loss, and alleviate the problem of user privacy leakage. Through experimental simulation, compared with other algorithms, the FABD algorithm has a higher delivery rate and lower data transmission delay and significantly improves the reliability of data transmission. |
| first_indexed | 2024-03-10T03:29:21Z |
| format | Article |
| id | doaj.art-d72d98b183ff4d5f8c71931bb483e424 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T03:29:21Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-d72d98b183ff4d5f8c71931bb483e4242023-11-23T12:00:41ZengMDPI AGMathematics2227-73902022-05-011010166910.3390/math10101669Node Screening Method Based on Federated Learning with IoT in Opportunistic Social NetworksYedong Shen0Fangfang Gou1Jia Wu2School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaWith the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population’s demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent “social nodes”, and a large number of social nodes are combined to form a new “opportunistic social network”. In this network, a large amount of data will be transmitted and the efficiency of data transmission is low. At the same time, the existence of “malicious nodes” in the opportunistic social network will cause problems of unstable data transmission and leakage of user privacy. In the information society, these problems will have a great impact on data transmission and data security; therefore, in order to solve the above problems, this paper first divides the nodes into “community divisions”, and then proposes a more effective node selection algorithm, i.e., the FL node selection algorithm based on Distributed Proximal Policy Optimization in IoT (FABD) algorithm, based on Federated Learning (FL). The algorithm is mainly divided into two processes: multi-threaded interaction and a global network update. The device node selection problem in federated learning is constructed as a Markov decision process. It takes into account the training quality and efficiency of heterogeneous nodes and optimizes it according to the distributed near-end strategy. At the same time, malicious nodes are screened to ensure the reliability of data, prevent data loss, and alleviate the problem of user privacy leakage. Through experimental simulation, compared with other algorithms, the FABD algorithm has a higher delivery rate and lower data transmission delay and significantly improves the reliability of data transmission.https://www.mdpi.com/2227-7390/10/10/1669opportunistic social networkfederated learningcommunity restructuringInternet of Thingsdeep reinforcement learningmobile edge computing |
| spellingShingle | Yedong Shen Fangfang Gou Jia Wu Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks Mathematics opportunistic social network federated learning community restructuring Internet of Things deep reinforcement learning mobile edge computing |
| title | Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks |
| title_full | Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks |
| title_fullStr | Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks |
| title_full_unstemmed | Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks |
| title_short | Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks |
| title_sort | node screening method based on federated learning with iot in opportunistic social networks |
| topic | opportunistic social network federated learning community restructuring Internet of Things deep reinforcement learning mobile edge computing |
| url | https://www.mdpi.com/2227-7390/10/10/1669 |
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