Link Quality Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model for Wireless Sensor Networks
A core factor to consider when designing wireless sensor networks is the reliable and efficient transmission of massive data from source to destination. In practical situations, data transmission is often disrupted by link interference and interruption resulting in the data losses. Link quality pred...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-10-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/828493 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559164507815936 |
|---|---|
| author | Yuxin Zhao Shenghong Li Jia Hou |
| author_facet | Yuxin Zhao Shenghong Li Jia Hou |
| author_sort | Yuxin Zhao |
| collection | DOAJ |
| description | A core factor to consider when designing wireless sensor networks is the reliable and efficient transmission of massive data from source to destination. In practical situations, data transmission is often disrupted by link interference and interruption resulting in the data losses. Link quality prediction is an important approach to solve this problem. By estimating the link quality based on the past knowledge and information, link quality prediction is essential for routing decisions of future data transmission. Traditional link quality prediction algorithms are simply based on the statistical information of the links in the wireless sensor network. By introducing complex network theory and machine learning techniques, we propose a neighborhood-based nonnegative matrix factorization model to predict link quality in wireless sensor networks. Our model learns latent features of the nodes from the information of past data transmissions combing with local neighborhood structures of the underlying network topology and then estimates the link quality depending on the common latent features of the two nodes between the link. Extensive experiments on both real-world networks and simulation networks demonstrate the effectiveness and efficiency of our proposed model. |
| format | Article |
| id | doaj-art-b115ab02e5934ecba42b3a5178ec0a47 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-b115ab02e5934ecba42b3a5178ec0a472025-02-03T01:30:41ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/828493828493Link Quality Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model for Wireless Sensor NetworksYuxin Zhao0Shenghong Li1Jia Hou2 Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China School of Electronic and Information Engineering, Soochow University, Suzhou 215006, ChinaA core factor to consider when designing wireless sensor networks is the reliable and efficient transmission of massive data from source to destination. In practical situations, data transmission is often disrupted by link interference and interruption resulting in the data losses. Link quality prediction is an important approach to solve this problem. By estimating the link quality based on the past knowledge and information, link quality prediction is essential for routing decisions of future data transmission. Traditional link quality prediction algorithms are simply based on the statistical information of the links in the wireless sensor network. By introducing complex network theory and machine learning techniques, we propose a neighborhood-based nonnegative matrix factorization model to predict link quality in wireless sensor networks. Our model learns latent features of the nodes from the information of past data transmissions combing with local neighborhood structures of the underlying network topology and then estimates the link quality depending on the common latent features of the two nodes between the link. Extensive experiments on both real-world networks and simulation networks demonstrate the effectiveness and efficiency of our proposed model.https://doi.org/10.1155/2015/828493 |
| spellingShingle | Yuxin Zhao Shenghong Li Jia Hou Link Quality Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model for Wireless Sensor Networks International Journal of Distributed Sensor Networks |
| title | Link Quality Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model for Wireless Sensor Networks |
| title_full | Link Quality Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model for Wireless Sensor Networks |
| title_fullStr | Link Quality Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model for Wireless Sensor Networks |
| title_full_unstemmed | Link Quality Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model for Wireless Sensor Networks |
| title_short | Link Quality Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model for Wireless Sensor Networks |
| title_sort | link quality prediction via a neighborhood based nonnegative matrix factorization model for wireless sensor networks |
| url | https://doi.org/10.1155/2015/828493 |
| work_keys_str_mv | AT yuxinzhao linkqualitypredictionviaaneighborhoodbasednonnegativematrixfactorizationmodelforwirelesssensornetworks AT shenghongli linkqualitypredictionviaaneighborhoodbasednonnegativematrixfactorizationmodelforwirelesssensornetworks AT jiahou linkqualitypredictionviaaneighborhoodbasednonnegativematrixfactorizationmodelforwirelesssensornetworks |