Channel autocorrelation-based dynamic slot scheduling for body area networks
Abstract As a promising technology in the context of m-health and e-medical, wireless body area networks (WBANs) have a stringent requirement in terms of transmission reliability. Meanwhile, the wireless channel in WBANs is prone to deep fading due to multiple reasons, such as shadowing by the body,...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2018-10-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13638-018-1261-8 |
| _version_ | 1819168977224466432 |
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| author | Hongyun Zhang Farzad Safaei Le Chung Tran |
| author_facet | Hongyun Zhang Farzad Safaei Le Chung Tran |
| author_sort | Hongyun Zhang |
| collection | DOAJ |
| description | Abstract As a promising technology in the context of m-health and e-medical, wireless body area networks (WBANs) have a stringent requirement in terms of transmission reliability. Meanwhile, the wireless channel in WBANs is prone to deep fading due to multiple reasons, such as shadowing by the body, reflection, diffraction, and interference. To meet the challenge in transmission reliability, the dynamic slot scheduling (DSS) methods have attracted considerable interest in recent years. DSS method does not require extra hardware or software overhead on the sensor side. Instead, the hub optimizes the time-division multiple access slots by selecting the best permutation at the beginning of each superframe to improve the transmission reliability. However, most existing DSS works optimize the time slot scheduling based on a two-state (“good” or “bad”) Markov channel model, which is insufficient for human daily life scenarios with a variety of irregular activities. In this paper, we first collect the channel gain data in the real human daily scenarios and analyze the autocorrelation of wireless channels based on this real database. Motivated by the significant temporal autocorrelation, we then propose a new DSS method, which applies a temporal autocorrelation model to predict the channel condition for future time slots. The new method is designed to be compatible with IEEE 802.15.6 standard. Compared to the classical Markov model-based methods, simulation results show that the newly proposed DSS method achieves up to 12.9% reduction in terms of packet loss ratios. |
| first_indexed | 2024-12-22T19:12:10Z |
| format | Article |
| id | doaj.art-c47297bd6d8143f99a743727f9795bc7 |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-12-22T19:12:10Z |
| publishDate | 2018-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-c47297bd6d8143f99a743727f9795bc72022-12-21T18:15:38ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992018-10-012018111710.1186/s13638-018-1261-8Channel autocorrelation-based dynamic slot scheduling for body area networksHongyun Zhang0Farzad Safaei1Le Chung Tran2School of Electrical, Computer and Telecommunications Engineering, University of WollongongSchool of Electrical, Computer and Telecommunications Engineering, University of WollongongSchool of Electrical, Computer and Telecommunications Engineering, University of WollongongAbstract As a promising technology in the context of m-health and e-medical, wireless body area networks (WBANs) have a stringent requirement in terms of transmission reliability. Meanwhile, the wireless channel in WBANs is prone to deep fading due to multiple reasons, such as shadowing by the body, reflection, diffraction, and interference. To meet the challenge in transmission reliability, the dynamic slot scheduling (DSS) methods have attracted considerable interest in recent years. DSS method does not require extra hardware or software overhead on the sensor side. Instead, the hub optimizes the time-division multiple access slots by selecting the best permutation at the beginning of each superframe to improve the transmission reliability. However, most existing DSS works optimize the time slot scheduling based on a two-state (“good” or “bad”) Markov channel model, which is insufficient for human daily life scenarios with a variety of irregular activities. In this paper, we first collect the channel gain data in the real human daily scenarios and analyze the autocorrelation of wireless channels based on this real database. Motivated by the significant temporal autocorrelation, we then propose a new DSS method, which applies a temporal autocorrelation model to predict the channel condition for future time slots. The new method is designed to be compatible with IEEE 802.15.6 standard. Compared to the classical Markov model-based methods, simulation results show that the newly proposed DSS method achieves up to 12.9% reduction in terms of packet loss ratios.http://link.springer.com/article/10.1186/s13638-018-1261-8Dynamic slot schedulingChannel autocorrelationScheduled accessWireless body area networksIEEE 802.15.6 |
| spellingShingle | Hongyun Zhang Farzad Safaei Le Chung Tran Channel autocorrelation-based dynamic slot scheduling for body area networks EURASIP Journal on Wireless Communications and Networking Dynamic slot scheduling Channel autocorrelation Scheduled access Wireless body area networks IEEE 802.15.6 |
| title | Channel autocorrelation-based dynamic slot scheduling for body area networks |
| title_full | Channel autocorrelation-based dynamic slot scheduling for body area networks |
| title_fullStr | Channel autocorrelation-based dynamic slot scheduling for body area networks |
| title_full_unstemmed | Channel autocorrelation-based dynamic slot scheduling for body area networks |
| title_short | Channel autocorrelation-based dynamic slot scheduling for body area networks |
| title_sort | channel autocorrelation based dynamic slot scheduling for body area networks |
| topic | Dynamic slot scheduling Channel autocorrelation Scheduled access Wireless body area networks IEEE 802.15.6 |
| url | http://link.springer.com/article/10.1186/s13638-018-1261-8 |
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