基于貝葉斯網(wǎng)絡(luò)的無(wú)線傳感網(wǎng)高效數(shù)據(jù)傳輸方法

劉廣怡; 李鷗; 宋濤; 孔范增

doi:10.11999/JEIT151027

基于貝葉斯網(wǎng)絡(luò)的無(wú)線傳感網(wǎng)高效數(shù)據(jù)傳輸方法

doi: 10.11999/JEIT151027

基金項(xiàng)目:

國(guó)家科技重大專(zhuān)項(xiàng)(2014ZX03006003)

計(jì)量
- 文章訪問(wèn)數(shù): 1614
- HTML全文瀏覽量: 160
- PDF下載量: 602
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-07-10
- 修回日期: 2016-02-25
- 刊出日期: 2016-06-19

Energy-efficiency Data Transmission Method in WSN Based on Bayesian Network

Funds:

The National Science and Technology Major Projects of China (2014ZX03006003)

摘要

摘要: 無(wú)線環(huán)境復(fù)雜經(jīng)常導(dǎo)致高誤碼率的出現(xiàn)，該文結(jié)合無(wú)線傳感網(wǎng)對(duì)傳輸能耗有較高要求的特點(diǎn)，針對(duì)分組協(xié)議字段錯(cuò)誤修復(fù)問(wèn)題提出基于貝葉斯網(wǎng)絡(luò)的最大后驗(yàn)修復(fù)方法MAP-BN。該方法使得傳感網(wǎng)節(jié)點(diǎn)在無(wú)需任何編碼的情況下可以得到向前糾錯(cuò)的能力。MAP-BN算法利用貝葉斯網(wǎng)絡(luò)對(duì)分組協(xié)議字段的先驗(yàn)信息進(jìn)行建模，并在此基礎(chǔ)上利用動(dòng)態(tài)規(guī)劃算法進(jìn)行最大后驗(yàn)概率推理，成功降低了最大后驗(yàn)修復(fù)的計(jì)算復(fù)雜度。仿真和分析結(jié)果表明，MAP-BN算法具有良好的數(shù)據(jù)差錯(cuò)控制能力，并可以很大程度上提升網(wǎng)絡(luò)節(jié)點(diǎn)傳輸數(shù)據(jù)的能效性。
- 無(wú)線傳感網(wǎng) /
- 貝葉斯網(wǎng)絡(luò) /
- 差錯(cuò)控制 /
- 能量有效性
Abstract: Complexity of wireless environment often poses high bit error problems. For that reason, and also concerning about the great demands on transmission energy consumption for the WSN, a Maximum A Posteriori method based on Bayesian Network (MAP-BN) is proposed for fixing the wrong protocol fields in packets, which is also according to the transmission characteristics of WSN. MAP-BN makes the nodes in WSN have the ability of forward-error-correction without any prior coding before transmitting a packet. To achieve that, Bayesian Network is used to modeling prior information of protocol fields in packets, and Maximum A Posteriori inference which involved a dynamic programming algorithm is used to reduce the computational complexity successfully. The simulation results show that MAP-BN performance is pretty good in the aspect of error control and energy-efficiency.
- Wireless Sensor Networks (WSN) /
- Bayesian Network (BN) /
- Error-control /
- Energy-efficiency

HTML全文

參考文獻(xiàn)(19)

ESTRIN D, GOVINDAN R, and HEIDEMANN J. Next century challenges: Scalable coordination in sensor networks[A]. Washington, USA, 1999: 263-270.

ABOUEI J, EDWARD S, and BROWN J D. On the energy efficiency of LT codes in proactive Wireless Sensor Networks[C]. 2010 25th Biennial Symposium on Communications (QBSC), Kingston, ON. 2010: 114-117.

郭銳, 劉春于, 張華, 等. 分簇?zé)o線傳感器網(wǎng)絡(luò)中根校驗(yàn)全分集LDPC碼設(shè)計(jì)與能效分析[J]. 電子與信息學(xué)報(bào), 2015, 37(7): 1580-1585. doi: 10.11999/JEIT141294.

GUO Rui, LIU Chunyu, ZHANG Hua, et al. Full diversity LDPC codes design and energy efficiency analysis for clustering wireless sensor networks[J]. Journal of Electronics Information Technology, 2015, 37(7): 1580-1585. doi: 10.11999/JEIT141294.

DELIGIANNIS N, ZIMOS E, OFRIM D M, et al. A distributed joint source-channel coding with copula- function-based correlation modeling for wireless sensors measuring temperature[J]. IEEE Sensors Journal, 2015, 15(8): 4496-4507.

JING Yue, LIN Zihuai, VUCETIC B, et al. The design of degree distribution for distributed fountain codes in wireless sensor networks[C]. 2014 IEEE International Conference on Communications (ICC), Sydney, 2014: 5796-5801.

SHIRVANIMOGHDDAM M, LI Yonghui , and VUCETIC B. Sparse event detection in wireless sensor networks using analog fountain codes[C]. 2014 IEEE Global Communications Conference (GLOBECOM), Austin, 2014: 3520-3525.

ABDERRAZAK A and El FOULY T M. On the distributed binary consensus algorithm in wireless sensor networks[C]. 2013 7th International Conference on Signal Processing and Communication Systems (ICSPCS), Carrara, 2013: 1-9.

NGUYEN D, LE Quang Vinh Tran, BERDER O, et al. A low-latency and energy-efficient MAC protocol for cooperative wireless sensor networks[C]. 2013 IEEE Global Communications Conference (GLOBECOM), Atlanta, 2013: 3826-3831.

DUHAMEL P and KIFFER M. Joint Source-channel Decoding: a Cross-layer Perspective with Applications in Video Broadcasting[M]. UK, Academic Press, 2009: 193-246.

SCHMID F, ORLEAR D, and WEHRLE K. A heuristic header error recovery scheme for RTP[C]. Proceedings of the Wireless On-demand Network Systems and Services (WONS), Alberta, Canada, 2013: 186-190.

MARIN C, LEPROVOST Y, and KIFFER M. Robust MAC-lite and soft header recovery for packetized multimedia transmission[J]. IEEE Transactions on Communications, 2010, 58(3): 775-782.

MERIAUX F and KIFFER M. Robust IP and UDP-lite header recovery for packetized multimedia transmission[C]. Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Texas, USA, 2010: 2358-2361.

王曉梅, 范亮, 陳彥, 等. 一種基于子集約束的協(xié)議首部糾錯(cuò)算法[J]. 電子與信息學(xué)報(bào), 2015, 37(8): 2014-2020. doi: 10.11999/JEIT141574.

WANG Xiaomei, FAN Liang, CHEN Yan, et al. Header recovery algorithm based on subset constraint[J]. Journal of Electronics Information Technology, 2015, 37(8): 2014-2020. doi: 10.11999/JEIT141574.

GAUVAIN J and LEE Chinhui. Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains[J]. IEEE Transactions on Speech and Audio Processing, 1994, 2(2): 291-298.

BEN MRAD A, DELCROIX V, PIECHOWIAK S, et al. Understanding soft evidence as probabilistic evidence: Illustration with several use cases[C]. 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), Hammamet, 2013: 1-6.

田真, 袁東風(fēng), 梁泉泉. 傳感器網(wǎng)絡(luò)差錯(cuò)控制技術(shù)的能效分析[J]. 通信學(xué)報(bào), 2008, 29(11): 78-83.

TIAN Zhen, YUAN Dongfeng, and LIANG Quanquan. Comparison of error control schemes in wireless sensor networks[J]. Journal on Communications, 2008, 29(11): 78-83.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問(wèn)統(tǒng)計(jì)