A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding

Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Wireless networks Jg. 26; H. 8; S. 5981 - 5995
Hauptverfasser:	Chen, Chen, Zhang, Limao, Tiong, Robert Lee Kong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2020 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Bayesian analysis Coding Communications Engineering Compression ratio Computer Communication Networks Computing costs Data compression Data storage Data transmission Digital media Electrical Engineering Engineering IT in Business Missing data Networks Predictions Regression models Sensors Statistical analysis Statistical inference Wireless networks Wireless sensor networks Lossy compression Predictive coding Wireless sensor networks Bayesian inference
ISSN:	1022-0038, 1572-8196
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression techniques are generally preferable for sensors in commercial nodes than the lossless ones as they provide a better compression ratio at a lower computational cost. However, the traditional approaches for data compression in WSNs are sensitive to sensor accuracy. They are less efficient when there are abnormal and faulty measurements or missing data. This paper proposes a new lossy compression approach using the Bayesian predictive coding (BPC). Instead of the original signals, predictive coding transmits the error terms which are calculated by subtracting the predicted signals from the actual signals to the receiving node. Its compression performance depends on the accuracy of the adopted prediction technique. BPC combines the Bayesian inference with the predictive coding. Prediction is made by the Bayesian inference instead of regression models as in traditional predictive coding. In this way, it can utilize prior information and provide inferences that are conditional on the data without reliance on asymptotic approximation. Experimental tests show that the BPC is the same efficient as the linear predictive coding when handling independent signals which follow a stationary probability distribution. More than that, the BPC is more robust toward occasionally erroneous or missing sensor data. The proposed approach is based on the physical knowledge of the phenomenon in applications. It can be considered as a complementary approach to the existing lossy compression family for WSNs.
AbstractList	Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression techniques are generally preferable for sensors in commercial nodes than the lossless ones as they provide a better compression ratio at a lower computational cost. However, the traditional approaches for data compression in WSNs are sensitive to sensor accuracy. They are less efficient when there are abnormal and faulty measurements or missing data. This paper proposes a new lossy compression approach using the Bayesian predictive coding (BPC). Instead of the original signals, predictive coding transmits the error terms which are calculated by subtracting the predicted signals from the actual signals to the receiving node. Its compression performance depends on the accuracy of the adopted prediction technique. BPC combines the Bayesian inference with the predictive coding. Prediction is made by the Bayesian inference instead of regression models as in traditional predictive coding. In this way, it can utilize prior information and provide inferences that are conditional on the data without reliance on asymptotic approximation. Experimental tests show that the BPC is the same efficient as the linear predictive coding when handling independent signals which follow a stationary probability distribution. More than that, the BPC is more robust toward occasionally erroneous or missing sensor data. The proposed approach is based on the physical knowledge of the phenomenon in applications. It can be considered as a complementary approach to the existing lossy compression family for WSNs.
Author	Zhang, Limao Tiong, Robert Lee Kong Chen, Chen
Author_xml	– sequence: 1 givenname: Chen surname: Chen fullname: Chen, Chen organization: School of Civil and Environmental Engineering, Nanyang Technological University – sequence: 2 givenname: Limao orcidid: 0000-0002-7245-3741 surname: Zhang fullname: Zhang, Limao email: limao.zhang@ntu.edu.sg organization: School of Civil and Environmental Engineering, Nanyang Technological University – sequence: 3 givenname: Robert Lee Kong surname: Tiong fullname: Tiong, Robert Lee Kong organization: School of Civil and Environmental Engineering, Nanyang Technological University
BookMark	eNp9kEtLAzEQgINUsK3-AU8Bz6uT7Cs51uILCl70HLK7szV1m9Rk69J_b3QFwUMPITPMfDPJNyMT6ywScsngmgGUN4ExXhYJcIgn43kynJApy0ueCCaLSYyB8wQgFWdkFsIGAEQq5ZSoBbU40M6FcKC12-48hmCcpbpbO2_6ty1tnaeD8djFCg1oQ8wt9oPz74Hug7FreqsPGIy2NOKNqXvziXFYE0vn5LTVXcCL33tOXu_vXpaPyer54Wm5WCV1ymSfVLIVVZVjUTLMGMtRijoTopYVsKIAqNK6ydMyjwHTVdkUMi14KZEhZIKzNp2Tq3HuzruPPYZebdze27hS8SyTWQ5S8tjFx67axw97bNXOm632B8VAfYtUo0gVRaofkWqIkPgH1abXfZTUe22642g6oiHusWv0f686Qn0BTvmLXg
CitedBy_id	crossref_primary_10_1109_ACCESS_2021_3060317 crossref_primary_10_3390_s22197685 crossref_primary_10_1109_ACCESS_2024_3476424 crossref_primary_10_1016_j_ins_2022_08_054 crossref_primary_10_1016_j_neucom_2021_07_071 crossref_primary_10_1109_LSENS_2024_3367044
Cites_doi	10.1016/j.is.2016.11.001 10.1109/JRPROC.1952.273898 10.1061/(ASCE)CP.1943-5487.0000721 10.1007/s12065-010-0044-x 10.1007/s11276-017-1570-6 10.1109/PROC.1975.9792 10.1109/TIP.2019.2934565 10.1016/j.compgeo.2017.01.014 10.1109/MCOM.2007.343615 10.1109/INFCOMW.2016.7562104 10.1147/rd.232.0149 10.4102/sajems.v18i4.966 10.1080/02564602.2016.1233835 10.1109/TNNLS.2018.2820019 10.1007/s11276-017-1543-9 10.1016/j.is.2015.10.006 10.1111/risa.12448 10.1109/JSEN.2016.2550599 10.1016/j.ress.2014.10.021 10.1016/j.ssci.2019.08.015 10.1109/TIT.1978.1055934 10.1109/TSP.2012.2201149 10.3390/s90906869 10.1109/COMST.2017.2691551 10.1109/MC.1984.1659158 10.1016/j.phycom.2009.07.001 10.2299/jsp.17.283 10.1016/j.is.2014.06.005 10.1016/j.jnca.2011.03.001 10.1109/ICCircuitsAndSystems.2012.6408307 10.1016/j.future.2016.01.015 10.1155/2013/253257 10.1016/j.ins.2010.01.027 10.1007/s11276-017-1471-8 10.3390/s19204540 10.1145/2990196 10.1016/j.is.2011.03.004 10.1080/01621459.1968.11009335 10.1109/ICC.2010.5502565 10.1109/SENSORCOMM.2009.84 10.1109/WSC.2015.7408498 10.1145/1614320.1614337 10.1109/LCN.2004.72 10.1109/INDIN.2007.4384776 10.1016/j.jksuci.2018.05.006
ContentType	Journal Article
Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020.
Copyright_xml	– notice: Springer Science+Business Media, LLC, part of Springer Nature 2020 – notice: Springer Science+Business Media, LLC, part of Springer Nature 2020.
DBID	AAYXX CITATION 3V. 7SC 7SP 7WY 7WZ 7XB 87Z 88I 8AO 8FD 8FE 8FG 8FK 8FL ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ L.- L7M L~C L~D M0C M2P P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI Q9U
DOI	10.1007/s11276-020-02425-w
DatabaseName	CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts Electronics & Communications Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Science Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni Edition) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials AUTh Library subscriptions: ProQuest Central Business Premium Collection Technology collection ProQuest One Community College ProQuest Central Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Business (OCUL) ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic
DatabaseTitle	CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Pharma Collection ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Science Journals (Alumni Edition) ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) Business Premium Collection (Alumni)
DatabaseTitleList	ABI/INFORM Global (Corporate)
Database_xml	– sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1572-8196
EndPage	5995
ExternalDocumentID	10_1007_s11276_020_02425_w
GrantInformation_xml	– fundername: Start-Up Grant at Nanyang Technological University, Singapore grantid: M4082160.030 – fundername: Ministère de l'Education Nationale, de la Formation professionnelle, de l'Enseignement Supérieur et de la Recherche Scientifique (MA) grantid: M4011971.030
GroupedDBID	-59 -5G -BR -EM -ET -Y2 -~C -~X .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29R 29~ 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5QI 5VS 67Z 6NX 7WY 85S 88I 8AO 8FE 8FG 8FL 8FW 8TC 8UJ 8US 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAHTB AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABGDV ABHFT ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABPEJ ABQBU ABQSL ABSXP ABTAH ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACGOD ACHSB ACHXU ACKNC ACM ACMDZ ACMLO ACOKC ACOMO ACPIV ACREN ACSNA ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADL ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWIH AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGWIL AGWZB AGYKE AHAVH AHBYD AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCEE ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EDO EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GROUPED_ABI_INFORM_RESEARCH GXS H13 HCIFZ HF~ HG5 HG6 HGAVV HMJXF HQYDN HRMNR HVGLF HZ~ I-F I07 I09 IHE IJ- IKXTQ ITG ITH ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6~ KDC KOV KOW KZ1 LAK LLZTM M0C M2P M4Y MA- N2Q NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 Q2X QF4 QM1 QN7 QO4 QOK QOS R4E R89 R9I RHV RIG RNI ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCLPG SCO SCV SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TAE TEORI TN5 TSG TSK TSV TUC TUS U2A U5U UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 W7O WK8 YIN YLTOR Z45 Z7R Z7X Z7Z Z83 Z88 Z8M Z8R Z8T Z8W Z92 ZMTXR ZY4 _50 ~A9 ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AETEA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 7SC 7SP 7XB 8FD 8FK JQ2 L.- L7M L~C L~D PKEHL PQEST PQUKI Q9U
ID	FETCH-LOGICAL-c319t-b9f8bb5e671e4115e98c488c9b016600b3cd53750b31ab7d6936279e1e04821f3
IEDL.DBID	M2P
ISICitedReferencesCount	10
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000547243000002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1022-0038
IngestDate	Wed Nov 05 02:21:54 EST 2025 Sat Nov 29 04:44:22 EST 2025 Tue Nov 18 21:48:47 EST 2025 Fri Feb 21 02:31:34 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	8
Keywords	Lossy compression Predictive coding Wireless sensor networks Bayesian inference
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c319t-b9f8bb5e671e4115e98c488c9b016600b3cd53750b31ab7d6936279e1e04821f3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-7245-3741
PQID	2449450992
PQPubID	26318
PageCount	15
ParticipantIDs	proquest_journals_2449450992 crossref_primary_10_1007_s11276_020_02425_w crossref_citationtrail_10_1007_s11276_020_02425_w springer_journals_10_1007_s11276_020_02425_w
PublicationCentury	2000
PublicationDate	20201100 2020-11-00 20201101
PublicationDateYYYYMMDD	2020-11-01
PublicationDate_xml	– month: 11 year: 2020 text: 20201100
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationSubtitle	The Journal of Mobile Communication, Computation and Information
PublicationTitle	Wireless networks
PublicationTitleAbbrev	Wireless Netw
PublicationYear	2020
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	Welch (CR41) 1984; 17 Pan, Zhang, Wu, Zhang, Skibniewski (CR31) 2019; 120 Sharma (CR36) 2010; 10 Chen, Liu, Wang, Wu (CR6) 2019; 25 Dang, Bulusu, Feng (CR9) 2013 CR35 Li, Liang (CR23) 2016; 12 Marcelloni, Vecchio (CR28) 2010; 180 CR33 Alsheikh, Lin, Niyato, Tan (CR2) 2016; 16 Makhoul (CR27) 1975; 63 Liu, Shimamura (CR24) 2013; 17 Banerjee, Bit (CR3) 2017; 25 Dan, Qian, Zhi, Baoling (CR8) 2016; 2016 Dumas, Roumy, Guillemot (CR13) 2020; 29 Wu, Liu, Zhang, Skibniewski, Deng, Teng (CR43) 2015; 134 Iqbal, Abdullah, Ahsan, Qureshi (CR18) 2017; 24 CR5 Ziv, Lempel (CR50) 1978; 24 Tandon, Huggins, Maclachlan, Dubrawski, Nelson, Labov (CR39) 2016; 57 CR47 Srisooksai, Keamarungsi, Lamsrichan, Araki (CR38) 2012; 35 CR40 Deligiannakis, Kotidis (CR11) 2011; 36 Noel, Abdaoui (CR30) 2017; 19 Rissanen, Langdon (CR34) 1979; 23 Zhang, Wu, Qin, Skibniewski, Liu (CR49) 2016; 36 Hollmig, Horne, Leimkühler, Schöll, Strunk, Englhardt, Efros, Buchmann, Böhm (CR15) 2017; 65 Wheeler (CR42) 2007; 45 Fu, Liang, Lei, Bian, Zhang, Akbari, Liang, Tu (CR14) 2020; 82 CR19 Xu, Chen, Lian, Li (CR45) 2018; 29 Zhan, Chen, Wang, Chen (CR46) 2017; 86 CR12 Kim, Han, Yu, Kim (CR22) 2015; 48 Davenport, Laska, Treichler, Baraniuk (CR10) 2012; 60 Rajarshi Middya (CR32) 2017; 34 Kaushik, Gautam, Elamaran (CR20) 2014; 2014 Chen, Guiling, Weixiang, Yi, Lequn (CR7) 2009; 1 Marcelloni, Vecchio (CR29) 2010; 3 Hosseini-Nejad, Jannesari, Sodagar (CR16) 2012; 2012 Kemda, Huang (CR21) 2015; 18 Akansu, Serdijn, Selesnick (CR1) 2010; 3 CR26 CR25 Zhang, Wen, Ashuri (CR48) 2018; 32 Xiong, Zhao, Shi, Wang (CR44) 2019; 19 Buratti, Conti, Dardari, Verdone (CR4) 2009; 9 Huffman (CR17) 1952; 40 Sheltami, Musaddiq, Shakshuki (CR37) 2016; 64 F Marcelloni (2425_CR28) 2010; 180 X Wu (2425_CR43) 2015; 134 K Xiong (2425_CR44) 2019; 19 T Sheltami (2425_CR37) 2016; 64 2425_CR19 A Deligiannakis (2425_CR11) 2011; 36 M Sharma (2425_CR36) 2010; 10 2425_CR12 G Hollmig (2425_CR15) 2017; 65 AN Akansu (2425_CR1) 2010; 3 T Dang (2425_CR9) 2013 L Zhang (2425_CR48) 2018; 32 Y Li (2425_CR23) 2016; 12 L Zhang (2425_CR49) 2016; 36 T Dumas (2425_CR13) 2020; 29 Z Chen (2425_CR7) 2009; 1 MA Alsheikh (2425_CR2) 2016; 16 2425_CR26 2425_CR25 S Iqbal (2425_CR18) 2017; 24 DA Huffman (2425_CR17) 1952; 40 AB Noel (2425_CR30) 2017; 19 L-T Zhan (2425_CR46) 2017; 86 NCMKN Rajarshi Middya (2425_CR32) 2017; 34 CSH Kaushik (2425_CR20) 2014; 2014 2425_CR5 R Banerjee (2425_CR3) 2017; 25 LE Kemda (2425_CR21) 2015; 18 S Chen (2425_CR6) 2019; 25 P Tandon (2425_CR39) 2016; 57 H Hosseini-Nejad (2425_CR16) 2012; 2012 2425_CR33 L Dan (2425_CR8) 2016; 2016 2425_CR35 Y Pan (2425_CR31) 2019; 120 J Rissanen (2425_CR34) 1979; 23 A Wheeler (2425_CR42) 2007; 45 A Kim (2425_CR22) 2015; 48 C Buratti (2425_CR4) 2009; 9 T Srisooksai (2425_CR38) 2012; 35 J Makhoul (2425_CR27) 1975; 63 L Liu (2425_CR24) 2013; 17 F Marcelloni (2425_CR29) 2010; 3 X Xu (2425_CR45) 2018; 29 2425_CR47 J Ziv (2425_CR50) 1978; 24 MA Davenport (2425_CR10) 2012; 60 2425_CR40 H Fu (2425_CR14) 2020; 82 A Welch (2425_CR41) 1984; 17
References_xml	– volume: 65 start-page: 65 year: 2017 end-page: 77 ident: CR15 article-title: An evaluation of combinations of lossy compression and change-detection approaches for time-series data publication-title: Information Systems doi: 10.1016/j.is.2016.11.001 – volume: 40 start-page: 1098 issue: 9 year: 1952 end-page: 1101 ident: CR17 article-title: A method for the construction of minimum-redundancy codes publication-title: Proceedings of the IRE doi: 10.1109/JRPROC.1952.273898 – ident: CR12 – volume: 32 start-page: 04017071 issue: 1 year: 2018 ident: CR48 article-title: BIM log mining: Measuring design productivity publication-title: Journal of Computing in Civil Engineering doi: 10.1061/(ASCE)CP.1943-5487.0000721 – volume: 3 start-page: 137 year: 2010 end-page: 153 ident: CR29 article-title: A two-objective evolutionary approach to design lossy compression algorithms for tiny nodes of wireless sensor networks publication-title: Evolutionary Intelligence doi: 10.1007/s12065-010-0044-x – ident: CR35 – volume: 25 start-page: 429 issue: 1 year: 2019 end-page: 438 ident: CR6 article-title: A hierarchical adaptive spatio-temporal data compression scheme for wireless sensor networks publication-title: Wireless Networks doi: 10.1007/s11276-017-1570-6 – volume: 63 start-page: 561 year: 1975 end-page: 580 ident: CR27 article-title: Linear prediction: A tutorial review publication-title: Proceedings of the IEEE doi: 10.1109/PROC.1975.9792 – volume: 29 start-page: 679 year: 2020 end-page: 693 ident: CR13 article-title: Context-Adaptive Neural Network-Based Prediction for Image Compression publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2019.2934565 – ident: CR25 – volume: 86 start-page: 230 year: 2017 end-page: 242 ident: CR46 article-title: Failure probability assessment and parameter sensitivity analysis of a contaminant’s transit time through a compacted clay liner publication-title: Computers and Geotechnics doi: 10.1016/j.compgeo.2017.01.014 – volume: 45 start-page: 70 issue: 4 year: 2007 end-page: 77 ident: CR42 article-title: Commercial applications of wireless sensor networks using ZigBee publication-title: IEEE Communications Magazine doi: 10.1109/MCOM.2007.343615 – volume: 2016 start-page: 372 year: 2016 end-page: 376 ident: CR8 article-title: Cluster-based energy-efficient transmission using a new hybrid compressed sensing in WSN publication-title: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) doi: 10.1109/INFCOMW.2016.7562104 – volume: 23 start-page: 149 issue: 2 year: 1979 end-page: 162 ident: CR34 article-title: Arithmetic coding publication-title: IBM Journal of Research and Development doi: 10.1147/rd.232.0149 – ident: CR19 – volume: 18 start-page: 551 issue: 4 year: 2015 end-page: 566 ident: CR21 article-title: Value-at-risk for the USD/ZAR exchange rate: The Variance-Gamma model publication-title: South African Journal of Economic and Management Sciences doi: 10.4102/sajems.v18i4.966 – volume: 34 start-page: 642 issue: 6 year: 2017 end-page: 654 ident: CR32 article-title: Compressive sensing in wireless sensor networks - a survey publication-title: IETE Technical Review doi: 10.1080/02564602.2016.1233835 – volume: 29 start-page: 6202 issue: 12 year: 2018 end-page: 6213 ident: CR45 article-title: Learning-based predictive control for discrete-time nonlinear systems with stochastic disturbances publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2018.2820019 – volume: 25 start-page: 167 issue: 1 year: 2017 end-page: 183 ident: CR3 article-title: An energy efficient image compression scheme for wireless multimedia sensor network using curve fitting technique publication-title: Wireless Networks doi: 10.1007/s11276-017-1543-9 – volume: 57 start-page: 195 year: 2016 end-page: 206 ident: CR39 article-title: Detection of radioactive sources in urban scenes using Bayesian aggregation of data from mobile spectrometers publication-title: Information Systems doi: 10.1016/j.is.2015.10.006 – ident: CR5 – ident: CR26 – volume: 36 start-page: 278 issue: 2 year: 2016 end-page: 301 ident: CR49 article-title: Towards a fuzzy Bayesian network based approach for safety risk analysis of tunnel-induced pipeline damage publication-title: Risk Analysis doi: 10.1111/risa.12448 – volume: 2014 start-page: 1 year: 2014 end-page: 6 ident: CR20 article-title: A tutorial review on discrete fourier transform with data compression application publication-title: International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE) – ident: CR47 – volume: 16 start-page: 5072 issue: 12 year: 2016 end-page: 5083 ident: CR2 article-title: Rate-distortion balanced data compression for wireless sensor networks publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2016.2550599 – ident: CR33 – volume: 134 start-page: 157 year: 2015 end-page: 168 ident: CR43 article-title: A dynamic Bayesian network based approach to safety decision support in tunnel construction publication-title: Reliability Engineering and System Safety doi: 10.1016/j.ress.2014.10.021 – volume: 120 start-page: 652 year: 2019 end-page: 665 ident: CR31 article-title: Structural health monitoring and assessment using wavelet packet energy spectrum publication-title: Safety Science doi: 10.1016/j.ssci.2019.08.015 – volume: 24 start-page: 530 issue: 5 year: 1978 end-page: 536 ident: CR50 article-title: Compression of individual sequences via variable-rate coding publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.1978.1055934 – volume: 60 start-page: 4628 issue: 9 year: 2012 end-page: 4642 ident: CR10 article-title: The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding versus Dynamic Range publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2012.2201149 – ident: CR40 – volume: 1 start-page: 283 year: 2009 end-page: 286 ident: CR7 article-title: Research on data compression algorithm based on prediction coding for wireless sensor network nodes publication-title: International Forum on Information Technology and Applications – volume: 10 start-page: 133 issue: 5 year: 2010 end-page: 141 ident: CR36 article-title: Compression using Huffman coding publication-title: International Journal of Computer Science and Network Security – volume: 9 start-page: 6869 year: 2009 end-page: 6896 ident: CR4 article-title: An overview on wireless sensor networks technology and evolution publication-title: Sensors doi: 10.3390/s90906869 – volume: 19 start-page: 1403 issue: 3 year: 2017 end-page: 1423 ident: CR30 article-title: Structural health monitoring using wireless sensor networks: A comprehensive survey publication-title: IEEE Communications Surveys & Tutorials doi: 10.1109/COMST.2017.2691551 – volume: 17 start-page: 8 issue: 6 year: 1984 end-page: 19 ident: CR41 article-title: Technique for high-performance data compression publication-title: Computer doi: 10.1109/MC.1984.1659158 – volume: 3 start-page: 1 issue: 1 year: 2010 end-page: 18 ident: CR1 article-title: Emerging applications of wavelets: A review publication-title: Physical Communication doi: 10.1016/j.phycom.2009.07.001 – volume: 17 start-page: 283 issue: 6 year: 2013 end-page: 292 ident: CR24 article-title: A noise compensation LPC method based on pitch synchronous analysis for speech publication-title: Journal of Signal Processing doi: 10.2299/jsp.17.283 – volume: 48 start-page: 320 year: 2015 end-page: 326 ident: CR22 article-title: Hybrid wireless sensor network for building energy management systems based on the 2.4 GHz and 400 MHz bands publication-title: Information Systems doi: 10.1016/j.is.2014.06.005 – volume: 35 start-page: 37 year: 2012 end-page: 59 ident: CR38 article-title: Practical data compression in wireless sensor networks: A survey publication-title: Journal of Network and Computer Applications doi: 10.1016/j.jnca.2011.03.001 – volume: 2012 start-page: 209 year: 2012 end-page: 213 ident: CR16 article-title: Data compression based on discrete cosine transform for implantable neural recording microsystems publication-title: IEEE International Conference on Circuits and Systems (ICCAS) doi: 10.1109/ICCircuitsAndSystems.2012.6408307 – volume: 64 start-page: 151 year: 2016 end-page: 162 ident: CR37 article-title: Data compression techniques in wireless sensor networks publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2016.01.015 – year: 2013 ident: CR9 publication-title: Robust data compression for irregular wireless sensor networks using logical mapping doi: 10.1155/2013/253257 – volume: 180 start-page: 1924 year: 2010 end-page: 1941 ident: CR28 article-title: Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization publication-title: Information Sciences doi: 10.1016/j.ins.2010.01.027 – volume: 24 start-page: 2685 year: 2017 end-page: 2697 ident: CR18 article-title: Critical link identification and prioritization using Bayesian theorem for dynamic channel assignment in wireless mesh networks publication-title: Wireless Networks doi: 10.1007/s11276-017-1471-8 – volume: 19 start-page: 4540 issue: 20 year: 2019 ident: CR44 article-title: A convex optimization algorithm for compressed sensing in a complex domain: The complex-valued split bregman method publication-title: Sensors Basel, Switzerlad doi: 10.3390/s19204540 – volume: 82 start-page: 115774 year: 2020 ident: CR14 article-title: Improved hybrid layered image compression using deep learning and traditional codecs publication-title: Signal Processing: Image Communication – volume: 12 year: 2016 ident: CR23 article-title: Temporal lossless and lossy compression in wireless sensor networks publication-title: ACM Transactions on Sensor Networks doi: 10.1145/2990196 – volume: 36 start-page: 1044 issue: 7 year: 2011 end-page: 1063 ident: CR11 article-title: Detecting proximity events in sensor networks publication-title: Information Systems doi: 10.1016/j.is.2011.03.004 – ident: 2425_CR19 doi: 10.1080/01621459.1968.11009335 – volume: 12 year: 2016 ident: 2425_CR23 publication-title: ACM Transactions on Sensor Networks doi: 10.1145/2990196 – volume: 36 start-page: 278 issue: 2 year: 2016 ident: 2425_CR49 publication-title: Risk Analysis doi: 10.1111/risa.12448 – volume: 3 start-page: 1 issue: 1 year: 2010 ident: 2425_CR1 publication-title: Physical Communication doi: 10.1016/j.phycom.2009.07.001 – volume: 45 start-page: 70 issue: 4 year: 2007 ident: 2425_CR42 publication-title: IEEE Communications Magazine doi: 10.1109/MCOM.2007.343615 – volume: 64 start-page: 151 year: 2016 ident: 2425_CR37 publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2016.01.015 – volume: 57 start-page: 195 year: 2016 ident: 2425_CR39 publication-title: Information Systems doi: 10.1016/j.is.2015.10.006 – volume: 65 start-page: 65 year: 2017 ident: 2425_CR15 publication-title: Information Systems doi: 10.1016/j.is.2016.11.001 – volume: 48 start-page: 320 year: 2015 ident: 2425_CR22 publication-title: Information Systems doi: 10.1016/j.is.2014.06.005 – volume: 86 start-page: 230 year: 2017 ident: 2425_CR46 publication-title: Computers and Geotechnics doi: 10.1016/j.compgeo.2017.01.014 – ident: 2425_CR26 doi: 10.1109/ICC.2010.5502565 – ident: 2425_CR33 – volume: 180 start-page: 1924 year: 2010 ident: 2425_CR28 publication-title: Information Sciences doi: 10.1016/j.ins.2010.01.027 – volume: 19 start-page: 1403 issue: 3 year: 2017 ident: 2425_CR30 publication-title: IEEE Communications Surveys & Tutorials doi: 10.1109/COMST.2017.2691551 – ident: 2425_CR5 doi: 10.1109/SENSORCOMM.2009.84 – volume: 34 start-page: 642 issue: 6 year: 2017 ident: 2425_CR32 publication-title: IETE Technical Review doi: 10.1080/02564602.2016.1233835 – volume: 10 start-page: 133 issue: 5 year: 2010 ident: 2425_CR36 publication-title: International Journal of Computer Science and Network Security – volume: 63 start-page: 561 year: 1975 ident: 2425_CR27 publication-title: Proceedings of the IEEE doi: 10.1109/PROC.1975.9792 – volume: 19 start-page: 4540 issue: 20 year: 2019 ident: 2425_CR44 publication-title: Sensors Basel, Switzerlad doi: 10.3390/s19204540 – volume: 60 start-page: 4628 issue: 9 year: 2012 ident: 2425_CR10 publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2012.2201149 – volume: 1 start-page: 283 year: 2009 ident: 2425_CR7 publication-title: International Forum on Information Technology and Applications – volume-title: Robust data compression for irregular wireless sensor networks using logical mapping year: 2013 ident: 2425_CR9 doi: 10.1155/2013/253257 – volume: 3 start-page: 137 year: 2010 ident: 2425_CR29 publication-title: Evolutionary Intelligence doi: 10.1007/s12065-010-0044-x – volume: 24 start-page: 530 issue: 5 year: 1978 ident: 2425_CR50 publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.1978.1055934 – volume: 2016 start-page: 372 year: 2016 ident: 2425_CR8 publication-title: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) doi: 10.1109/INFCOMW.2016.7562104 – volume: 17 start-page: 283 issue: 6 year: 2013 ident: 2425_CR24 publication-title: Journal of Signal Processing doi: 10.2299/jsp.17.283 – ident: 2425_CR47 doi: 10.1109/WSC.2015.7408498 – volume: 18 start-page: 551 issue: 4 year: 2015 ident: 2425_CR21 publication-title: South African Journal of Economic and Management Sciences doi: 10.4102/sajems.v18i4.966 – volume: 29 start-page: 6202 issue: 12 year: 2018 ident: 2425_CR45 publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2018.2820019 – volume: 36 start-page: 1044 issue: 7 year: 2011 ident: 2425_CR11 publication-title: Information Systems doi: 10.1016/j.is.2011.03.004 – volume: 40 start-page: 1098 issue: 9 year: 1952 ident: 2425_CR17 publication-title: Proceedings of the IRE doi: 10.1109/JRPROC.1952.273898 – volume: 25 start-page: 167 issue: 1 year: 2017 ident: 2425_CR3 publication-title: Wireless Networks doi: 10.1007/s11276-017-1543-9 – volume: 24 start-page: 2685 year: 2017 ident: 2425_CR18 publication-title: Wireless Networks doi: 10.1007/s11276-017-1471-8 – volume: 82 start-page: 115774 year: 2020 ident: 2425_CR14 publication-title: Signal Processing: Image Communication – volume: 35 start-page: 37 year: 2012 ident: 2425_CR38 publication-title: Journal of Network and Computer Applications doi: 10.1016/j.jnca.2011.03.001 – volume: 29 start-page: 679 year: 2020 ident: 2425_CR13 publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2019.2934565 – volume: 16 start-page: 5072 issue: 12 year: 2016 ident: 2425_CR2 publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2016.2550599 – volume: 32 start-page: 04017071 issue: 1 year: 2018 ident: 2425_CR48 publication-title: Journal of Computing in Civil Engineering doi: 10.1061/(ASCE)CP.1943-5487.0000721 – volume: 134 start-page: 157 year: 2015 ident: 2425_CR43 publication-title: Reliability Engineering and System Safety doi: 10.1016/j.ress.2014.10.021 – ident: 2425_CR25 doi: 10.1145/1614320.1614337 – volume: 25 start-page: 429 issue: 1 year: 2019 ident: 2425_CR6 publication-title: Wireless Networks doi: 10.1007/s11276-017-1570-6 – ident: 2425_CR35 doi: 10.1109/LCN.2004.72 – volume: 9 start-page: 6869 year: 2009 ident: 2425_CR4 publication-title: Sensors doi: 10.3390/s90906869 – ident: 2425_CR12 doi: 10.1109/INDIN.2007.4384776 – volume: 2012 start-page: 209 year: 2012 ident: 2425_CR16 publication-title: IEEE International Conference on Circuits and Systems (ICCAS) doi: 10.1109/ICCircuitsAndSystems.2012.6408307 – volume: 23 start-page: 149 issue: 2 year: 1979 ident: 2425_CR34 publication-title: IBM Journal of Research and Development doi: 10.1147/rd.232.0149 – volume: 120 start-page: 652 year: 2019 ident: 2425_CR31 publication-title: Safety Science doi: 10.1016/j.ssci.2019.08.015 – volume: 2014 start-page: 1 year: 2014 ident: 2425_CR20 publication-title: International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE) – ident: 2425_CR40 doi: 10.1016/j.jksuci.2018.05.006 – volume: 17 start-page: 8 issue: 6 year: 1984 ident: 2425_CR41 publication-title: Computer doi: 10.1109/MC.1984.1659158
SSID	ssj0008399
Score	2.3378637
Snippet	Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	5981
SubjectTerms	Accuracy Algorithms Bayesian analysis Coding Communications Engineering Compression ratio Computer Communication Networks Computing costs Data compression Data storage Data transmission Digital media Electrical Engineering Engineering IT in Business Missing data Networks Predictions Regression models Sensors Statistical analysis Statistical inference Wireless networks Wireless sensor networks
SummonAdditionalLinks	– databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NS8MwFA86PejBb3E6JQdvGliarm2OUxweRMQvdgvNR-egdrJWx_57X9J2U1FBjyHJI7yXl_cL7wuhY8ps2bWgTXyqNYH_lyKA8imJDWAHFavA007SV-H1ddTv85sqKSyvo91rl6R7qefJbtQLbcBsmzicTCaLaAnMXWQbNtzePc7eXzD53Pk44ZtlHV9Vqsz3ND6boznG_OIWddamt_6_c26gtQpd4m55HTbRgsm20OqHmoPbSHQxIGmcwmmm2AaUl4GwGY7TwWg8LJ6eMeBYbEsYpzCDc_jnwjgrw8VzbAPlB_gsnhqbfolhux66NxOIWTu4gx56F_fnl6TqskAUqF9BJE8iKTsmCKnxAR8aHinQasUloEGAQ5Ip3WEALCSjsQx1wMHmhdxQA8rv0YTtokY2yswewolibSO5lH4Q-ZypKIkCqgNfaeklLPabiNbMFqoqQW47YaRiXjzZMk8A84Rjnpg00clsz0tZgOPX1a1ahqJSxlwAguE-3BTuNdFpLbP59M_U9v-2_ACteFbsLlOxhRrF-NUcomX1Vgzz8ZG7pO8MH-F7 priority: 102 providerName: Springer Nature
Title	A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding
URI	https://link.springer.com/article/10.1007/s11276-020-02425-w https://www.proquest.com/docview/2449450992
Volume	26
WOSCitedRecordID	wos000547243000002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1572-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0008399 issn: 1022-0038 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07T8MwED5BYYCBN6I8Kg9sYFEnaRJPCBCIAaqKNyxR_EipFNLSFKr-e85OSgEJFhZLVuKT5bPvPvteALvMNWnX_Dr1mFIU71-SIspnNNaIHWQsfUdZTl8EzWb48MBb5YNbXrpVjmWiFdSqK80b-QGqIe6hduPOYe-VmqpRxrpaltCYhhlENsy4dF06rU9JjMqfW2snXriMCawMmilC55gTGPfbOrWomw6_K6YJ2vxhILV652zxvzNegoUScZKjYossw5TOVmD-Sx7CVYiOCKJrkuK8RsQ4mRfOsRmJ0zZSHDy_EMS2xKQ1TvELyfHui_2scCHPiXGeb5PjeKRNSCbB4apj5SgSM7pxDW7PTm9OzmlZeYFKPJIDKngSCtHQfsC0hyureSjxpEsuECEiRBKuVA0XwYZwWSwC5XPUgwHXTKNAcFjirkMl62Z6A0gi3boWXAjPDz3uyjAJfaZ8TyrhJG7sVYGNlz2SZVpyUx0jjSYJlQ2rImRVZFkVDauw9zmmVyTl-PPv7TF_ovKA5tGEOVXYH3N48vl3apt_U9uCOcdsKhutuA2VQf9N78CsfB908n4NpoP7xxrMHJ82W1fYu6yf1OyWxbbVeML26vruA4c28Hs
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB5RWqlwoC1QsUBbH8qpWKydkMQHhKAtArFdcaASNxM_QpGW8MjCav9Uf2NnnISFSuXGocfI8UiOP898k3kY4LOIqO1a0uWxcI6j_2U5snzBc4_cweY2kS7sdC_t97OTE3U0Bb_bWhhKq2x1YlDU7tLSP_INNEMqRuum5PbVNadboyi62l6hUcPi0I9H6LJVWwffcH_XpNz7fvx1nze3CnCLcBtyo4rMmE2fpMLHyIe8yiyi2CqD7AfNv4ms24zQkJpI5CZ1iUIdnyovPIJdiiJCuS_gZUydxShVUB7da34kGypEV9HBo5BbU6RTl-oJmVK6b5cHls9Hjw3hhN3-FZANdm7vzf_2hd7CXMOo2U59BN7BlC_nYfZBn8UF0DsMvQc2wO8wZpREXyf_liwfnOEKhr8uGHJ3Rm2bBzjCKvTt8bmsU-QrRsUBZ2w3H3sqOWU43Z0HO4HCyPYvws9nWeF7mC4vS78ErLBR1xtlTJxksYpsVmSJcElsnZFFlMcdEO02a9u0XafbPwZ60jCaoKERGjpAQ4868OV-zlXddOTJt1dbPOhGAVV6AoYOrLeImgz_W9ry09I-wev94x893TvoH67AjCRAh8rMVZge3tz6D_DK3g3Pq5uP4WgwOH1upP0BVB1FWg
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB5RqCp6gBaKWB6tD-VUrF0n2SQ-IMSjqyLQag-ttOJi4ke2SCFQkna1f41fx9hJWEBibxx6jByP5PjzzDeZhwG-Mt-2XQs7NGBaU_S_FEWWz2hikDuoRIWedjt9FvX78XDIB3Nw19TC2LTKRic6Ra2vlf1H3kYzxAO0btxrp3VaxOC4t3_zh9obpGyktblOo4LIqZmM0X0r9k6Oca93PK_3_efRD1rfMEAVQq-kkqexlF0TRswEyI0MjxUiWnGJTAipgPSV7vpoVKXPEhnpkKO-j7hhBoHvsdRHuW9gIcI12nTCQff8wQog8eAu0orOng2_1QU7Vdke8yKb-tuhjvHT8VOjOGW6z4Kzzub1lv_nr_UBlmqmTQ6qo_ER5ky-Au8f9V9cBXFA0KsgGX6TCbHJ9VVScE6SbIQrKH9fEeT0xLZzznCEFOjz43Nepc4XxBYNjMhhMjG2FJXgdH3p7AcKs5zgE_x6lRWuwXx-nZt1IKnyO0ZyKYMwDriv4jQOmQ4DpaWX-knQAtZsuVB1O3Z7K0gmpo2kLUwEwkQ4mIhxC749zLmpmpHMfHurwYaoFVMhpsBowW6Drunwy9I2Zkv7Au8QYOLspH-6CYuexbYr2NyC-fL2r9mGt-pfeVncfnanhMDFawPtHtjDTkY
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+new+lossy+compression+algorithm+for+wireless+sensor+networks+using+Bayesian+predictive+coding&rft.jtitle=Wireless+networks&rft.au=Chen%2C+Chen&rft.au=Zhang+Limao&rft.au=Lee+Kong%2C+Tiong+Robert&rft.date=2020-11-01&rft.pub=Springer+Nature+B.V&rft.issn=1022-0038&rft.eissn=1572-8196&rft.volume=26&rft.issue=8&rft.spage=5981&rft.epage=5995&rft_id=info:doi/10.1007%2Fs11276-020-02425-w&rft.externalDBID=HAS_PDF_LINK
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1022-0038&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1022-0038&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1022-0038&client=summon