{"id":"https://openalex.org/W2725747721","doi":"https://doi.org/10.1109/vtcfall.2017.8288147","title":"Efficient Bayesian Communication Approach for Smart Agriculture Applications","display_name":"Efficient Bayesian Communication Approach for Smart Agriculture Applications","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2725747721","doi":"https://doi.org/10.1109/vtcfall.2017.8288147","mag":"2725747721"},"language":"en","primary_location":{"id":"doi:10.1109/vtcfall.2017.8288147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2017.8288147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053524202","display_name":"Cristanel Razafimandimby","orcid":null},"institutions":[{"id":"https://openalex.org/I4210138412","display_name":"Centre de recherche Inria Lille - Nord Europe","ror":"https://ror.org/04eej9726","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1326498283","https://openalex.org/I4210138412"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Cristanel Razafimandimby","raw_affiliation_strings":["Inria Lille-Nord Europe, Lille, France"],"affiliations":[{"raw_affiliation_string":"Inria Lille-Nord Europe, Lille, France","institution_ids":["https://openalex.org/I4210138412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044507011","display_name":"Val\u00e9ria Loscr\u00ec","orcid":"https://orcid.org/0000-0003-2558-1801"},"institutions":[{"id":"https://openalex.org/I4210138412","display_name":"Centre de recherche Inria Lille - Nord Europe","ror":"https://ror.org/04eej9726","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1326498283","https://openalex.org/I4210138412"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Valeria Loscri","raw_affiliation_strings":["Inria Lille-Nord Europe, Lille, France"],"affiliations":[{"raw_affiliation_string":"Inria Lille-Nord Europe, Lille, France","institution_ids":["https://openalex.org/I4210138412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074303469","display_name":"Anna Maria Vegni","orcid":"https://orcid.org/0000-0002-3069-298X"},"institutions":[{"id":"https://openalex.org/I119003972","display_name":"Roma Tre University","ror":"https://ror.org/05vf0dg29","country_code":"IT","type":"education","lineage":["https://openalex.org/I119003972"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Anna Maria Vegni","raw_affiliation_strings":["Dept. of Eng., Roma Tre Univ., Rome, Italy"],"affiliations":[{"raw_affiliation_string":"Dept. of Eng., Roma Tre Univ., Rome, Italy","institution_ids":["https://openalex.org/I119003972"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010884586","display_name":"Alessandro Neri","orcid":"https://orcid.org/0000-0002-5911-9490"},"institutions":[{"id":"https://openalex.org/I119003972","display_name":"Roma Tre University","ror":"https://ror.org/05vf0dg29","country_code":"IT","type":"education","lineage":["https://openalex.org/I119003972"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alessandro Neri","raw_affiliation_strings":["Dept. of Eng., Roma Tre Univ., Rome, Italy"],"affiliations":[{"raw_affiliation_string":"Dept. of Eng., Roma Tre Univ., Rome, Italy","institution_ids":["https://openalex.org/I119003972"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5053524202"],"corresponding_institution_ids":["https://openalex.org/I4210138412"],"apc_list":null,"apc_paid":null,"fwci":1.6576,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.8611649,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.736492395401001},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5210729837417603},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5035931468009949},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4819314479827881},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.47151103615760803},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.466513991355896},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.4534889757633209},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.4474025368690491},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4445308744907379},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4114246964454651},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4021921753883362},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3819773197174072},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3353714942932129},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2675059139728546},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.14390617609024048},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13385653495788574},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13197597861289978}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.736492395401001},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5210729837417603},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5035931468009949},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4819314479827881},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.47151103615760803},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.466513991355896},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.4534889757633209},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.4474025368690491},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4445308744907379},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4114246964454651},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4021921753883362},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3819773197174072},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3353714942932129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2675059139728546},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.14390617609024048},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13385653495788574},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13197597861289978},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtcfall.2017.8288147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2017.8288147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W87821109","https://openalex.org/W335370180","https://openalex.org/W1976864125","https://openalex.org/W2019599312","https://openalex.org/W2114358147","https://openalex.org/W2135053460","https://openalex.org/W2186066651","https://openalex.org/W2203411864","https://openalex.org/W2234437762","https://openalex.org/W2283478272","https://openalex.org/W2399252288","https://openalex.org/W2510032411","https://openalex.org/W2559518640","https://openalex.org/W2600705926","https://openalex.org/W4300506978","https://openalex.org/W6686840473","https://openalex.org/W6687659289","https://openalex.org/W6689623980"],"related_works":["https://openalex.org/W2111773953","https://openalex.org/W2407375987","https://openalex.org/W2505726097","https://openalex.org/W2950975704","https://openalex.org/W2010643158","https://openalex.org/W3049691116","https://openalex.org/W2106867672","https://openalex.org/W4310268968","https://openalex.org/W3081214562","https://openalex.org/W2753713401"],"abstract_inverted_index":{"To":[0,96],"meet":[1],"the":[2,6,12,33,48,76,83,88,110,124,165,178,186,198,204],"food":[3],"demand":[4],"of":[5,14,38,50,79,90,112,138,200,212],"future,":[7],"farmers":[8,31],"are":[9],"turning":[10],"to":[11,62,129,163,170],"Internet":[13],"Things":[15],"(IoT)":[16],"for":[17,153],"advanced":[18],"analytics.":[19],"In":[20,117],"this":[21,98,118],"case,":[22],"data":[23,80,93,179,202,213],"generated":[24,81],"by":[25,30,82,136],"sensor":[26],"nodes":[27],"and":[28,55,64,203],"collected":[29,180],"on":[32,60,123,158,177,185],"field":[34],"provide":[35],"a":[36,102],"wealth":[37],"information":[39],"about":[40],"soil,":[41],"seeds,":[42],"crops,":[43],"plant":[44],"diseases,":[45],"etc.":[46],"Therefore,":[47],"use":[49],"high":[51,113],"tech":[52],"farming":[53],"techniques":[54],"IoT":[56],"technology":[57],"offer":[58],"insights":[59],"how":[61],"optimize":[63],"increase":[65],"yield.":[66],"However,":[67],"one":[68],"major":[69],"challenge":[70],"that":[71,192],"should":[72],"be":[73],"addressed":[74],"is":[75,121,174],"huge":[77],"amount":[78],"sensing":[84,167],"devices,":[85],"which":[86,107,127],"make":[87],"control":[89],"sending":[91],"useless":[92],"very":[94],"important.":[95],"face":[97],"challenge,":[99],"we":[100],"present":[101],"Bayesian":[103],"Inference":[104],"Approach":[105],"(BIA),":[106],"allows":[108],"avoiding":[109],"transmission":[111],"spatio-temporal":[114],"correlated":[115],"data.":[116,168],"paper,":[119],"BIA":[120,173],"based":[122,176],"PEACH":[125],"project,":[126],"aims":[128],"predict":[130],"frost":[131],"events":[132],"in":[133,161],"peach":[134,187],"orchards":[135],"means":[137],"dense":[139],"monitoring":[140],"using":[141],"low-power":[142],"wireless":[143],"mesh":[144],"networking":[145],"technology.":[146],"Belief":[147],"Propagation":[148],"algorithm":[149],"has":[150],"been":[151],"chosen":[152],"performing":[154],"an":[155,209],"approximate":[156],"inference":[157],"our":[159,193],"model":[160],"order":[162],"reconstruct":[164],"missing":[166],"According":[169],"different":[171],"scenarios,":[172],"evaluated":[175],"from":[181],"real":[182],"sensors":[183],"deployed":[184],"orchard.":[188],"The":[189],"results":[190],"show":[191],"proposed":[194],"approach":[195],"reduces":[196],"drastically":[197],"number":[199],"transmitted":[201],"energy":[205],"consumption,":[206],"while":[207],"maintaining":[208],"acceptable":[210],"level":[211],"prediction":[214],"accuracy.":[215]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}