{"id":"https://openalex.org/W2000969357","doi":"https://doi.org/10.1145/1132983.1132997","title":"A supervised learning approach for routing optimizations in wireless sensor networks","display_name":"A supervised learning approach for routing optimizations in wireless sensor networks","publication_year":2006,"publication_date":"2006-05-26","ids":{"openalex":"https://openalex.org/W2000969357","doi":"https://doi.org/10.1145/1132983.1132997","mag":"2000969357"},"language":"en","primary_location":{"id":"doi:10.1145/1132983.1132997","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1132983.1132997","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality","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/A5100424366","display_name":"Yong Wang","orcid":"https://orcid.org/0000-0001-7670-3958"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yong Wang","raw_affiliation_strings":["Princeton University","Princeton University#TAB#"],"affiliations":[{"raw_affiliation_string":"Princeton University","institution_ids":["https://openalex.org/I20089843"]},{"raw_affiliation_string":"Princeton University#TAB#","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002015508","display_name":"Margaret Martonosi","orcid":"https://orcid.org/0000-0001-9683-8032"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Margaret Martonosi","raw_affiliation_strings":["Princeton University","Princeton University#TAB#"],"affiliations":[{"raw_affiliation_string":"Princeton University","institution_ids":["https://openalex.org/I20089843"]},{"raw_affiliation_string":"Princeton University#TAB#","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057413185","display_name":"Li-Shiuan Peh","orcid":"https://orcid.org/0000-0001-9010-6519"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li-Shiuan Peh","raw_affiliation_strings":["Princeton University","Princeton University#TAB#"],"affiliations":[{"raw_affiliation_string":"Princeton University","institution_ids":["https://openalex.org/I20089843"]},{"raw_affiliation_string":"Princeton University#TAB#","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100424366"],"corresponding_institution_ids":["https://openalex.org/I20089843"],"apc_list":null,"apc_paid":null,"fwci":2.205,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.88583765,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"79","last_page":"86"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":1.0,"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":1.0,"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/T10246","display_name":"Mobile Ad Hoc Networks","score":0.9998000264167786,"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/T11896","display_name":"Opportunistic and Delay-Tolerant Networks","score":0.9977999925613403,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8207338452339172},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.7314038276672363},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.638742208480835},{"id":"https://openalex.org/keywords/routing-protocol","display_name":"Routing protocol","score":0.591225266456604},{"id":"https://openalex.org/keywords/geographic-routing","display_name":"Geographic routing","score":0.559188723564148},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45049750804901123},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.42556536197662354},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40173661708831787},{"id":"https://openalex.org/keywords/link-state-routing-protocol","display_name":"Link-state routing protocol","score":0.3911926746368408},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.37678930163383484},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3623039722442627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3561726212501526}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8207338452339172},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.7314038276672363},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.638742208480835},{"id":"https://openalex.org/C104954878","wikidata":"https://www.wikidata.org/wiki/Q1648707","display_name":"Routing protocol","level":3,"score":0.591225266456604},{"id":"https://openalex.org/C50558702","wikidata":"https://www.wikidata.org/wiki/Q5535067","display_name":"Geographic routing","level":5,"score":0.559188723564148},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45049750804901123},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.42556536197662354},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40173661708831787},{"id":"https://openalex.org/C89305328","wikidata":"https://www.wikidata.org/wiki/Q1755411","display_name":"Link-state routing protocol","level":4,"score":0.3911926746368408},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.37678930163383484},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3623039722442627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3561726212501526}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1132983.1132997","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1132983.1132997","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.74.8393","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.8393","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://parapet.ee.princeton.edu/papers/realman2006wang.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W159719520","https://openalex.org/W1504694836","https://openalex.org/W1558875883","https://openalex.org/W1570448133","https://openalex.org/W1670263352","https://openalex.org/W1904710828","https://openalex.org/W2000234133","https://openalex.org/W2041248965","https://openalex.org/W2081211681","https://openalex.org/W2095971510","https://openalex.org/W2105454049","https://openalex.org/W2108481447","https://openalex.org/W2117441298","https://openalex.org/W2117520883","https://openalex.org/W2125055259","https://openalex.org/W2128815505","https://openalex.org/W2133304743","https://openalex.org/W2138933282","https://openalex.org/W2148134592","https://openalex.org/W2153589127","https://openalex.org/W2153905331","https://openalex.org/W2155479707","https://openalex.org/W2165004589","https://openalex.org/W2169458211","https://openalex.org/W2966207845","https://openalex.org/W4233793161"],"related_works":["https://openalex.org/W2774404108","https://openalex.org/W2800714197","https://openalex.org/W2114784287","https://openalex.org/W2620451430","https://openalex.org/W4224257934","https://openalex.org/W2226043842","https://openalex.org/W3007557981","https://openalex.org/W2373440974","https://openalex.org/W2777052597","https://openalex.org/W2810255618"],"abstract_inverted_index":{"Routing":[0],"in":[1,14,23,56,66,129,155,187],"sensor":[2,58,139,189],"networks":[3],"maintains":[4],"information":[5,29,113],"on":[6,136,151,164,176],"neighbor":[7],"states":[8],"and":[9,27,31,41,71,74,179],"potentially":[10],"many":[11],"other":[12,165],"factors":[13],"order":[15],"to":[16,37,52,90,125,148,184],"make":[17],"informed":[18],"decisions.":[19],"Challenges":[20],"arise":[21],"both":[22],"(a)":[24],"performing":[25],"accurate":[26],"adaptive":[28],"discovery":[30],"(b)":[32],"processing/analyzing":[33],"the":[34,92],"gathered":[35],"data":[36,152,170],"extract":[38],"useful":[39],"features":[40],"correlations.":[42],"In":[43],"this":[44,98],"paper,":[45],"we":[46,85,100],"explore":[47],"using":[48,76,114],"supervised":[49],"learning":[50,61],"techniques":[51],"address":[53],"such":[54,168],"challenges":[55],"wireless":[57,188],"networks.":[59,190],"Machine":[60],"has":[62],"been":[63],"very":[64],"effective":[65],"discovering":[67],"relations":[68],"between":[69],"attributes":[70],"extracting":[72],"knowledge":[73,115],"patterns":[75],"a":[77,82,118,137,156,181],"large":[78],"corpus":[79],"of":[80,94],"samples.As":[81],"case":[83],"study,":[84],"use":[86],"link":[87,111],"quality":[88,112],"prediction":[89],"demonstrate":[91],"effectiveness":[93],"our":[95],"approach.":[96],"For":[97],"purpose,":[99],"present":[101],"MetricMap,a":[102],"link-quality":[103],"aware":[104],"collection":[105],"protocol":[106],"atop":[107],"MintRoute":[108],"that":[109,143],"derives":[110],"acquired":[116],"from":[117],"training":[119],"phase.":[120],"Our":[121,172],"approach":[122,173],"allows":[123],"MetricMap":[124,144],"maintain":[126],"efficient":[127],"routing":[128,185],"situations":[130],"where":[131],"traditional":[132],"approaches":[133],"fail.":[134],"Evaluation":[135],"30-node":[138],"network":[140],"testbed":[141],"shows":[142],"can":[145],"achieve":[146],"up":[147],"300%":[149],"improvement":[150],"delivery":[153],"rate":[154],"high":[157],"data-rate":[158],"application,":[159],"with":[160],"no":[161],"negative":[162],"impact":[163],"performance":[166],"metrics,":[167],"as":[169],"latency.":[171],"is":[174],"based":[175],"real-world":[177],"measurement":[178],"provides":[180],"new":[182],"perspective":[183],"optimizations":[186]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}