{"id":"https://openalex.org/W2917279254","doi":"https://doi.org/10.1109/glocom.2018.8647334","title":"Cloud-Assisted Real-Time Road Condition Monitoring System for Vehicles","display_name":"Cloud-Assisted Real-Time Road Condition Monitoring System for Vehicles","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2917279254","doi":"https://doi.org/10.1109/glocom.2018.8647334","mag":"2917279254"},"language":"en","primary_location":{"id":"doi:10.1109/glocom.2018.8647334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2018.8647334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Communications Conference (GLOBECOM)","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/A5041117178","display_name":"Mohamed Akram Ameddah","orcid":null},"institutions":[{"id":"https://openalex.org/I154799132","display_name":"Universit\u00e9 de Moncton","ror":"https://ror.org/029tnqt29","country_code":"CA","type":"education","lineage":["https://openalex.org/I154799132"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Mohamed Akram Ameddah","raw_affiliation_strings":["Department of Computer Science, Universite de Moncton, Moncton, NB, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universite de Moncton, Moncton, NB, Canada","institution_ids":["https://openalex.org/I154799132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091833246","display_name":"Bhaskar Das","orcid":null},"institutions":[{"id":"https://openalex.org/I154799132","display_name":"Universit\u00e9 de Moncton","ror":"https://ror.org/029tnqt29","country_code":"CA","type":"education","lineage":["https://openalex.org/I154799132"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Bhaskar Das","raw_affiliation_strings":["Department of Computer Science, Universite de Moncton, Moncton, NB, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universite de Moncton, Moncton, NB, Canada","institution_ids":["https://openalex.org/I154799132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111912045","display_name":"Jalal Almhana","orcid":null},"institutions":[{"id":"https://openalex.org/I154799132","display_name":"Universit\u00e9 de Moncton","ror":"https://ror.org/029tnqt29","country_code":"CA","type":"education","lineage":["https://openalex.org/I154799132"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jalal Almhana","raw_affiliation_strings":["Department of Computer Science, Universite de Moncton, Moncton, NB, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Universite de Moncton, Moncton, NB, Canada","institution_ids":["https://openalex.org/I154799132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5041117178"],"corresponding_institution_ids":["https://openalex.org/I154799132"],"apc_list":null,"apc_paid":null,"fwci":1.6537,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.87385582,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.678141713142395},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.6546513438224792},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.6402475833892822},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5822272896766663},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5736159086227417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3620173931121826},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3344130516052246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.678141713142395},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.6546513438224792},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.6402475833892822},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5822272896766663},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5736159086227417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3620173931121826},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3344130516052246},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/glocom.2018.8647334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2018.8647334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1977324115","https://openalex.org/W2000948558","https://openalex.org/W2042881845","https://openalex.org/W2097612790","https://openalex.org/W2101765566","https://openalex.org/W2111162011","https://openalex.org/W2122192957","https://openalex.org/W2127218421","https://openalex.org/W2140254487","https://openalex.org/W2144169341","https://openalex.org/W2157738829","https://openalex.org/W2184642312","https://openalex.org/W2331994864","https://openalex.org/W2614267756","https://openalex.org/W2753266903","https://openalex.org/W2767026970","https://openalex.org/W2772406819","https://openalex.org/W2793895414","https://openalex.org/W6676974665","https://openalex.org/W6678914141","https://openalex.org/W6686555560"],"related_works":["https://openalex.org/W2765080098","https://openalex.org/W2385749422","https://openalex.org/W2355290145","https://openalex.org/W2353465659","https://openalex.org/W2009888974","https://openalex.org/W2355539379","https://openalex.org/W2056341223","https://openalex.org/W3023105672","https://openalex.org/W4231410700","https://openalex.org/W4237770763"],"abstract_inverted_index":{"Road":[0,38],"infrastructure":[1],"is":[2,60,182,210],"the":[3,7,29,36,81,96,108,111,117,121,125,144,150,172,185,200,228],"life":[4],"line":[5],"of":[6,130,152,178],"transportation":[8],"industry":[9],"and":[10,31,53,56,133,202,215],"it":[11,21,59,221],"should":[12],"be":[13,75],"monitored":[14],"at":[15,116,149],"regular":[16,54],"intervals":[17],"to":[18,28,35,62,87,103,124],"ensure":[19],"that":[20,50,163,181,194,220],"provides":[22],"a":[23,93,99,160,179,190],"smooth":[24],"riding":[25],"experience,":[26],"safety":[27],"passenger":[30],"causes":[32],"less":[33],"damage":[34],"vehicles.":[37],"conditions":[39,90,166,205,225],"are":[40],"affected":[41],"by":[42,169,197],"several":[43],"factors":[44],"such":[45],"as":[46],"weather":[47],"conditions,":[48],"accidents":[49],"have":[51,188],"occurred,":[52],"wear":[55],"tear,":[57],"hence":[58],"difficult":[61],"monitor":[63],"them":[64],"in":[65,155,167,206],"real-time.":[66,207],"Previous":[67],"research":[68],"works":[69],"on":[70,92,212,227],"road":[71,89,165,204,224],"monitoring":[72,203],"systems":[73],"can":[74,222],"broadly":[76],"classified":[77],"into":[78],"three":[79,134,142],"groups;":[80],"first":[82],"group":[83,139],"uses":[84,113],"sensor":[85,105],"data":[86,106,173,230],"detect":[88],"based":[91,211,226],"given":[94],"threshold,":[95],"second":[97],"employs":[98],"machine":[100,114],"learning":[101,115,128,170,192],"algorithm":[102,193,209],"acquire":[104],"from":[107,171,175],"vehicle,":[109],"while":[110],"third":[112],"server":[118,201],"then":[119],"transmits":[120],"result":[122],"back":[123],"vehicle.":[126,186],"The":[127,208],"algorithms":[129],"groups":[131],"two":[132],"provide":[135],"better":[136],"results":[137,218],"than":[138],"one.":[140],"Group":[141],"yields":[143],"more":[145],"accurate":[146],"results,":[147],"but":[148],"cost":[151],"time.":[153],"Therefore,":[154],"this":[156],"paper,":[157],"we":[158],"propose":[159],"novel":[161],"system":[162],"monitors":[164],"realtime":[168],"obtained":[174],"built-in":[176],"sensors":[177],"smartphone":[180],"mounted":[183],"inside":[184],"We":[187],"designed":[189],"lightweight":[191],"improves":[195],"accuracy":[196],"interacting":[198],"with":[199,231],"k-means":[213],"clustering":[214],"our":[216],"experimental":[217],"show":[219],"classify":[223],"accelerometer":[229],"88.67%":[232],"accuracy.":[233]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-11T08:14:18.477133","created_date":"2025-10-10T00:00:00"}
