{"id":"https://openalex.org/W7123268029","doi":"https://doi.org/10.48550/arxiv.2601.05984","title":"Community-Based Model Sharing and Generalisation: Anomaly Detection in IoT Temperature Sensor Networks","display_name":"Community-Based Model Sharing and Generalisation: Anomaly Detection in IoT Temperature Sensor Networks","publication_year":2026,"publication_date":"2026-01-09","ids":{"openalex":"https://openalex.org/W7123268029","doi":"https://doi.org/10.48550/arxiv.2601.05984"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.05984","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.05984","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.05984","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035722384","display_name":"Sahibzada Saadoon Hammad","orcid":"https://orcid.org/0000-0002-2524-6299"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hammad, Sahibzada Saadoon","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Guijarro, Joaqu\u00edn Huerta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guijarro, Joaqu\u00edn Huerta","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122756133","display_name":"Francisco Ramos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramos, Francisco","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122771464","display_name":"Michael Gould Carlson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carlson, Michael Gould","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Oliver, Sergio Trilles","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oliver, Sergio Trilles","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5035722384"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7085999846458435,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7085999846458435,"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"}},{"id":"https://openalex.org/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.051500000059604645,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.030500000342726707,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.6845999956130981},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.6183000206947327},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.550000011920929},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4771000146865845},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.46470001339912415},{"id":"https://openalex.org/keywords/silhouette","display_name":"Silhouette","score":0.4327000081539154},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.36880001425743103},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.36419999599456787},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3571000099182129},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.35280001163482666}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6845999956130981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.675000011920929},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.6183000206947327},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6168000102043152},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.550000011920929},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4771000146865845},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4489000141620636},{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41609999537467957},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36169999837875366},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.35269999504089355},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C2780615140","wikidata":"https://www.wikidata.org/wiki/Q920419","display_name":"Upgrade","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3434999883174896},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C37054046","wikidata":"https://www.wikidata.org/wiki/Q641888","display_name":"Elevation (ballistics)","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.28940001130104065},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.28380000591278076},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.27239999175071716},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.05984","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.05984","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.05984","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.05984","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8316967487335205,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,131],"rapid":[1],"deployment":[2],"of":[3,5,25,128,139,173],"Internet":[4],"Things":[6],"(IoT)":[7],"devices":[8,40],"has":[9],"led":[10],"to":[11,182],"large-scale":[12],"sensor":[13,36,188],"networks":[14,37],"that":[15,69],"monitor":[16],"environmental":[17,45],"and":[18,44,82,97,103,115,123,142,181],"urban":[19],"phenomena":[20],"in":[21,177],"real":[22],"time.":[23],"Communities":[24],"Interest":[26],"(CoIs)":[27],"provide":[28],"a":[29,65,153],"promising":[30],"paradigm":[31,58],"for":[32],"organising":[33],"heterogeneous":[34],"IoT":[35,187],"by":[38,59],"grouping":[39,60],"with":[41,111],"similar":[42],"operational":[43],"characteristics.":[46],"This":[47],"work":[48],"presents":[49],"an":[50],"anomaly":[51],"detection":[52],"framework":[53],"based":[54,90],"on":[55,91,117,135],"the":[56,92,120,124,140,158,168,171],"CoI":[57],"sensors":[61],"into":[62],"communities":[63,164],"using":[64,78,107],"fused":[66],"similarity":[67],"matrix":[68],"incorporates":[70],"temporal":[71],"correlations":[72],"via":[73],"Spearman":[74],"coefficients,":[75],"spatial":[76],"proximity":[77],"Gaussian":[79],"distance":[80],"decay,":[81],"elevation":[83],"similarities.":[84],"For":[85],"each":[86],"community,":[87],"representative":[88,126],"stations":[89,118,127],"best":[93,125],"silhouette":[94],"are":[95,105,133,144,165],"selected":[96],"three":[98],"autoencoder":[99],"architectures":[100],"(BiLSTM,":[101],"LSTM,":[102],"MLP)":[104],"trained":[106,134],"Bayesian":[108],"hyperparameter":[109],"optimization":[110],"expanding":[112],"window":[113],"cross-validation":[114],"tested":[116],"from":[119],"same":[121],"cluster":[122],"other":[129],"clusters.":[130],"models":[132],"normal":[136],"temperature":[137],"patterns":[138],"data":[141],"anomalies":[143],"detected":[145],"through":[146],"reconstruction":[147],"error":[148],"analysis.":[149],"Experimental":[150],"results":[151,169],"show":[152],"robust":[154],"within-community":[155],"performance":[156],"across":[157,163,186],"evaluated":[159],"configurations,":[160],"while":[161],"variations":[162],"observed.":[166],"Overall,":[167],"support":[170],"applicability":[172],"community-based":[174],"model":[175,184],"sharing":[176],"reducing":[178],"computational":[179],"overhead":[180],"analyse":[183],"generalisability":[185],"networks.":[189]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2026-01-13T00:00:00"}
