{"id":"https://openalex.org/W3108788300","doi":"https://doi.org/10.1145/3384419.3430782","title":"ePerceptive","display_name":"ePerceptive","publication_year":2020,"publication_date":"2020-11-16","ids":{"openalex":"https://openalex.org/W3108788300","doi":"https://doi.org/10.1145/3384419.3430782","mag":"3108788300"},"language":"en","primary_location":{"id":"doi:10.1145/3384419.3430782","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3384419.3430782","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th Conference on Embedded Networked Sensor Systems","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/A5101566671","display_name":"Alessandro Montanari","orcid":"https://orcid.org/0000-0003-4444-6242"},"institutions":[{"id":"https://openalex.org/I4210098141","display_name":"Nokia (United Kingdom)","ror":"https://ror.org/00zpf0626","country_code":"GB","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I4210098141"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alessandro Montanari","raw_affiliation_strings":["Nokia Bell Labs, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, UK","institution_ids":["https://openalex.org/I4210098141"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075677116","display_name":"Manuja Sharma","orcid":"https://orcid.org/0000-0003-4404-7778"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Manuja Sharma","raw_affiliation_strings":["University of Washington"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023180768","display_name":"Dainius Jenkus","orcid":null},"institutions":[{"id":"https://openalex.org/I84884186","display_name":"Newcastle University","ror":"https://ror.org/01kj2bm70","country_code":"GB","type":"education","lineage":["https://openalex.org/I84884186"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dainius Jenkus","raw_affiliation_strings":["Newcastle University, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Newcastle University, UK","institution_ids":["https://openalex.org/I84884186"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074468932","display_name":"Mohammed Alloulah","orcid":"https://orcid.org/0000-0003-3724-7333"},"institutions":[{"id":"https://openalex.org/I4210098141","display_name":"Nokia (United Kingdom)","ror":"https://ror.org/00zpf0626","country_code":"GB","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I4210098141"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mohammed Alloulah","raw_affiliation_strings":["Nokia Bell Labs, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, UK","institution_ids":["https://openalex.org/I4210098141"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054150857","display_name":"Lorena Qendro","orcid":"https://orcid.org/0000-0002-3391-1658"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Lorena Qendro","raw_affiliation_strings":["University of Cambridge, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge, UK","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053438231","display_name":"Fahim Kawsar","orcid":"https://orcid.org/0000-0001-5057-9557"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Fahim Kawsar","raw_affiliation_strings":["TU Delft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TU Delft","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9138,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.9164777,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"382","last_page":"394"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11392","display_name":"Energy Harvesting in Wireless Networks","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11392","display_name":"Energy Harvesting in Wireless Networks","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11230","display_name":"Innovative Energy Harvesting Technologies","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"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/T13553","display_name":"Age of Information Optimization","score":0.9941999912261963,"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":0.7528150081634521},{"id":"https://openalex.org/keywords/microcontroller","display_name":"Microcontroller","score":0.5400209426879883},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5229647755622864},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5047065019607544},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.47021985054016113},{"id":"https://openalex.org/keywords/energy-harvesting","display_name":"Energy harvesting","score":0.46585091948509216},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4642447233200073},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.44410187005996704},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4282081723213196},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.42270755767822266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4155808091163635},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3885045051574707},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.15785470604896545},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1446559727191925}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7528150081634521},{"id":"https://openalex.org/C173018170","wikidata":"https://www.wikidata.org/wiki/Q165678","display_name":"Microcontroller","level":2,"score":0.5400209426879883},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5229647755622864},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5047065019607544},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.47021985054016113},{"id":"https://openalex.org/C101518730","wikidata":"https://www.wikidata.org/wiki/Q930236","display_name":"Energy harvesting","level":3,"score":0.46585091948509216},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4642447233200073},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.44410187005996704},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4282081723213196},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.42270755767822266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4155808091163635},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3885045051574707},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.15785470604896545},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1446559727191925},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3384419.3430782","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3384419.3430782","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th Conference on Embedded Networked Sensor Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1601612555","https://openalex.org/W1991781995","https://openalex.org/W2010689540","https://openalex.org/W2024338138","https://openalex.org/W2063744911","https://openalex.org/W2166996723","https://openalex.org/W2546536770","https://openalex.org/W2546724727","https://openalex.org/W2554302513","https://openalex.org/W2606637138","https://openalex.org/W2612445135","https://openalex.org/W2620172851","https://openalex.org/W2620872300","https://openalex.org/W2761242776","https://openalex.org/W2785201743","https://openalex.org/W2790352815","https://openalex.org/W2792808617","https://openalex.org/W2794259430","https://openalex.org/W2883386984","https://openalex.org/W2887117815","https://openalex.org/W2895082331","https://openalex.org/W2897268228","https://openalex.org/W2897303255","https://openalex.org/W2898269841","https://openalex.org/W2950027350","https://openalex.org/W2962677625","https://openalex.org/W2962861284","https://openalex.org/W2962988160","https://openalex.org/W2963122961","https://openalex.org/W2964299589","https://openalex.org/W2972538264","https://openalex.org/W2980856918","https://openalex.org/W2983071751","https://openalex.org/W2987383153","https://openalex.org/W2988271927","https://openalex.org/W2988851551","https://openalex.org/W2990958518","https://openalex.org/W2995722708","https://openalex.org/W3011214831","https://openalex.org/W3103203826"],"related_works":["https://openalex.org/W4316095964","https://openalex.org/W2383001583","https://openalex.org/W2131084560","https://openalex.org/W2771395446","https://openalex.org/W3112038843","https://openalex.org/W3094215878","https://openalex.org/W2088310429","https://openalex.org/W3209836052","https://openalex.org/W2898145991","https://openalex.org/W3184852948"],"abstract_inverted_index":{"For":[0],"long,":[1],"we":[2,20,31,89],"have":[3],"studied":[4],"tiny":[5],"energy":[6,41,79,113,164,213],"harvesters":[7],"to":[8,57,61,71,77,80,110,131,154,186,241,244],"liberate":[9],"sensors":[10,25,220],"from":[11,54],"batteries.":[12],"With":[13],"remarkable":[14],"progress":[15],"in":[16,75,108,160,197],"embedded":[17,102],"deep":[18,127],"learning,":[19],"are":[21,32],"now":[22],"re-imagining":[23],"these":[24,169,218],"as":[26],"intelligent":[27],"compute":[28],"nodes.":[29],"Naturally,":[30],"approaching":[33],"a":[34,49,97,125,147,172,245,250],"crossroad":[35],"where":[36],"sensor":[37],"intelligence":[38,46,73],"is":[39,70],"meeting":[40],"autonomy":[42],"enabling":[43],"maintenance-free":[44],"swarm":[45],"and":[47,93,178,201,208,211,224],"unleashing":[48],"plethora":[50],"of":[51,65,95,163,168,195,217,232,254],"applications":[52],"ranging":[53],"precision":[55],"agriculture":[56],"ubiquitous":[58],"asset":[59],"tracking":[60],"infrastructure":[62],"monitoring.":[63],"One":[64],"the":[66,82,91,111,161,183,187,193,229,236],"critical":[67],"challenges,":[68],"however,":[69],"adapt":[72],"fidelity":[74,106],"response":[76],"available":[78,188],"maximise":[81],"overall":[83],"system":[84],"availability.":[85],"To":[86],"this":[87],"end,":[88],"present":[90],"design":[92],"implementation":[94],"ePerceptive:":[96],"novel":[98],"framework":[99],"for":[100],"best-effort":[101],"intelligence,":[103],"i.e.,":[104],"inference":[105,176,237],"varies":[107],"proportion":[109],"instantaneous":[112],"supplied.":[114],"ePerceptive":[115,196,233],"operates":[116],"on":[117,133],"two":[118],"core":[119],"principles.":[120],"First,":[121],"it":[122,145],"enables":[123],"training":[124],"single":[126],"neural":[128],"network":[129],"(DNN)":[130],"operate":[132],"multiple":[134,152,222],"input":[135],"resolutions":[136],"without":[137],"compromising":[138],"accuracy":[139,180,252],"or":[140],"incurring":[141],"memory":[142],"overhead.":[143],"Second,":[144],"modifies":[146],"DNN":[148],"architecture":[149],"by":[150,239],"injecting":[151],"exits":[153],"guarantee":[155],"valid,":[156],"albeit":[157],"lower-fidelity":[158],"inferences":[159],"event":[162],"interruption.":[165],"The":[166],"combination":[167],"techniques":[170],"offers":[171],"smooth":[173],"adaptation":[174,231],"between":[175],"latency":[177],"recognition":[179],"while":[181,248],"matching":[182],"computational":[184],"load":[185],"power":[189],"budget.":[190],"We":[191],"report":[192],"manifestation":[194],"designing":[198],"batteryless":[199,219],"cameras":[200],"microphones":[202],"built":[203],"with":[204,221],"TI":[205],"MSP430":[206],"MCU":[207],"off-the-shelf":[209],"RF":[210],"solar":[212],"harvesters.":[214],"Our":[215],"evaluation":[216],"vision":[223],"acoustic":[225],"workloads":[226],"suggest":[227],"that":[228],"dynamic":[230],"can":[234],"increase":[235],"throughput":[238],"up":[240],"80%":[242],"compared":[243],"static":[246],"baseline":[247],"ensuring":[249],"maximum":[251],"drop":[253],"less":[255],"than":[256],"6%.":[257]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2020-12-07T00:00:00"}
