{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T23:55:45Z","timestamp":1758326145046,"version":"3.44.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["RS-2024-00451716"],"award-info":[{"award-number":["RS-2024-00451716"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2025-RS-2020-II201795"],"award-info":[{"award-number":["IITP-2025-RS-2020-II201795"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The scope of the Internet of Things (IoT) environment has been expanding from private to public spaces, where selecting the most appropriate service by predicting the service quality has become a timely problem. However, IoT services can be physically affected by (1) uncertain environmental factors such as obstacles and (2) interference among services in the same environment while interacting with users. Using the traditional modeling-based approach, analyzing the influence of such factors on the service quality requires modeling efforts and lacks generalizability. In this study, we propose <jats:italic>Learning Physical Environment factors based on the Attention mechanism to Select Services for UsERs (PLEASSURE)<\/jats:italic>, a novel framework that selects IoT services by learning the uncertain influence and predicting the long-term quality from the users\u2019 feedback without additional modeling. Furthermore, we propose <jats:italic>fingerprint attention<\/jats:italic> that extends the attention mechanism to capture the physical interference among services. We evaluate PLEASSURE by simulating various IoT environments with mobile users and IoT services. The results show that PLEASSURE outperforms the baseline algorithms in rewards consisting of users\u2019 feedback on satisfaction and interference.<\/jats:p>","DOI":"10.1007\/s10489-025-06472-8","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T02:53:22Z","timestamp":1743216802000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Effective selection of public IoT services by learning uncertain environmental factors using fingerprint attention"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5887-5948","authenticated-orcid":false,"given":"KyeongDeok","family":"Baek","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3843-263X","authenticated-orcid":false,"given":"In-Young","family":"Ko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,26]]},"reference":[{"issue":"3","key":"6472_CR1","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1007\/s10489-020-01861-7","volume":"51","author":"S-S Fang","year":"2021","unstructured":"Fang S-S, Chai Z-Y, Li Y-L (2021) Dynamic multi-objective evolutionary algorithm for iot services. Appl Intell 51(3):1177\u20131200","journal-title":"Appl Intell"},{"key":"6472_CR2","doi-asserted-by":"crossref","unstructured":"Benazzouz Y, Munilla C, G\u00fcnalp O, Gallissot M, G\u00fcrgen L (2014) Sharing user iot devices in the cloud. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), pp 373\u2013374. IEEE","DOI":"10.1109\/WF-IoT.2014.6803193"},{"issue":"1","key":"6472_CR3","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/MC.2015.12","volume":"48","author":"R Want","year":"2015","unstructured":"Want R, Schilit BN, Jenson S (2015) Enabling the internet of things. Computer 48(1):28\u201335","journal-title":"Computer"},{"issue":"9","key":"6472_CR4","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1145\/3464960","volume":"64","author":"A Bouguettaya","year":"2021","unstructured":"Bouguettaya A, Sheng QZ, Benatallah B, Neiat AG, Mistry S, Ghose A, Nepal S, Yao L (2021) An internet of things service roadmap. Commun ACM 64(9):86\u201395","journal-title":"Commun ACM"},{"key":"6472_CR5","doi-asserted-by":"crossref","unstructured":"Issarny V, Bouloukakis G, Georgantas N, Billet B (2016) Revisiting service-oriented architecture for the iot: a middleware perspective. In: International conference on service-oriented computing, pp 3\u201317. Springer","DOI":"10.1007\/978-3-319-46295-0_1"},{"issue":"1","key":"6472_CR6","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1080\/13683500.2020.1730309","volume":"24","author":"C-F Chen","year":"2021","unstructured":"Chen C-F, Huang C-Y (2021) Investigating the effects of a shared bike for tourism use on the tourist experience and its consequences. Curr Issues Tour 24(1):134\u2013148","journal-title":"Curr Issues Tour"},{"key":"6472_CR7","doi-asserted-by":"crossref","unstructured":"Coenen J, Wouters N, Moere AV (2016) Synchronized wayfinding on multiple consecutively situated public displays. In: Proceedings of the 5th ACM international symposium on pervasive displays, pp 182\u2013196","DOI":"10.1145\/2914920.2929906"},{"key":"6472_CR8","doi-asserted-by":"crossref","unstructured":"Knierim P, Maurer S, Wolf K, Funk M (2018) Quadcopter-projected in-situ navigation cues for improved location awareness. In: Proceedings of the 2018 CHI conference on human factors in computing systems, pp 1\u20136","DOI":"10.1145\/3173574.3174007"},{"key":"6472_CR9","doi-asserted-by":"crossref","unstructured":"Alqahtani A, Alsubai S, Bhatia M (2024) Applied artificial intelligence framework for smart evacuation in industrial disasters. Appl Intell 1\u201316","DOI":"10.1007\/s10489-024-05550-7"},{"issue":"12","key":"6472_CR10","doi-asserted-by":"publisher","first-page":"15261","DOI":"10.1007\/s10489-022-04223-7","volume":"53","author":"Q Liu","year":"2023","unstructured":"Liu Q, Xu H, He B, Yuan H, Liu Z, Fan S, Xu J, Li T, Li J, Wang M et al (2023) A novel context inconsistency elimination algorithm based on the optimized dempster-shafer evidence theory for context-awareness systems. Appl Intell 53(12):15261\u201315277","journal-title":"Appl Intell"},{"key":"6472_CR11","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv:1409.0473"},{"key":"6472_CR12","unstructured":"Sutton RS, Barto AG (2018) Reinforcement Learning: An Introduction. MIT press"},{"issue":"2","key":"6472_CR13","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/TSMCC.2007.913919","volume":"38","author":"L Busoniu","year":"2008","unstructured":"Busoniu L, Babuska R, De Schutter B (2008) A comprehensive survey of multiagent reinforcement learning. IEEE Trans Syst Man Cybern Part C (Appl Rev) 38(2):156\u2013172","journal-title":"IEEE Trans Syst Man Cybern Part C (Appl Rev)"},{"issue":"23","key":"6472_CR14","doi-asserted-by":"publisher","first-page":"28832","DOI":"10.1007\/s10489-023-05065-7","volume":"53","author":"Z Chai","year":"2023","unstructured":"Chai Z, Hou H, Li Y (2023) A dynamic queuing model based distributed task offloading algorithm using deep reinforcement learning in mobile edge computing. Appl Intell 53(23):28832\u201328847","journal-title":"Appl Intell"},{"issue":"1","key":"6472_CR15","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1007\/s10489-022-03482-8","volume":"53","author":"X Zhang","year":"2023","unstructured":"Zhang X, Wang Y (2023) Deepmecagent: multi-agent computing resource allocation for uav-assisted mobile edge computing in distributed iot system. Appl Intell 53(1):1180\u20131191","journal-title":"Appl Intell"},{"issue":"9","key":"6472_CR16","doi-asserted-by":"publisher","first-page":"2419","DOI":"10.1007\/s10994-021-05961-4","volume":"110","author":"G Dulac-Arnold","year":"2021","unstructured":"Dulac-Arnold G, Levine N, Mankowitz DJ, Li J, Paduraru C, Gowal S, Hester T (2021) Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. Mach Learn 110(9):2419\u20132468","journal-title":"Mach Learn"},{"key":"6472_CR17","doi-asserted-by":"crossref","unstructured":"Moghaddam M, Davis JG (2014) Service selection in web service composition: A comparative review of existing approaches. Web services foundations, pp 321\u2013346","DOI":"10.1007\/978-1-4614-7518-7_13"},{"key":"6472_CR18","doi-asserted-by":"crossref","unstructured":"Yen I, Bastani F, Hwang S-Y, Zhu W, Zhou G et al (2017) From software services to iot services: the modeling perspective. In: International conference on serviceology, pp 215\u2013223. Springer","DOI":"10.1007\/978-3-319-61240-9_20"},{"issue":"6","key":"6472_CR19","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1007\/s10796-016-9650-1","volume":"19","author":"X Jin","year":"2017","unstructured":"Jin X, Chun S, Jung J, Lee K-H (2017) A fast and scalable approach for iot service selection based on a physical service model. Inf Syst Front 19(6):1357\u20131372","journal-title":"Inf Syst Front"},{"issue":"9","key":"6472_CR20","doi-asserted-by":"publisher","first-page":"7497","DOI":"10.1109\/JIOT.2020.3040775","volume":"8","author":"A Altaf","year":"2020","unstructured":"Altaf A, Abbas H, Iqbal F, Khan MMZM, Daneshmand M (2020) Robust, secure, and adaptive trust-oriented service selection in iot-based smart buildings. IEEE Internet Things J 8(9):7497\u20137509","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"6472_CR21","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.1109\/JIOT.2022.3211270","volume":"10","author":"K Baek","year":"2023","unstructured":"Baek K, Ko I-Y (2023) Dynamic and effect-driven output service selection for iot environments using deep reinforcement learning. IEEE Internet Things J 10(4):3339\u20133355","journal-title":"IEEE Internet Things J"},{"issue":"5","key":"6472_CR22","doi-asserted-by":"publisher","first-page":"3833","DOI":"10.1109\/JIOT.2020.2975418","volume":"7","author":"D Minovski","year":"2020","unstructured":"Minovski D, \u00c5hlund C, Mitra K (2020) Modeling quality of iot experience in autonomous vehicles. IEEE Internet Things J 7(5):3833\u20133849","journal-title":"IEEE Internet Things J"},{"issue":"2","key":"6472_CR23","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MNET.2010.5430138","volume":"24","author":"P Brooks","year":"2010","unstructured":"Brooks P, Hestnes B (2010) User measures of quality of experience: why being objective and quantitative is important. IEEE Netw 24(2):8\u201313","journal-title":"IEEE Netw"},{"issue":"5","key":"6472_CR24","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1109\/TMC.2013.155","volume":"14","author":"K Mitra","year":"2013","unstructured":"Mitra K, Zaslavsky A, \u00c5hlund C (2013) Context-aware qoe modelling, measurement, and prediction in mobile computing systems. IEEE Trans Mob Comput 14(5):920\u2013936","journal-title":"IEEE Trans Mob Comput"},{"key":"6472_CR25","doi-asserted-by":"publisher","first-page":"19507","DOI":"10.1109\/ACCESS.2022.3149592","volume":"10","author":"G Kougioumtzidis","year":"2022","unstructured":"Kougioumtzidis G, Poulkov V, Zaharis ZD, Lazaridis PI (2022) A survey on multimedia services qoe assessment and machine learning-based prediction. IEEE Access 10:19507\u201319538","journal-title":"IEEE Access"},{"issue":"2s","key":"6472_CR26","first-page":"1","volume":"14","author":"L Skorin-Kapov","year":"2018","unstructured":"Skorin-Kapov L, Varela M, Ho\u00dffeld T, Chen K-T (2018) A survey of emerging concepts and challenges for qoe management of multimedia services. ACM Trans Multimed Comput Commun Appl (TOMM) 14(2s):1\u201329","journal-title":"ACM Trans Multimed Comput Commun Appl (TOMM)"},{"issue":"2","key":"6472_CR27","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1007\/s10462-020-09872-z","volume":"54","author":"L Purohit","year":"2021","unstructured":"Purohit L, Kumar S (2021) A study on evolutionary computing based web service selection techniques. Artif Intell Rev 54(2):1117\u20131170","journal-title":"Artif Intell Rev"},{"key":"6472_CR28","doi-asserted-by":"publisher","first-page":"21787","DOI":"10.1109\/ACCESS.2019.2894683","volume":"7","author":"F Dahan","year":"2019","unstructured":"Dahan F, Mathkour H, Arafah M (2019) Two-step artificial bee colony algorithm enhancement for qos-aware web service selection problem. IEEE Access 7:21787\u201321794","journal-title":"IEEE Access"},{"issue":"5","key":"6472_CR29","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/TSC.2017.2727050","volume":"13","author":"L Ren","year":"2017","unstructured":"Ren L, Wang W, Xu H (2017) A reinforcement learning method for constraint-satisfied services composition. IEEE Trans Serv Comput 13(5):786\u2013800","journal-title":"IEEE Trans Serv Comput"},{"key":"6472_CR30","doi-asserted-by":"crossref","unstructured":"Wang H, Wu Q, Chen X, Yu Q, Zheng Z, Bouguettaya A (2014) Adaptive and dynamic service composition via multi-agent reinforcement learning. In: 2014 IEEE international conference on web services, pp 447\u2013454. IEEE","DOI":"10.1109\/ICWS.2014.70"},{"key":"6472_CR31","doi-asserted-by":"crossref","unstructured":"Zhang X, Tian S, Liu Y, Cao Z (2023) User location-aware edge services selection based on generative adversarial network and improved ant colony algorithm. Appl Intell 53(11):13643\u201313664","DOI":"10.1007\/s10489-022-04093-z"},{"key":"6472_CR32","doi-asserted-by":"crossref","unstructured":"Wang X, Ye J, Lui JC (2023) Online learning aided decentralized multi-user task offloading for mobile edge computing. IEEE Trans Mob Comput","DOI":"10.1109\/TMC.2023.3275851"},{"key":"6472_CR33","first-page":"11853","volume":"33","author":"M Zhou","year":"2020","unstructured":"Zhou M, Liu Z, Sui P, Li Y, Chung YY (2020) Learning implicit credit assignment for cooperative multi-agent reinforcement learning. Adv Neural Inf Process Syst 33:11853\u201311864","journal-title":"Adv Neural Inf Process Syst"},{"key":"6472_CR34","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems, vol 30"},{"issue":"7540","key":"6472_CR35","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533","journal-title":"Nature"},{"key":"6472_CR36","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp 1273\u20131282. PMLR"},{"key":"6472_CR37","doi-asserted-by":"publisher","first-page":"100467","DOI":"10.1016\/j.cosrev.2022.100467","volume":"44","author":"E Schiller","year":"2022","unstructured":"Schiller E, Aidoo A, Fuhrer J, Stahl J, Zi\u00f6rjen M, Stiller B (2022) Landscape of iot security. Comput Sci Rev 44:100467","journal-title":"Comput Sci Rev"},{"key":"6472_CR38","unstructured":"Steinparz CA, Schmied T, Paischer F, Dinu M-C, Patil VP, Bitto-Nemling A, Eghbal-zadeh H, Hochreiter S (2022) Reactive exploration to cope with non-stationarity in lifelong reinforcement learning. In: Conference on lifelong learning agents, pp 441\u2013469. PMLR"},{"key":"6472_CR39","doi-asserted-by":"crossref","unstructured":"Kim W, Lee S, Chang Y, Lee T, Hwang I, Song J (2021) Hivemind: social control-and-use of iot towards democratization of public spaces. In: Proceedings of the 19th annual international conference on mobile systems, applications, and services, pp 467\u2013482","DOI":"10.1145\/3458864.3466626"},{"key":"6472_CR40","doi-asserted-by":"crossref","unstructured":"Karapetyan A, Chau SC-K, Elbassioni K, Khonji M, Dababseh E (2018) Smart lighting control using oblivious mobile sensors. In: Proceedings of the 5th conference on systems for built environments, pp 158\u2013167","DOI":"10.1145\/3276774.3276788"},{"key":"6472_CR41","doi-asserted-by":"crossref","unstructured":"Kim S, Ko I-Y (2022) A conversational approach for modifying service mashups in iot environments. In: Proceedings of the 2022 CHI conference on human factors in computing systems, pp 1\u201316","DOI":"10.1145\/3491102.3517655"},{"key":"6472_CR42","doi-asserted-by":"publisher","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. https:\/\/doi.org\/10.48550\/arxiv.1412.6980. arXiv:1412.6980","DOI":"10.48550\/arxiv.1412.6980"},{"key":"6472_CR43","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M (2013) Playing atari with deep reinforcement learning. arXiv:1312.5602"},{"key":"6472_CR44","doi-asserted-by":"crossref","unstructured":"Baek K-D, Ko I-Y (2017) Spatially cohesive service discovery and dynamic service handover for distributed iot environments. In: International conference on web engineering, pp 60\u201378. Springer","DOI":"10.1007\/978-3-319-60131-1_4"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06472-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06472-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06472-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:38:05Z","timestamp":1758310685000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06472-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,26]]},"references-count":44,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6472"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06472-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,3,26]]},"assertion":[{"value":"14 March 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"578"}}