{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:15:11Z","timestamp":1792790111357,"version":"4.5.0"},"reference-count":20,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information & Communications Technology Planning & Evaluation","award":["RS-2026-25523610"],"award-info":[{"award-number":["RS-2026-25523610"]}],"id":[{"id":"https:\/\/ror.org\/01g0hqq23","id-type":"ROR","asserted-by":"crossref"}]},{"award":["RS-2026-25523610"],"award-info":[{"award-number":["RS-2026-25523610"]}],"id":[{"id":"https:\/\/ror.org\/01g0hqq23","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Future Internet of Things (IoT) systems increasingly rely on multiple sensing modalities to support sensing-intensive tasks such as localization and smart healthcare. However, always collecting all available sensing data can waste energy and communication resources, whereas aggressive sensing reduction may degrade task-level performance. The key challenge is therefore to adapt sensing operations according to the requirements of the target task while avoiding unnecessary sensing effort. To address this challenge, this article presents Task-Aware Multi-Modal Sensing Control (TA-MSC) for IoT systems. TA-MSC treats sensing operation as a task-level-output-driven closed-loop control problem that identifies a low-cost sensing configuration while satisfying a task-level performance requirement. By analyzing task-level outputs from the inference process, TA-MSC evaluates whether the current sensing configuration is sufficient and updates modality-wise sensing levels accordingly. The selected sensing configuration can be realized through practical control dimensions such as modality selection, sensor activation, channel adjustment, and duty-cycle management. We instantiate TA-MSC through a representative multi-modal localization case study, in which sensing energy is evaluated using literature-based sensing and transmission-energy models rather than direct hardware measurements. In the evaluated cross-device analysis, TA-MSC reduces the mean total modeled sensing energy by 52.8% relative to All-Max, while achieving a Top-1 localization accuracy of 96.14% compared with 97.37% for All-Max and a confidence-satisfaction rate of 98.94% compared with 97.68% for All-Max. These results indicate substantial modeled-energy savings with a modest Top-1 accuracy tradeoff, while the confidence-based task condition is satisfied more frequently.<\/jats:p>","DOI":"10.3390\/s26196001","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T15:28:43Z","timestamp":1790090923000},"page":"6001","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Task-Aware Multi-Modal Sensing Control for IoT Systems"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5247-0296","authenticated-orcid":false,"given":"Yeunwoong","family":"Kyung","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2424-6722","authenticated-orcid":false,"given":"Dogyu","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1042-8050","authenticated-orcid":false,"given":"Jihoon","family":"Sung","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Jeonbuk National University, 567 Baekje-daero, Jeonju-si 54896, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3274-8550","authenticated-orcid":false,"given":"Geunho","family":"Lee","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1844-8079","authenticated-orcid":false,"given":"Jeongwook","family":"Seo","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Hanshin University, Osan 18101, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9067-445X","authenticated-orcid":false,"given":"Haneul","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Kyung Hee University, Yongin 17104, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Jaehyuk","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Seowon University, Cheongju-si 28674, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/s41597-021-00832-y","article-title":"OutFin, a Multi-Device and Multi-Modal Dataset for Outdoor Localization Based on the Fingerprinting Approach","volume":"8","author":"Alhomayani","year":"2021","journal-title":"Sci. Data"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20737","DOI":"10.1109\/JSEN.2023.3242759","article-title":"SALA-IoT: Self-Reduced Internet of Things With Learning Automaton Sleep Scheduling Algorithm","volume":"23","author":"Sangaiah","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9662","DOI":"10.1109\/JIOT.2023.3234582","article-title":"Optimal Sleep Scheduling for Energy-Efficient AoI Optimization in Industrial Internet of Things","volume":"10","author":"Cao","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ben-Aboud, Y., Licea, D.B., Ghogho, M., and Kobbane, A. (2021, January 14\u201318). On adaptive sampling algorithms for IoT devices. Proceedings of the ICC 2021\u2014IEEE International Conference on Communications, Montreal (Virtual), QC, Canada.","DOI":"10.1109\/ICC42927.2021.9500326"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Giouroukis, D., Dadiani, A., Traub, J., Zeuch, S., and Markl, V. (2020, January 13\u201317). A survey of adaptive sampling and filtering algorithms for the internet of things. Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems, Montreal, QC, Canada.","DOI":"10.1145\/3401025.3403777"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Paik, M., and Ko, H. (2020). Distributed Group Location Update Algorithm for Massive Machine Type Communication. Sensors, 20.","DOI":"10.3390\/s20247336"},{"key":"ref_7","first-page":"273","article-title":"Age- and Correlation-Aware Information Gathering","volume":"11","author":"Dobre","year":"2021","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.future.2024.03.021","article-title":"Uncertainty-aware autonomous sensing with deep reinforcement learning","volume":"156","author":"Murad","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hor, S., El-Khamy, M., Zhou, Y., Arbabian, A., and Lim, S. (2024, January 7\u20139). CM-ASAP: Cross-Modality Adaptive Sensing and Perception for Efficient Hand Gesture Recognition. Proceedings of the 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA.","DOI":"10.1109\/MIPR62202.2024.00039"},{"key":"ref_10","unstructured":"Liu, Y., Fan, Y., Zhao, M., and Jiang, X. (2026). ReSPEC: A Framework for Online Multispectral Sensor Reconfiguration in Dynamic Environments. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6125","DOI":"10.1109\/JIOT.2018.2869671","article-title":"Calibrated Data Simplification for Energy-Efficient Location Sensing in Internet of Things","volume":"6","author":"Zhou","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102927","DOI":"10.1016\/j.adhoc.2022.102927","article-title":"IoT Sensor Selection for Target Localization: A Reinforcement Learning based Approach","volume":"134","author":"Shurrab","year":"2022","journal-title":"Ad Hoc Netw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13551","DOI":"10.1109\/JSEN.2021.3069264","article-title":"Learning-Based Adaptive Sensor Selection Framework for Multi-Sensing WSN","volume":"21","author":"Ghosh","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Naeini, E.K., Shahhosseini, S., Kanduri, A., Liljeberg, P., Rahmani, A.M., and Dutt, N. (2022, January 14\u201323). AMSER: Adaptive Multimodal Sensing for Energy Efficient and Resilient eHealth Systems. Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium.","DOI":"10.23919\/DATE54114.2022.9774590"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Huang, J., Qian, F., Gerber, A., Mao, Z.M., Sen, S., and Spatscheck, O. (2012, January 25\u201329). A close examination of performance and power characteristics of 4G LTE networks. Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, Low Wood Bay, UK.","DOI":"10.1145\/2307636.2307658"},{"key":"ref_16","unstructured":"Li, T. (2014). Cutting Wi-Fi Scan Tax for Smart Devices. [Master\u2019s Thesis, Dartmouth College]."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Montanari, A., Nawaz, S., Mascolo, C., and Sailer, K. (2017, January 13\u201317). A Study of Bluetooth Low Energy performance for human proximity detection in the workplace. Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), Kona, HI, USA.","DOI":"10.1109\/PERCOM.2017.7917855"},{"key":"ref_18","unstructured":"Carroll, A. (2017). Understanding and Reducing Smartphone Energy Consumption. [Ph.D. Thesis, University of New South Wales]. Available online: https:\/\/www.trustworthy.systems\/publications\/papers\/Carroll%3Aphd.pdf."},{"key":"ref_19","unstructured":"Chen, Y., Dong, J., and Wang, Z. (2021). A Primal-Dual Approach to Constrained Markov Decision Processes. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Won, H.-M., Lee, J., and Oh, J. (2025). Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localization. Technologies, 13.","DOI":"10.3390\/technologies13090386"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6001\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T15:29:47Z","timestamp":1790090987000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6001"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,22]]},"references-count":20,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196001"],"URL":"https:\/\/doi.org\/10.3390\/s26196001","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,22]]}}}