survey

Sensing within Smart Buildings: A Survey

Authors:

Charith PereraAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 13s

Article No.: 297, Pages 1 - 35

https://doi.org/10.1145/3596600

Published: 13 July 2023 Publication History

Abstract

Increasingly, buildings are being fitted with sensors for the needs of different sectors, such as education, industry and business. Using Internet of Things devices combined with analysis of data being generated by these devices, it is possible to infer a number of metrics, e.g., building occupancy and activities of occupants. The information thus gathered can be used to develop software applications to support energy management, occupant comfort, and space utilization. This survey explores the use of sensors in smart building environments, identifying different approaches to employ sensors in buildings. The most commonly used data-driven approaches for activity recognition in such buildings is also investigated, concluding by highlighting current research challenges and future research directions in this area.

References

[1]

(n.d.). Connected campus. Retrieved January 26, 2023 from https://cto.berkeley.edu/innovation/connected-campus.

[2]

(n.d.). Sint-Maarten Hospital, Belgium. Retrieved January 7, 2023 from https://www.siemens.com/global/en/products/buildings/references/sint-maarten-hospital.html

[3]

MMR. 2020. Global Smart Building Market Key Trends (2017–2024)—Market Size. Retrieved from https://www.maximizemarketresearch.com/market-report/global-smart-building-market-key-trends/6854/.

[4]

Muhammad Raisul Alam, Mamun Bin Ibne Reaz, and Mohd Alauddin Mohd Ali. 2012. A review of smart homes-past, present, and future. IEEE Trans. Syst., Man, Cybernet., Part C (2012).

Digital Library

[5]

Alaa Alhamoud, Vaidehi Muradi, Doreen Böhnstedt, and Ralf Steinmetz. 2016. Activity recognition in multi-user environments using techniques of multi-label classification. In Proceedings of the 6th Conference on the Internet of Things.

Digital Library

[6]

Timothy M. Amado and Jennifer C. Dela Cruz. 2018. Development of machine learning-based predictive models for air quality monitoring and characterization. In Proceedings of the IEEE Region 10 Conference (TENCON’18). IEEE.

[7]

Krzysztof Arendt, Aslak Johansen, Bo Nørregaard Jørgensen, Mikkel Baun Kjærgaard, Claudio Giovanni Mattera, Fisayo Caleb Sangogboye, Jens Hjort Schwee, and Christian T. Veje. 2018. Room-level occupant counts, airflow and co2 data from an office building. In Proceedings of the 1st Workshop on Data Acquisition to Analysis.

Digital Library

[8]

Muhammad Azam, Marion Blayo, Jean-Simon Venne, and Michel Allegue-Martinez. 2019. Occupancy estimation using wifi motion detection via supervised machine learning algorithms. In Proceedings of the GlobalSIP. IEEE.

[9]

Omar Aziz, Colin M. Russell, Edward J. Park, and Stephen N. Robinovitch. 2014. The effect of window size and lead time on pre-impact fall detection accuracy using support vector machine analysis of waist mounted inertial sensor data. In Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE.

[10]

Bharathan Balaji, Jian Xu, Anthony Nwokafor, Rajesh Gupta, and Yuvraj Agarwal. 2013. Sentinel: Occupancy-based HVAC actuation using existing WiFi infrastructure within commercial buildings. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems.

Digital Library

[11]

Abdelkareem Bedri, Apoorva Verlekar, Edison Thomaz, Valerie Avva, and Thad Starner. 2015. A wearable system for detecting eating activities with proximity sensors in the outer ear. In Proceedings of the ACM International Symposium on Wearable Computers.

Digital Library

[12]

Sayantani Bhattacharya, S. Sridevi, and R. Pitchiah. 2012. Indoor air quality monitoring using wireless sensor network. In Proceedings of the 6th International Conference on Sensing Technology (ICST’12). IEEE.

[13]

Shengjie Bi, Tao Wang, Ellen Davenport, Ronald Peterson, Ryan Halter, Jacob Sorber, and David Kotz. 2017. Toward a wearable sensor for eating detection. In Proceedings of the Workshop on Wearable Systems and Applications.

Digital Library

[14]

Oscar Blanco-Novoa, Tiago M. Fernández-Caramés, Paula Fraga-Lamas, and Luis Castedo. 2018. A cost-effective IoT system for monitoring indoor radon gas concentration. Sensors (2018).

[15]

Ben Buurman, Joarder Kamruzzaman, Gour Karmakar, and Syed Islam. 2020. Low-power wide-area networks: Design goals, architecture, suitability to use cases and research challenges. IEEE Access (2020).

[16]

Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. 2015. ActivityNet: A large-scale video benchmark for human activity understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15).

[17]

Luis M. Candanedo, Véronique Feldheim, and Dominique Deramaix. 2017. Data-driven prediction models of energy use of appliances in a low-energy house. Energy Build. (2017).

[18]

Elif Cay, Yeliz Mert, Ali Bahcetepe, Bugra Kagan Akyazi, and Arif Selcuk Ogrenci. 2017. Beacons for indoor positioning. In Proceedings of the International Conference on Engineering and Technology (ICET’17). IEEE.

[19]

Cody Chand, Angelo Villanueva, Matt Marty, and Michal Aibin. 2021. Privacy preserving occupancy detection using NB IoT sensors. In Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering (CCECE’21). IEEE.

[20]

Fathia Chekired, Laurent Canale, Sidahmed Tadjer, Amira Louni, Constantinos A. Bouroussis, and Amar Tilmatine. 2021. Low Cost House Automation system based on arduino microcontroller. In Proceedings of the IEEE Industry Applications Society (IAS’21). IEEE.

[21]

Chih-Yen Chen, Chi-Wen Hsieh, Yi-Hsiang Liao, and Tai-Jei Yin. 2018. Implementation of wearable devices and indoor positioning system for a smart hospital environment. In Proceedings of the International Space Science Institute (ISSI’18). IEEE.

[22]

Hao Chen, Seung Hyun Cha, and Tae Wan Kim. 2019. A framework for group activity detection and recognition using smartphone sensors and beacons. Build. Environ. (2019).

[23]

Joy Iong Zong Chen. 2020. Smart security system for suspicious activity detection in volatile areas. J. Info. Technol. (2020).

[24]

Liming Chen, Jesse Hoey, Chris D. Nugent, Diane J. Cook, and Zhiwen Yu. 2012. Sensor-based activity recognition. IEEE Trans. Syst., Man, Cybern., Part C (Appl. Rev.) (2012).

[25]

Xuxu Chen, Yu Zheng, Yubiao Chen, Qiwei Jin, Weiwei Sun, Eric Chang, and Wei-Ying Ma. 2014. Indoor air quality monitoring system for smart buildings. In Proceedings of the ACM Conference on Pervasive and Ubiquitous Computing.

Digital Library

[26]

Zhenghua Chen, Rui Zhao, Qingchang Zhu, Mustafa K Masood, Yeng Chai Soh, and Kezhi Mao. 2017. Building occupancy estimation with environmental sensors via CDBLSTM. IEEE Trans. Industr. Electr. (2017).

[27]

Seyed Ali Cheraghi, Vinod Namboodiri, and Laura Walker. 2017. GuideBeacon: Beacon-based indoor wayfinding for the blind, visually impaired, and disoriented. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom’17). IEEE.

[28]

Claudia Chiţu, Grigore Stamatescu, and Alberto Cerpa. 2019. Building occupancy estimation using supervised learning techniques. In Proceedings of the 23rd International Conference on System Theory, Control and Computing (ICSTCC). IEEE.

[29]

Keum San Chun, Sarnab Bhattacharya, and Edison Thomaz. 2018. Detecting eating episodes by tracking jawbone movements with a non-contact wearable sensor. In Proceedings of the ACM Conference on Interactive, Mobile, Wearable and Ubiquitous Technologies.

Digital Library

[30]

Wei Cui, Bing Li, Le Zhang, and Zhenghua Chen. 2021. Device-free single-user activity recognition using diversified deep ensemble learning. Appl. Soft Comput. (2021).

Digital Library

[31]

Anooshmita Das, Krister Jens, and Mikkel Baun Kjærgaard. 2020. Space utilization and activity recognition using 3D stereo vision camera inside an educational building. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings.

Digital Library

[32]

Alessandra De Paola, Marco Ortolani, Giuseppe Lo Re, Giuseppe Anastasi, and Sajal K. Das. 2014. Intelligent management systems for energy efficiency in buildings: A survey. ACM Comput. Surveys 47, 1 (2014), 1–38.

Digital Library

[33]

Djamel Djenouri, Roufaida Laidi, Youcef Djenouri, and Ilangko Balasingham. 2019. Machine learning for smart building applications: Review and taxonomy. ACM Comput. Surveys 52, 2 (2019), 1–36.

Digital Library

[34]

Bing Dong, Vishnu Prakash, Fan Feng, and Zheng O’Neill. 2019. A review of smart building sensing system for better indoor environment control. Energy Build. 199 (2019).

[35]

Tobore Ekwevugbe, Neil Brown, and Denis Fan. 2012. A design model for building occupancy detection using sensor fusion. In Proceedings of the 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST’12). IEEE.

[36]

Tobore Ekwevugbe, Neil Brown, Vijay Pakka, and Denis Fan. 2013. Real-time building occupancy sensing using neural-network based sensor network. In Proceedings of the 7th IEEE Digital EcoSystems and Technologies Conference (DEST’13). IEEE.

[37]

Mostafa Elhoushi, Jacques Georgy, Aboelmagd Noureldin, and Michael J. Korenberg. 2016. A survey on approaches of motion mode recognition using sensors. IEEE Trans. Intell. Transport. Syst. (2016).

[38]

Seham Abd Elkader, Michael Barlow, and Erandi Lakshika. 2018. Wearable sensors for recognizing individuals undertaking daily activities. In Proceedings of the ACM International Symposium on Wearable Computers.

Digital Library

[39]

Maria Fazio, Alina Buzachis, Antonino Galletta, Antonio Celesti, and Massimo Villari. 2020. A proximity-based indoor navigation system tackling the COVID-19 social distancing measures. In Proceedings of the IEEE Symposium on Computers and Communications (ISCC’20). IEEE.

[40]

Faeghe Fereidoonian, Farshad Firouzi, and Bahar Farahani. 2020. Human activity recognition: From sensors to applications. In Proceedings of the International Conference on Omni-layer Intelligent Systems (COINS’20). IEEE.

[41]

Florian Fiebig, Sebastian Kochanneck, Ingo Mauser, and Hartmut Schmeck. 2017. Detecting occupancy in smart buildings by data fusion from low-cost sensors: Poster description. In Proceedings of the 8th International Conference on Future Energy Systems.

Digital Library

[42]

S. Mitchell Finnigan, Adrian K. Clear, Geremy Farr-Wharton, Karim Ladha, and Rob Comber. 2017. Augmenting audits: Exploring the role of sensor toolkits in sustainable buildings management. In Proceedings of the ACM Conference on Interactive, Mobile, Wearable and Ubiquitous Technologies.

Digital Library

[43]

Patricia Franco, Jose Manuel Martinez, Young-Chon Kim, and Mohamed A. Ahmed. 2021. IoT based approach for load monitoring and activity recognition in smart homes. IEEE Access (2021).

[44]

Pradnya Gaonkar, Jyotsna Bapat, Debabrata Das, and Subrahmanya V. R. K. Rao. 2019. Occupancy estimation in semi-public spaces using sensor fusion and context awareness. In Proceedings of the IEEE Region 10 Symposium (TENSYMP’19). IEEE.

[45]

Rayan Gargees, James M. Keller, Mihail Popescu, and Marjorie Skubic. 2019. Non-invasive classification of sleep stages with a hydraulic bed sensor using deep learning. In Proceedings of the International Conference on Smart Homes and Health.

Digital Library

[46]

Hangli Ge, Zhe Sun, Yasuhira Chiba, and Noboru Koshizuka. 2022. Accurate indoor location awareness based on machine learning of environmental sensing data. Comput. Electric. Eng. (2022).

Digital Library

[47]

Sunil Kumar Ghai, Lakshmi V. Thanayankizil, Deva P. Seetharam, and Dipanjan Chakraborty. 2012. Occupancy detection in commercial buildings using opportunistic context sources. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops. IEEE.

[48]

Luis Gonzalez, Reimar Stier, and Oliver Amft. 2016. Data mining-based localisation of spatial low-resolution sensors in commercial buildings. In Proceedings of the 3rd ACM Conference on Systems for Energy-Efficient Built Environments.

Digital Library

[49]

Tianbo Gu, Zheng Fang, Zhicheng Yang, Pengfei Hu, and Prasant Mohapatra. 2019. MmSense: Multi-person detection and identification via mmWave sensing. In Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems. 45–50.

Digital Library

[50]

Jun Han, Shijia Pan, Manal Kumar Sinha, Hae Young Noh, Pei Zhang, and Patrick Tague. 2018. Smart home occupant identification via sensor fusion across on-object devices. ACM Trans. Sensor Netw. (2018).

Digital Library

[51]

Mohammed Mehedi Hassan, Md Zia Uddin, Amr Mohamed, and Ahmad Almogren. 2018. A robust human activity recognition system using smartphone sensors and deep learning. Future Gen. Comput. Syst. (2018).

Digital Library

[52]

Brodie W. Hobson, Daniel Lowcay, H. Burak Gunay, Araz Ashouri, and Guy R. Newsham. 2019. Opportunistic occupancy-count estimation using sensor fusion: A case study. Build. Environ. (2019).

[53]

Zawar Hussain, Michael Sheng, and Wei Emma Zhang. 2019. Different approaches for human activity recognition: A survey. Retrieved from https://arXiv:1906.05074.

[54]

Mostafa Ibrahim, Srikanth Muralidharan, Zhiwei Deng, Arash Vahdat, and Greg Mori. 2016. A hierarchical deep temporal model for group activity recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

[55]

Mohamed Ibrahim, Viet Nguyen, Siddharth Rupavatharam, Minitha Jawahar, Marco Gruteser, and Richard Howard. 2016. Visible light based activity sensing using ceiling photosensors. In Proceedings of the 3rd Workshop on Visible Light Communication Systems.

Digital Library

[56]

Haruyuki Ichino, Katsuhiko Kaji, Ken Sakurada, Kei Hiroi, and Nobuo Kawaguchi. 2016. HASC-PAC2016: Large scale human pedestrian activity corpus and its baseline recognition. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct.

Digital Library

[57]

IEA. 2021. Buildings—Topics. Retrieved from https://www.iea.org/topics/buildings.

[58]

Ahmad Jalal, Mouazma Batool, and Kibum Kim. 2020. Stochastic recognition of physical activity and healthcare using tri-axial inertial wearable sensors. Appl. Sci. (2020).

[59]

Aftab Jalia and Michael Ramage. (n.d.). The Edge, Amsterdam, 33. https://open.metu.edu.tr/handle/11511/96369.

[60]

Kasthuri Jayarajah, Zaman Lantra, and Archan Misra. 2016. Fusing wifi and video sensing for accurate group detection in indoor spaces. In Proceedings of the 3rd International on Workshop on Physical Analytics.

Digital Library

[61]

Youngmin Ji, Kisu Ok, and Woo Suk Choi. 2018. Occupancy detection technology in the building based on IoT environment sensors. In Proceedings of the 8th International Conference on the Internet of Things.

Digital Library

[62]

Mengda Jia, Ali Komeily, Yueren Wang, and Ravi S. Srinivasan. 2019. Adopting internet of things for the development of smart buildings: A review of enabling technologies and applications. Autom. Construct. (2019).

[63]

Mengda Jia, Ravi S. Srinivasan, Robert Ries, and Gnana Bharathy. 2018. Exploring the validity of occupant behavior model for improving office building energy simulation. In Proceedings of the Winter Simulation Conference (WSC’18). IEEE.

[64]

Chaoyang Jiang, Zhenghua Chen, Lih Chieh Png, Korkut Bekiroglu, Seshadhri Srinivasan, and Rong Su. 2018. Building occupancy detection from carbon-dioxide and motion sensors. In Proceedings of the 15th International Conference on (ICARCV’18). IEEE.

[65]

Aqeel Kazmi, Michael O’Grady, Declan Delaney, Antonio G. Ruzzelli, and Gregory M. P. O’hare. 2014. A review of wireless-sensor-network-enabled building energy management systems. ACM Trans. Sensor Netw. (2014).

Digital Library

[66]

Oussama Kerdjidj, Naeem Ramzan, Khalida Ghanem, Abbes Amira, and Fatima Chouireb. 2020. Fall detection and human activity classification using wearable sensors and compressed sensing. J. Ambient Intell. Human. Comput. (2020).

[67]

Aftab Khan, James Nicholson, Sebastian Mellor, Daniel Jackson, Karim Ladha, Cassim Ladha, Jon Hand, Joseph Clarke, Patrick Olivier, and Thomas Plötz. 2014. Occupancy monitoring using environmental and context sensors and a hierarchical analysis framework. https://nrl.northumbria.ac.uk/id/eprint/35912/.

[68]

Donya Sheikh Khan, Jakub Kolarik, Christian Anker Hviid, and Peter Weitzmann. 2021. Method for long-term mapping of occupancy patterns in open-plan and single office spaces by using passive-infrared (PIR) sensors mounted below desks. Energy Build. (2021).

[69]

Abdelhak Kharbouch, Anass Berouine, Hamza Elkhoukhi, Soukayna Berrabah, Mohamed Bakhouya, Driss El Ouadghiri, and Jaafar Gaber. 2022. Internet-of-things based hardware-in-the-loop framework for model-predictive-control of smart building ventilation. Sensors (2022).

[70]

Mone Kijima, Norihisa Segawa, Masato Yazawa, and Masa-yuki Yamamoto. 2018. Multiple door opening/closing detection and identification system using infrasound sensors. In Proceedings of the 5th Conference on Systems for Built Environments.

Digital Library

[71]

Minseok Kim, Takeshi Tasaki, and Satoshi Yamakawa. 2019. Millimeter-wave radio tomographic imaging technique using multipath components for indoor localization. In Proceedings of the International Symposium on Antennas and Propagation (ISAP’19). IEEE, 1–3.

[72]

Tae Wan Kim, Seunghyun Cha, and Youngchul Kim. 2018. Space choice, rejection and satisfaction in university campus. Indoor Built Environ. (2018).

[73]

Joseph Korpela, Kazuyuki Takase, Takahiro Hirashima, Takuya Maekawa, Julien Eberle, Dipanjan Chakraborty, and Karl Aberer. 2015. An energy-aware method for the joint recognition of activities and gestures using wearable sensors. In Proceedings of the ACM International Symposium on Wearable Computers.

Digital Library

[74]

Heli Koskimäki, Henna Mönttinen, Pekka Siirtola, Hanna-Leena Huttunen, Raija Halonen, and Juha Röning. 2017. Early detection of migraine attacks based on wearable sensors: Experiences of data collection using Empatica E4. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers.

Digital Library

[75]

Anuj Kumar, Abhishek Singh, Ashok Kumar, Manoj Kumar Singh, Pinakeswar Mahanta, and Subhas Mukhopadhyay. 2018. Sensing technologies for monitoring intelligent buildings: A review. IEEE Sensors J. (2018).

[76]

Roufaida Laidi and Djamel Djenouri. 2018. UDEPLOY: User-driven learning for occupancy sensors DEPLOYment in smart buildings. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom’18). IEEE.

[77]

Jared Langevin. 2019. Longitudinal dataset of human-building interactions in U.S. offices. Sci. Data (2019).

[78]

Gierad Laput and Chris Harrison. 2019. Sensing fine-grained hand activity with smartwatches. In Proceedings of the CHI Conference on Human Factors in Computing Systems.

Digital Library

[79]

Billy Pik Lik Lau, Tanmay Chaturvedi, Benny Kai Kiat Ng, Kai Li, Marakkalage Sumudu Hasala, and Chau Yuen. 2016. Spatial and temporal analysis of urban space utilization with renewable wireless sensor network. In Proceedings of the IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT’16). IEEE.

Digital Library

[80]

Haobo Li, Aman Shrestha, Francesco Fioranelli, Julien Le Kernec, and Hadi Heidari. 2018. Hierarchical classification on multimodal sensing for human activity recognition and fall detection. In Proceedings of the IEEE SENSORS. IEEE.

[81]

Haobo Li, Aman Shrestha, Francesco Fioranelli, Julien Le Kernec, Hadi Heidari, Matteo Pepa, Enea Cippitelli, Ennio Gambi, and Susanna Spinsante. 2017. Multisensor data fusion for human activities classification and fall detection. In Proceedings of the IEEE SENSORS. IEEE.

[82]

Haobo Li, Aman Shrestha, Hadi Heidari, Julien Le Kernec, and Francesco Fioranelli. 2019. Activities recognition and fall detection in continuous data streams using radar sensor. In Proceedings of the IEEE MTT-S (IMBioC). IEEE.

[83]

Qimeng Li, Raffaele Gravina, Ye Li, Saeed H. Alsamhi, Fangmin Sun, and Giancarlo Fortino. 2020. Multi-user activity recognition: Challenges and opportunities. Info. Fusion (2020).

[84]

Xinyu Li, Yanyi Zhang, Ivan Marsic, Aleksandra Sarcevic, and Randall S. Burd. 2016. Deep learning for RFID-based activity recognition. In Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM.

Digital Library

[85]

Zhaoxuan Li and Bing Dong. 2017. A new modeling approach for short-term prediction of occupancy in residential buildings. Build. Environment (2017).

[86]

Ming Liang, Bin Yang, Yun Chen, Rui Hu, and Raquel Urtasun. 2019. Multi-task multi-sensor fusion for 3D object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[87]

Qingshan Liang, Lei Yu, Xuping Zhai, Zaihong Wan, and Hong Nie. 2018. Activity recognition based on thermopile imaging array sensor. In Proceedings of the IEEE International Conference on Electro/Information Technology (EIT’18). IEEE.

[88]

Yunji Liang, Xin Wang, Zhiwen Yu, Bin Guo, Xiaolong Zheng, and Sagar Samtani. 2021. Energy-efficient collaborative sensing: Learning the latent correlations of heterogeneous sensors. ACM Trans. Sensor Netw. (2021).

Digital Library

[89]

Wen-Hau Liau, Chao-Lin Wu, and Li-Chen Fu. 2008. Inhabitants tracking system in a cluttered home environment via floor load sensors. IEEE Trans. Autom. Sci. Eng. (2008).

[90]

Guanxiong Liu, Yishuang Geng, and Kaveh Pahlavan. 2015. Effects of calibration RFID tags on performance of inertial navigation in indoor environment. In Proceedings of the International Conference on Computing, Networking and Communications. IEEE.

[91]

Ye Liu, Liqiang Nie, Li Liu, and David S. Rosenblum. 2016. From action to activity: Sensor-based activity recognition. Neurocomputing (2016).

[92]

Zhen Liu, Zhiyu Deng, and Peter Demian. 2018. Integration of building information modelling (BIM) and sensor technology: A review of current developments and future outlooks. In Proceedings of the 2nd International Conference on Computer Science and Application Engineering.

Digital Library

[93]

Zhenguang Liu, Luming Zhang, Qi Liu, Yifang Yin, Li Cheng, and Roger Zimmermann. 2016. Fusion of magnetic and visual sensors for indoor localization: Infrastructure-free and more effective. IEEE Trans. Multimedia (2016).

[94]

Edoardo Longo, Alessandro E. C. Redondi, Massimo Bianchini, Patrizia Bolzan, and Stefano Maffei. 2020. Smart gate: A modular system for occupancy and environmental monitoring of spaces. In Proceedings of the 5th Conference on International Conference on Smart and Sustainable Technologies (SpliTech’20). IEEE.

[95]

Fei Luo, Stefan Poslad, and Eliane Bodanese. 2019. Kitchen activity detection for healthcare using a low-power radar-enabled sensor network. In Proceedings of the IEEE International Conference on Communications (ICC’19). IEEE.

[96]

Na Luo, Zhe Wang, David Blum, Christopher Weyandt, Norman Bourassa, Mary Piette, and Tianzhen Hong. 2022. A three-year dataset supporting research on building energy management and occupancy analytics. Sci. Data (2022).

[97]

Xiaomu Luo, Huoyuan Tan, Qiuju Guan, Tong Liu, Hankz Hankui Zhuo, and Baihua Shen. 2016. Abnormal activity detection using pyroelectric infrared sensors. Sensors 16, 6 (2016).

[98]

Congcong Ma, Wenfeng Li, Raffaele Gravina, and Giancarlo Fortino. 2017. Posture detection based on smart cushion for wheelchair users. Sensors (2017).

[99]

Claudio Martani, David Lee, Prudence Robinson, Rex Britter, and Carlo Ratti. 2012. ENERNET: Studying the dynamic relationship between building occupancy and energy consumption. Energy Build. (2012).

[100]

Sakorn Mekruksavanich, Narit Hnoohom, and Anuchit Jitpattanakul. 2018. Smartwatch-based sitting detection with human activity recognition for office workers syndrome. In Proceedings of the International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON’18). IEEE.

[101]

Ryan Melfi, Ben Rosenblum, Bruce Nordman, and Ken Christensen. 2011. Measuring building occupancy using existing network infrastructure. In Proceedings of the International Green Computing Conference and Workshops. IEEE.

Digital Library

[102]

Aravind K. Mikkilineni, Jin Dong, Teja Kuruganti, and David Fugate. 2019. A novel occupancy detection solution using low-power IR-FPA based wireless occupancy sensor. Energy Build. (2019).

[103]

Shinya Misaki, Sopicha Stirapongsasuti, Tomokazu Matsui, Hirohiko Suwa, and Keiichi Yasumoto. 2020. Activity recognition through intermittent distributed processing by energy harvesting PIR sensors: Demo abstract. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems.

Digital Library

[104]

Nicola Moretti, J. D. Blanco Cadena, A. Mannino, T. Poli, and F. Re Cecconi. 2021. Maintenance service optimization in smart buildings through ultrasonic sensors network. Intell. Build. Int. (2021).

[105]

Mondher Bouazizi, Tomoaki Ohtsuki and others. 2020. Detection of human activity based on hybrid deep learning model using a low-resolution infrared array sensor. IEICE Technical Report; IEICE Tech. Rep. 120, 261 (2020), 99–104.

[106]

K. A. Muthukumar, Mohdher Bouazizi, and Tomaki Ohtsuki. 2021. A novel hybrid deep learning model for activity detection using wide-angle low-resolution infrared array sensor. IEEE Access (2021).

[107]

Chihiro Nakatani, Kohei Sendo, and Norimichi Ukita. 2021. Group activity recognition using joint learning of individual action recognition and people grouping. In Proceedings of the 17th International Conference on Machine Vision Applications (MVA’21). IEEE.

[108]

Nashreen Nesa and Indrajit Banerjee. 2017. IoT-based sensor data fusion for occupancy sensing using dempster–shafer evidence theory for smart buildings. IEEE Internet Things J. (2017).

[109]

Tuan Anh Nguyen and Marco Aiello. 2013. Energy intelligent buildings based on user activity: A survey. Energy Build. (2013).

[110]

Maciej A. Noras. 2020. Activity Detection and Recognition With Passive electric field sensors. In Proceedings of the IEEE Industry Applications Society Annual Meeting. IEEE.

[111]

Mark Mbock Ogonji, George Okeyo, and Joseph Muliaro Wafula. 2020. A survey on privacy and security of internet of things. Comput. Sci. Rev. (2020).

[112]

Babatunji Omoniwa, Riaz Hussain, Muhammad Javed, Safdar Bouk, and Shahzad A. Malik. 2018. Fog/edge computing-based IoT (FECIoT): Architecture, applications, and research issues. IEEE Internet Things J. (2018).

[113]

Mohammad Ostadijafari, Anamika Dubey, and Nanpeng Yu. 2020. Linearized price-responsive HVAC controller for optimal scheduling of smart building loads. IEEE Trans. Smart Grid (2020).

[114]

Jianli Pan, Raj Jain, and Subharthi Paul. 2014. A survey of energy efficiency in buildings and microgrids using networking technologies. IEEE Commun. Surveys Tutor. 16, 3 (2014).

[115]

C. Papatsimpa and J. P. M. G. Linnartz. 2018. Propagating sensor uncertainty to better infer office occupancy in smart building control. Energy Build. (2018).

[116]

Alec Parise, Miguel A. Manso-Callejo, Hung Cao, Marco Mendonca, Harpreet Kohli, and Monica Wachowicz. 2019. Indoor occupancy prediction using an IoT platform. In Proceedings of the 6th International Conference (IOTSMS’19). IEEE.

[117]

Charith Perera, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos. 2013. Context aware computing for the internet of things: A survey. IEEE Commun. Surveys Tutor. (2013).

[118]

Manisa Pipattanasomporn, Gopal Chitalia, Jitkomut Songsiri, Chaodit Aswakul, Wanchalerm Pora, Surapong Suwankawin, Kulyos Audomvongseree, and Naebboon Hoonchareon. 2020. CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets. Sci. Data (2020).

[119]

Kerly Prastika et al. 2020. Application of individual activity recognition in the room using CNN Alexnet method. In Proceedings of the IOP Conference Series: Materials Science and Engineering. IOP Publishing.

[120]

Azkario Rizky Pratama, Alexander Lazovik, and Marco Aiello. 2019. Office multi-occupancy detection using BLE beacons and power meters. In Proceedings of the 10th Annual Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON’19). IEEE.

[121]

Loubna Qabbal, Zohir Younsi, and Hassane Naji. 2021. An indoor air quality and thermal comfort appraisal in a retrofitted university building via low-cost smart sensor. Indoor Built Environ. (2021).

[122]

Loubna Qabbal, Zohir Younsi, and Hassane Naji. 2022. An indoor air quality and thermal comfort appraisal in a retrofitted university building via low-cost smart sensor. Indoor Built Environ. (2022).

[123]

Basheer Qolomany, Ala Al-Fuqaha, Ajay Gupta, Driss Benhaddou, Safaa Alwajidi, Junaid Qadir, and Alvis C. Fong. 2019. Leveraging machine learning and big data for smart buildings: A comprehensive survey. IEEE Access 7 (2019).

[124]

Ioana Radoi, Gabriel Gutu, Traian Rebedea, Cristian Neagu, and Marius Popa. 2017. Indoor positioning inside an office building using BLE. In Proceedings of the 21st International Conference on Control Systems and Computer Science (CSCS’17). IEEE.

[125]

Haolia Rahman, Abdul Azis Abdillah, Asep Apriana, Devi Handaya, and Idrus Assagaf. 2021. Indoor \(\text{CO}_2\) level-based occupancy estimation at low-scale occupant using statistical learning method. In Proceedings of the 4th International Conference of Computer and Informatics Engineering (IC2IE’21). IEEE.

[126]

Princy Randhawa, Vijay Shanthagiri, Ajay Kumar, and Vinod Yadav. 2020. Human activity detection using machine learning methods from wearable sensors. Sensor Rev. (2020).

[127]

Krati Rastogi and Divya Lohani. 2019. IoT-based occupancy estimation models for indoor non-residential environments. In Proceedings of the IEEE 16th India Council International Conference (INDICON). IEEE.

[128]

Attila Reiss and Didier Stricker. 2012. Introducing a new benchmarked dataset for activity monitoring. In Proceedings of the 16th International Symposium on Wearable Computers. IEEE.

Digital Library

[129]

Ricardo Ribani and Mauricio Marengoni. 2019. A survey of transfer learning for convolutional neural networks. In Proceedings of the 32nd SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T’19). IEEE.

[130]

Daniel Roggen, Alberto Calatroni, Mirco Rossi, Thomas Holleczek, Kilian Förster, Gerhard Tröster, Paul Lukowicz, David Bannach, Gerald Pirkl, Alois Ferscha, et al. 2010. Collecting complex activity datasets in highly rich networked sensor environments. In Proceedings of the 7th Conference of the Institute for National Security Studies (INSS’10). IEEE.

[131]

Homagni Saha, Anthony R. Florita, Gregor P. Henze, and Soumik Sarkar. 2019. Occupancy sensing in buildings: A review of data analytics approaches. Energy Build. 188 (2019).

[132]

Ehsan Samani, Parviz Khaledian, Armin Aligholian, Evangelos Papalexakis, Shawn Cun, Masoud H. Nazari, and Hamed Mohsenian-Rad. 2020. Anomaly detection in IoT-based PIR occupancy sensors to improve building energy efficiency. In Proceedings of the IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT’20). IEEE.

[133]

Gheorghe Sebestyen, Ionut Stoica, and Anca Hangan. 2016. Human activity recognition and monitoring for elderly people. In Proceedings of the IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP’16). IEEE.

[134]

David Sembroiz, Davide Careglio, Sergio Ricciardi, and Ugo Fiore. 2019. Planning and operational energy optimization solutions for smart buildings. Info. Sci. (2019).

[135]

Sindhu S. Shetty, Hoang Duc Chinh, Manish Gupta, and Sanjeev Kumar Panda. 2017. User presence estimation in multi-occupancy rooms using plug-load meters and PIR sensors. In Proceedings of the Global Communications Conference. IEEE.

Digital Library

[136]

Qiongfeng Shi, Zixuan Zhang, Tianyiyi He, Zhongda Sun, Bingjie Wang, Yuqin Feng, Xuechuan Shan, Budiman Salam, and Chengkuo Lee. 2020. Deep learning enabled smart mats as a scalable floor monitoring system. Nature Commun. (2020).

[137]

Huang-Chia Shih. 2014. A robust occupancy detection and tracking algorithm for the automatic monitoring and commissioning of a building. Energy Build. (2014).

[138]

Muhammad Shoaib, Hans Scholten, Paul J. M. Havinga, and Ozlem Durmaz Incel. 2016. A hierarchical lazy smoking detection algorithm using smartwatch sensors. In Proceedings of the 18th IEEE International Conference on E-health Networking, Application & Services (Healthcom’16). IEEE.

[139]

Pekka Siirtola. 2019. Continuous stress detection using the sensors of commercial smartwatch. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers.

Digital Library

[140]

Akash Deep Singh, Sandeep Singh Sandha, Luis Garcia, and Mani Srivastava. 2019. Radhar: Human activity recognition from point clouds generated through a millimeter-wave radar. In Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems.

Digital Library

[141]

Beril Sirmacek and Maria Riveiro. 2020. Occupancy prediction using low-cost and low-resolution heat sensors for smart offices. Sensors (2020).

[142]

Abdul Syafiq Abdull Sukor, Ammar Zakaria, Norasmadi Abdul Rahim, Latifah Munirah Kamarudin, Rossi Setchi, and Hiromitsu Nishizaki. 2019. A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes. J. Intell. Fuzzy Syst. (2019).

[143]

Shengjing Sun, Xiaochen Zheng, Javier Villalba-Díez, and Joaquín Ordieres-Meré. 2019. Indoor air-quality data-monitoring system: Long-term monitoring benefits. Sensors (2019).

[144]

Chiara Tagliaro, Yaoyi Zhou, and Ying Hua. 2020. A change in granularity: Measure space utilization through smart technologies. Facilities (2020).

[145]

Sheng Tan, Linghan Zhang, Zi Wang, and Jie Yang. 2019. MultiTrack: Multi-user tracking and activity recognition using commodity WiFi. In Proceedings of the CHI Conference on Human Factors in Computing Systems.

Digital Library

[146]

Jinhui Tang, Xiangbo Shu, Rui Yan, and Liyan Zhang. 2019. Coherence constrained graph LSTM for group activity recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2019).

[147]

Yansong Tang, Jiwen Lu, Zian Wang, Ming Yang, and Jie Zhou. 2019. Learning semantics-preserving attention and contextual interaction for group activity recognition. IEEE Trans. Image Process. (2019).

Digital Library

[148]

Thiago Teixeira, Gershon Dublon, and Andreas Savvides. 2010. A survey of human-sensing: Methods for detecting presence, count, location, track, and identity. Comput. Surveys (2010).

[149]

Zeynep Duygu Tekler, Eikichi Ono, Yuzhen Peng, Sicheng Zhan, Bertrand Lasternas, and Adrian Chong. 2022. ROBOD, room-level occupancy and building operation dataset. In Building Simulation. Springer.

[150]

Lakshmi V. Thanayankizil, Sunil Kumar Ghai, Dipanjan Chakraborty, and Deva P. Seetharam. 2012. Softgreen: Towards energy management of green office buildings with soft sensors. In Proceedings of the 4th Conference on (COMSNETS’12). IEEE.

[151]

Edison Thomaz, Abdelkareem Bedri, Temiloluwa Prioleau, Irfan Essa, and Gregory D. Abowd. 2017. Exploring symmetric and asymmetric bimanual eating detection with inertial sensors on the wrist. In Proceedings of the 1st Workshop on Digital Biomarkers.

Digital Library

[152]

Jorge Tuesta, Demetrio Albornoz, Guillermo Kemper, and Carlos A. Almenara. 2019. A sociometric sensor based on proximity, movement and verbal interaction detection. In Proceedings of the International Conference on Information Systems and Computer Science (INCISCOS’19). IEEE.

[153]

Ash Tyndall, Rachel Cardell-Oliver, and Adrian Keating. 2016. Occupancy estimation using a low-pixel count thermal imager. IEEE Sensors J. (2016).

[154]

Md Zia Uddin and Mohammad Mehedi Hassan. 2018. Activity recognition for cognitive assistance using body sensors data and deep convolutional neural network. IEEE Sensors J. (2018).

[155]

Amari Vaughn, Paul Biocco, Yang Liu, and Mohd Anwar. 2018. Activity detection and analysis using smartphone sensors. In Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI’18). IEEE.

Digital Library

[156]

Anurag Verma, Surya Prakash, Vishal Srivastava, Anuj Kumar, and Subhas Chandra Mukhopadhyay. 2019. Sensing, controlling, and IoT infrastructure in smart building: A review. IEEE Sensors J. (2019).

[157]

Federico Viani, Alessandro Polo, Fabrizio Robol, Giacomo Oliveri, Paolo Rocca, and Andrea Massa. 2014. Crowd detection and occupancy estimation through indirect environmental measurements. In Proceedings of the 8th European Conference on Antennas and Propagation (EuCAP’14).

[158]

Giulia Violatto, Ashish Pandharipande, Shuai Li, and Luca Schenato. 2019. Anomalous occupancy sensor behavior detection in connected indoor lighting systems. In Proceedings of the IEEE 5th World Forum on Internet of Things (WF-IoT’19). IEEE.

[159]

Stefan Wagner, Esben Hunnerup, and Jorge Miranda. 2019. Demonstration of a micro-services based multi-purpose sensor platform for supporting ambient assisted living systems. Sens. Technol. Pervas. Healthcare: Eval. Design Senior Cit. Cont. Care (2019).

[160]

Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, and Lisha Hu. 2019. Deep learning for sensor-based activity recognition: A survey. Pattern Recogn. Lett. 119 (2019), 3–11.

Digital Library

[161]

Wei Wang, Jiayu Chen, and Tianzhen Hong. 2018. Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings. Autom. Construc. (2018).

[162]

Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni. 2020. Generalizing from a few examples: A survey on few-shot learning. ACM Comput. Surveys (2020).

[163]

Zhe Wang, Tianzhen Hong, Mary Ann Piette, and Marco Pritoni. 2019. Inferring occupant counts from Wi-Fi data in buildings through machine learning. Build. Environ. (2019).

[164]

Jamie A. Ward, Gerald Pirkl, Peter Hevesi, and Paul Lukowicz. 2016. Towards recognising collaborative activities using multiple on-body sensors. In Proceedings of the ACM Joint Conference on Pervasive and Ubiquitous Computing.

Digital Library

[165]

Martin Wirz, Daniel Roggen, and Gerhard Troster. 2009. Decentralized detection of group formations from wearable acceleration sensors. In Proceedings of the International Conference on Computational Science and Engineering. IEEE.

Digital Library

[166]

Fang-Jing Wu, Yu-Chee Tseng, and Wen-Chih Peng. 2016. Activity sense organs: Energy-efficient activity sensing with adaptive duty cycle control. In Proceedings of ACM Joint Conference on Pervasive and Ubiquitous Computing.

Digital Library

[167]

Xugang Xi, Minyan Tang, Seyed M. Miran, and Zhizeng Luo. 2017. Evaluation of feature extraction and recognition for activity monitoring and fall detection based on wearable sEMG sensors. Sensors (2017).

[168]

Ruchao Xiahou, Jianjun Yi, Liang He, Wang He, and Tianhua Huang. 2019. Indoor air monitoring system based on Internet of things and its prediction model. In Proceedings of the International Conference on Industrial Control Network and System Engineering Research.

Digital Library

[169]

Xiaoke Yang, Lingyu Yang, and Jing Zhang. 2017. A WiFi-enabled indoor air quality monitoring and control system: The design and control experiments. In Proceedings of the 13th IEEE International Conference on Control and Automation (ICCA’17). IEEE.

[170]

Zheng Yang and Burcin Becerik-Gerber. 2015. Cross-space building occupancy modeling by contextual information based learning. In Proceedings of the 2nd ACM Conference on Embedded Systems for Energy-Efficient Built Environments.

Digital Library

[171]

Zheng Yang, Nan Li, Burcin Becerik-Gerber, and Michael Orosz. 2012. A multi-sensor based occupancy estimation model for supporting demand-driven HVAC operations. In Proceedings of the Symposium on Simulation for Architecture and Urban Design. Citeseer.

[172]

Ehsan Yavari, Xiaomeng Gao, and Olga Boric-Lubecke. 2018. Subject count estimation by using doppler radar occupancy sensor. In Proceedings of the 40th Annual Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’18). IEEE.

[173]

Cunyi Yin, Jing Chen, Xiren Miao, Hao Jiang, and Deying Chen. 2021. Device-free human activity recognition with low-resolution infrared array sensor using long short-term memory neural network. Sensors (2021).

[174]

Xizhe Yin, Weiming Shen, Jagath Samarabandu, and Xianbin Wang. 2015. Human activity detection based on multiple smart phone sensors and machine learning algorithms. In Proceedings of the 19th Conference on (CSCWD’15). IEEE.

[175]

Faheem Zafari, Athanasios Gkelias, and Kin K. Leung. 2019. A survey of indoor localization systems and technologies. IEEE Commun. Surveys Tutor. (2019).

[176]

Wei Zhang, Weizheng Hu, and Yonggang Wen. 2018. Thermal comfort modeling for smart buildings: A fine-grained deep learning approach. IEEE Internet Things J. (2018).

[177]

Yang Zhao, Peter Tu, and Ming-Ching Chang. 2019. Occupancy sensing and activity recognition with cameras and wireless sensors. In Proceedings of the 2nd Workshop on Data Acquisition to Analysis.

Digital Library

[178]

Stylianos Zikos, Apostolos Tsolakis, Dimitrios Meskos, Athanasios Tryferidis, and Dimitrios Tzovaras. 2016. Conditional random fields-based approach for real-time building occupancy estimation with multi-sensory networks. Autom. Construct. (2016).

[179]

Lars Zimmermann, Robert Weigel, and Georg Fischer. 2017. Fusion of nonintrusive environmental sensors for occupancy detection in smart homes. IEEE Internet Things J. (2017).

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 13s

December 2023

1367 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3606252

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Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 13 July 2023

Online AM: 16 May 2023

Accepted: 02 April 2023

Revised: 02 March 2023

Received: 12 September 2022

Published in CSUR Volume 55, Issue 13s

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