The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
WSN- and IOT-Based Smart Homes and Their Extension to Smart Buildings
Open accessAbstract
Our research approach is to design and develop reliable, efficient, flexible, economical, real-time and realistic wellness sensor networks for smart home systems. The heterogeneous sensor and actuator nodes based on wireless networking technologies are deployed into the home environment. These nodes generate real-time data related to the object usage and movement inside the home, to forecast the wellness of an individual. Here, wellness stands for how efficiently someone stays fit in the home environment and performs his or her daily routine in order to live a long and healthy life. We initiate the research with the development of the smart home approach and implement it in different home conditions (different houses) to monitor the activity of an inhabitant for wellness detection. Additionally, our research extends the smart home system to smart buildings and models the design issues related to the smart building environment; these design issues are linked with system performance and reliability. This research paper also discusses and illustrates the possible mitigation to handle the ISM band interference and attenuation losses without compromising optimum system performance.
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The Internet of Things: A survey
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Towards the Implementation of IoT for Environmental Condition Monitoring in Homes
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Self-citationSmartSantander: IoT experimentation over a smart city testbed
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A capability-based security approach to manage access control in the Internet of Things
Gusmeroli, S., Piccione, S., & Rotondi, D. (2013) · Mathematical and Computer Modelling · doi:10.1016/j.mcm.2013.02.006 · Cited by 361
Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly
Suryadevara, N. K., & Mukhopadhyay, S. C. (2012) · IEEE Sensors Journal · doi:10.1109/jsen.2011.2182341 · Cited by 350
Self-citationSurvey on Fall Detection and Fall Prevention Using Wearable and External Sensors
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Forecasting the behavior of an elderly using wireless sensors data in a smart home
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Self-citation
Cited by
Minoli, D., Sohraby, K., & Occhiogrosso, B. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2647881 · Cited by 874
Federated-Learning-Based Anomaly Detection for IoT Security Attacks
Mothukuri, V., Khare, P., Parizi, R. M., Pouriyeh, S., Dehghantanha, A., & Srivastava, G. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3077803 · Cited by 722
Industrial Internet of Things and its Applications in Industry 4.0: State of The Art
Malik, P. K., Sharma, R., Singh, R., Gehlot, A., Satapathy, S. C., Alnumay, W. S., Pelusi, D., Ghosh, U., & Nayak, J. (2021) · Computer Communications · doi:10.1016/j.comcom.2020.11.016 · Cited by 523
Computational methodologies for optimal sensor placement in structural health monitoring: A review
Tan, Y., & Zhang, L. (2020) · Structural Health Monitoring · doi:10.1177/1475921719877579 · Cited by 186
GDPR Compliance Verification in Internet of Things
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Metadata
- Authors
- Ghayvat, H., Mukhopadhyay, S., Gui, X., & Suryadevara, N.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2015-05-04
- Volume
- 15
- Issue
- 5
- Pages
- 10350-10379
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W1480811131
- Cited by
- 370
- References
- 31
- FWCI
- 16.52
- Type
- journal article
- Language
- en
- Open access
- gold
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