<i>DeepContext:</i> Mobile Context Modeling and Prediction Via HMMs and Deep Learning

Sadhu, Vidyasagar; Zonouz, Saman; Pompili, Dario

doi:10.1109/tmc.2022.3200947

Cited by 1 publication

(1 citation statement)

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“…Authors introduced and tested personalized and privacy-preserving collaborative-filtering models that used sequential history to predict current and future mobile context, using a combination of HMMs and DL [ 99 ]. These models could be used to verify or improve the context data obtained from sensors in real time.…”

Section: Data Analysis Approachmentioning

confidence: 99%

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Khan,

Zedadra,

Guerrieri

et al. 2024

Sensors

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In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

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Section: Data Analysis Approachmentioning

confidence: 99%