FaiR-IoT

Elmalaki, Salma

doi:10.1145/3450268.3453525

Cited by 10 publications

(11 citation statements)

References 37 publications

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“…Recent work in the literature targets human-in-the-loop smart heating, ventilation, and air conditioning system (HVAC) while trying to assist human satisfaction [40]. RL has been proposed to adapt the HVAC set-point based on human activity [28]. A human-in-the-loop HVAC system should take the human state and preferences into the computation loop while calculating the HVAC set-point.…”

Section: Application 1: Human-in-the-loop Smart Home-a Thermal Systemmentioning

confidence: 99%

“…Simulated Human Model: We model the humans as a heat source with heat flow that depends on the average exhale breath temperature (𝐸𝐵𝑇 ) and the respiratory minute volume (𝑅𝑀𝑉 ) [28]. The 𝑅𝑀𝑉 is the product of the breathing frequency (𝑓 ) and the volume of gas exchanged during the breathing cycle, which is highly dependent on human activity.…”

Section: System Design and Implementationmentioning

confidence: 99%

“…The fundamental essence of designing HITL applications is learning the best adaptation to the environment, which is subjective to the human interaction and response to this adaptation which vary from one human to another [28]. Reinforcement Learning (RL) has proven to be adequate for monitoring human intentions and responses to provide such personalized adaptations [18,29,37].…”

Section: Introductionmentioning

confidence: 99%

“…This increasing adoption of RL-based models in various HITL applications has paved the way to reformulate the trained policies with constraints to address fairness [28], risk-sensitivity [33], safety under exploration [38], and human variability [20]. Adapting to the human often leads to systems where increased sophistication comes at the expense of more privacy weaknesses.…”

Section: Introductionmentioning

confidence: 99%

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adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential Decision Making Human-in-the-Loop Systems

Taherisadr

Stavroulakis

Elmalaki

2023

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation

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Reinforcement learning (RL) presents numerous benefits compared to rule-based approaches in various applications. Privacy concerns have grown with the widespread use of RL trained with privacy-sensitive data in IoT devices, especially for human-in-the-loop systems.On the one hand, RL methods enhance the user experience by trying to adapt to the highly dynamic nature of humans. On the other hand, trained policies can leak the user's private information. Recent attention has been drawn to designing privacy-aware RL algorithms while maintaining an acceptable system utility. A central challenge in designing privacy-aware RL, especially for human-in-the-loop systems, is that humans have intrinsic variability, and their preferences and behavior evolve. The effect of one privacy leak mitigation can differ for the same human or across different humans over time. Hence, we can not design one fixed model for privacy-aware RL that fits all. To that end, we propose adaPARL, an adaptive approach for privacy-aware RL, especially for human-in-the-loop IoT systems. adaPARL provides a personalized privacy-utility trade-off depending on human behavior and preference. We validate the proposed adaPARL on two IoT applications, namely (i) Human-in-the-Loop Smart Home and (ii) Human-in-the-Loop Virtual Reality (VR) Smart Classroom. Results obtained on these two applications validate the generality of adaPARL and its ability to provide a personalized privacy-utility trade-off.On average, for the first application, adaPARL improves the utility by 57% over the baseline and by 43% over randomization while reducing the privacy leak by 23% on average. For the second application, adaPARL decreases the privacy leak to 44% before the utility drops by 15%.

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Section: Application 1: Human-in-the-loop Smart Home-a Thermal Systemmentioning

confidence: 99%

Section: System Design and Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential Decision Making Human-in-the-Loop Systems

Taherisadr

Stavroulakis

Elmalaki

2023

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation

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“…In addition, recent research in the human-in-the-loop IoT systems has shown that humans have intrinsic inter-and intra-variation. Hence, we can not have one model-fits-all approach in designing real-time adaptations to improve the human learning [15], [14], [76], [5].…”

Section: Introductionmentioning

confidence: 99%

ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

Taherisadr¹,

Faruque²,

Elmalaki³

2023

Preprint

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Thanks to the rapid growth in wearable technologies and recent advancement in machine learning and signal processing, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing many of these IoT systems arises from the requirement to infer the human mental state, such as intention, stress, cognition load, or learning ability. While different human contexts can be inferred from the fusion of different sensor modalities that can correlate to a particular mental state, the human brain provides a richer sensor modality that gives us more insights into the required human context. This paper proposes ERUDITE, a human-in-the-loop IoT system for the learning environment that exploits recent wearable neurotechnology to decode brain signals.Through insights from concept learning theory, ERUDITE can infer the human state of learning and understand when human learning increases or declines. By quantifying human learning as an input sensory signal, ERUDITE can provide adequate personalized feedback to humans in a learning environment to enhance their learning experience. ERUDITE is evaluated across 15 participants and showed that by using the brain signals as a sensor modality to infer the human learning state and providing personalized adaptation to the learning environment, the participants' learning performance increased on average by 26%. Furthermore, we showed that ERUDITE can be deployed on an edge-based prototype to evaluate its practicality and scalability.

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