Jarvis: Moving Towards a Smarter Internet of Things

Mudgerikar, Anand; Bertino, Elisa

doi:10.1109/icdcs47774.2020.00020

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…The authors of [4] use Q-Learning to build a task-recommendation service for daily activities in Smart Homes. In [3], the authors use Deep Reinforcement Learning to optimize temperature, used energy, and cost. The main difference between the aforementioned works and Q-SMASH is that the human values are respected via the value-reasoning process of Q-SMASH.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things

Rahimi¹,

Trentin²,

Ramparany³

et al. 2021

Preprint

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Section: Related Work and Discussionmentioning

confidence: 99%

Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things

Rahimi¹,

Trentin²,

Ramparany³

et al. 2021

Preprint

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“…Jarvis is a Reinforcement Learning technique for predicting the optimal and safe IoT systems decisions [66]. Jarvis models the simulated IoT environment based on the states and actions.…”

Section: Hybrid Analysismentioning

confidence: 99%

“…ForeSee correctness properties specified as Linear Temporal Logic (LTL) formulas [44]. While Jarvis represents safe states and transitions in defined formulas [66]. On the other hand, FriendlyFire uses calculus in representing the properties [72].…”

Section: Formal and Mathematical Representationsmentioning

confidence: 99%

“…On the other hand, Jarvis relies on safe states to be able to run Reinforcement Learning (RL) to compare the the normal behavior vs the safe behavior [66]. IoTPraetor uses the Device Usage Rules in order to compare the device behavior with these predefined rules [58].…”

Section: Other Representationsmentioning

confidence: 99%

“…Some Literature tools consider applying different Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the analysis of IoT systems. For example, Jarvis [66] uses Reinforcement Learning, Deep Q learning network, along with deep neural network to train the agent in order to improve the detection and achieve the best quality of the results. IoTMon [42], iRuler [48], and IoT-Praetor [58], uses Natural Language Processing (NLP) in their IoT apps analysis.…”

Section: Optimization Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Analysis Methods for Security and Safety verification in IoT Systems

Abuserrieh¹,

Alalfi²

2022

Preprint

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Internet of Things (IoT) has been rapidly growing in the past few years in all life disciplines. IoT provides automation and smart control to its users in different domains such as home automation, healthcare systems, automotive, and many more. Given the tremendous number of connected IoT devices, this growth leads to enormous automatic interactions among sizeable IoT apps in their environment, making IoT apps more intelligent and more enjoyable to their users. But some unforeseen interactions of IoT apps and any potential malicious behaviour can seriously cause insecure and unsafe consequences to its users, primarily non-experts, who lack the required knowledge regarding the potential impact of their IoT automation processes. In this paper, we study the problem of security and safety verification of IoT systems. We survey techniques that utilize program analysis to verify IoT applications' security and safety properties. The study proposes a set of categorization and classification attributes to enhance our understanding of the research landscape in this domain. Moreover, we discuss the main challenges considered in the surveyed work and potential solutions that could be adopted to ensure the security and safety of IoT systems.

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A Survey on Verification of Security and Safety in IoT Systems

Abuserrieh,

Alalfi

2024

IEEE Access

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Jarvis: Moving Towards a Smarter Internet of Things

Cited by 9 publications

References 17 publications

Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things

Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things

A Survey of Analysis Methods for Security and Safety verification in IoT Systems

A Survey on Verification of Security and Safety in IoT Systems

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