An Approach to Share Self-Taught Knowledge between Home IoT Devices at the Edge

Jang, Ingook; Lee, Donghun; Choi, Jinchul; Son, Young-Sung

doi:10.3390/s19040833

Cited by 14 publications

(9 citation statements)

References 43 publications

(47 reference statements)

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“…A study intended to make proximity-based payments resilient to replay attacks proposed using environment data that were collected by mobile devices [ 35 ]. Another work used total response time metric in order to evaluate usability in financial transaction scenarios [ 36 ], and a study regarding real-time inference in edge devices evaluated response time to consider user perspective advantages of the proposed approach [ 37 ]. An exploratory study regarding user recognition based on Alexa Skills usage is presented in [ 38 ].…”

Section: Related Workmentioning

confidence: 99%

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Hayashi

Ruggiero

2020

Sensors

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Smart speakers, such as Alexa and Google Home, support daily activities in smart home environments. Even though voice commands enable friction-less interactions, existing financial transaction authorization mechanisms hinder usability. A non-invasive authorization by leveraging presence and light sensors’ data is proposed in order to replace invasive procedure through smartphone notification. The Coloured Petri Net model was created for synthetic data generation, and one month data were collected in test bed with real users. Random Forest machine learning models were used for smart home behavior information retrieval. The LSTM prediction model was evaluated while using test bed data, and an open dataset from CASAS. The proposed authorization mechanism is based on Physical Unclonable Function usage as a random number generator seed in a Challenge Response protocol. The simulations indicate that the proposed scheme with specialized autonomous device could halve the total response time for low value financial transactions triggered by voice, from 7.3 to 3.5 s in a non-invasive manner, maintaining authorization security.

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Section: Related Workmentioning

confidence: 99%

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Hayashi

Ruggiero

2020

Sensors

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“…The system modularized a video analytics process that improves its efficiency, demonstrating a lower response delay than a traditional cloud computing model. A knowledge-sharing framework based on edge computing was proposed in [28], which enables an IoT device to share its knowledge with other devices at the edge in the vicinity. The authors found that the proposed framework reduces the response time for intelligent IoT devices and the power consumption for computation.…”

Section: Edge Computing-based Solutionsmentioning

confidence: 99%

Edge Computing-Based Self-Organized Device Network for Awareness Activities of Daily Living in the Home

2020

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Activities of daily living (ADL) are important indicators for awareness of brain health in the elderly, and hospitals use ADL as a standard test for diagnosing chronic brain diseases such as dementia. However, since it is difficult to judge real-life ADL in hospitals, doctors typically predict ADL ability through interviews with patients or accompanying caregivers. Recently, many studies have attempted to diagnose accurate brain health by collecting and analyzing the real-life ADL of patients in their living environments. However, most of these were conducted by constructing and implementing expensive smart homes with the concept of centralized computing, and ADL data were collected from simple data about patients’ home appliance usage and the surrounding environment. Despite the high cost of building a smart home, the collected ADL data are inadequate for predicting accurate brain health. In this study, we developed and used three types of portable devices (wearable, tag, and stationary) that can be easily installed and operated in typical existing houses. We propose a self-organized device network structure based on edge computing that can perform user perception, location perception, and behavioral perception simultaneously. This approach enables us to collect user activity data, analyze ADL in real-time to determine if the user’s behavior was successful or abnormal, and record the physical ability of the user to move between fixed spaces. The characteristics of this proposed system enable us to distinguish patients from other family members and provide real-time notifications after a forgetful or mistaken action. We implemented devices that constitute the edge network of the smart home scenario and evaluated the performance of this system to verify its usefulness.

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“…Due to the variety of EC devices, data types, and algorithms [77], many AI-based or smart offloading and transmission strategies are being proposed, such as employing machine and deep learning methods [75,78], or mimicking human brain networks [79]. Similarly, knowledge-sharing strategies to take advantage of self-taught knowledge between EC devices, such as pertaining to home IoT, have been considered [80]. Undoubtedly, EC-specialized or related optimization problems formulation and solution will be valuable [72,81,82,83], as discussed in the case of optimization consideration for platooning for automatic driving [84], evolutionary game theoretical proposals for mobile devices and security [85,86], better estimation of interference in EC devices [87], cost-effective placement of EC servers [88], computational power allocation for blockchains [89], and incorporation of computer vision [90].…”

Section: Edge Computingmentioning

confidence: 99%

Nanosystems, Edge Computing, and the Next Generation Computing Systems

Passian

Imam

2019

Sensors

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It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing.

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An Approach to Share Self-Taught Knowledge between Home IoT Devices at the Edge

Cited by 14 publications

References 43 publications

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Edge Computing-Based Self-Organized Device Network for Awareness Activities of Daily Living in the Home

Nanosystems, Edge Computing, and the Next Generation Computing Systems

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