Smart Home: Cognitive Interactive People-Centric Internet of Things

Feng, Shuo; Setoodeh, Peyman

doi:10.1109/mcom.2017.1600682cm

Cited by 147 publications

(66 citation statements)

References 13 publications

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“…Line 4 creates a mutually dependent schedule for the pair of devices with temporal dependency. Lines 5 and 6 consider all of these mutually dependent 9 We consider only pairs of devices, to simply demonstrate the relative ordering. However, a different subset may contain as large as the total number of devices.…”

Section: Verifiable Ordering Protocolmentioning

confidence: 99%

Verifiable Round-Robin Scheme for Smart Homes

Panwar

Sharma

Wang

et al. 2019

Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy

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Advances in sensing, networking, and actuation technologies have resulted in the IoT wave that is expected to revolutionize all aspects of modern society. This paper focuses on the new challenges of privacy that arise in IoT in the context of smart homes. Specifically, the paper focuses on preventing the user's privacy via inferences through channel and in-home device activities. We propose a method for securely scheduling the devices while decoupling the device and channels activities. The proposed solution avoids any attacks that may reveal the coordinated schedule of the devices, and hence, also, assures that inferences that may compromise individual's privacy are not leaked due to device and channel level activities. Our experiments also validate the proposed approach, and consequently, an adversary cannot infer device and channel activities by just observing the network traffic. CCS CONCEPTS• Security and privacy → Security protocols; Mobile and wireless security; Domain-specific security and privacy architectures; Social aspects of security and privacy. KEYWORDSInternet of Things; smart homes; user privacy; channel and device activity; inference attacks.

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Section: Verifiable Ordering Protocolmentioning

confidence: 99%

Verifiable Round-Robin Scheme for Smart Homes

Panwar

Sharma

Wang

et al. 2019

Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy

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“…Also, CDS was applied as the brain of complex network [11]. A prospective block diagram of a CDS for smart homes was presented in [12]. In [13], the application of CDS for cybersecurity applications was discussed.…”

Section: Introductionmentioning

confidence: 99%

“…Here, LGE means that the outputs of the environment linearly depend on the inputs and they have a Gaussian distribution. Therefore, the typical CDS in [2][3][4][5][6][7][8][9][10][11][12][13] uses the Kalman filter. Usually, the Kalman filter, can be applied to linear environments only.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, the Kalman filter, can be applied to linear environments only. The simplified version of Shannon theory is used in [2][3][4][5][6][7][8][9][10][11][12][13] for a Gaussian environment for calculating the entropic state at time n. A typical CDS can use the entropic state at time n as the internal reward. However, Shannon's entropy equation cannot be simplified for non-Gaussian environment.…”

Section: Introductionmentioning

confidence: 99%

“…The computational complexity of CDS algorithms in [2][3][4][5][6][7][8][9][10][11][12][13] is high in a complex environment. Also, in an NGNLE, the complexity of CDS algorithms in [2][3][4][5][6][7][8][9][10][11][12][13] is higher than those for a LGE. In this paper, we develop a CDS for NGNLE with simple, straightforward, and faster PAC algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory

2020

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The cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in NGNLE to understand the current state condition. Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making. The simple low-complexity algorithmic design of the proposed CDM system can make it suitable for real-time applications. A case study of the implementation of the CDS on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link was performed. An improvement in Q-factor of~7 dB and an enhancement in data rate efficiency~43% were achieved using the proposed algorithms. Furthermore, an extra 20% data rate enhancement was obtained by guaranteeing to keep the CDM error automatically under the system threshold. The proposed system can be extended as a general software-based platform for brain-inspired decision making in smart systems in the presence of nonlinearity and non-Gaussian characteristics. Therefore, it can easily upgrade the conventional systems to a smart one for autonomic CDM applications.

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