Self-configuration and smart binding control on IOT applications

Hsieh, Wen-Kuei; Hsieh, Wen-Hsu; Chen, Jiann-Liang; Lin, Ching-Fuh

doi:10.1109/icact.2016.7423282

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…The rise of Wireless Sensor Network (WSN) provided more flexibility to the system. The most commonly used communication protocols are Zigbee, Bluetooth, and WiFi (Hsieh et al, 2016). Gateways or router boxes aggregate sensors and actuators data.…”

Section: Related Papersmentioning

confidence: 99%

A QR code based framework for auto-configuration of IoT sensor networks in buildings

2021

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Worldwide buildings are responsible for about 40% of the overall consumption and contribute to an average of 30% percent of the global carbon emissions. Nevertheless, most current buildings lack efficient energy management systems because such solutions are very expensive, especially when necessary instrumentation needs to be installed after the building’s construction. As an alternative, we purpose the use of IoT sensor networks to retrofit existing medium and large-sized buildings to provide energy management capabilities in a cost-effective way. An IoT network auto-configuration platform for building energy management was developed. In order to efficiently manage metadata related to location and devices, a database using dynamic QR codes was created. Furthermore, we discuss the potential and shortcomings of different sensor-gateway pairing strategies that are applicable to an auto-configuring system. Lastly, we share our implementation of these concepts and demonstrate their use in a medium-sized building case study. The results show a trade-off between optimal configuration and total configuration time with a focus on the quality of the communication signal strength. The proposal provided the necessary automation for a cost-effective energy management system that can be deployed in both new constructions and existing buildings.

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Section: Related Papersmentioning

confidence: 99%

A QR code based framework for auto-configuration of IoT sensor networks in buildings

2021

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“…We observe that, as for linear transmission, when γ → ∞, we haveD d,2 = σ 2 v (1 − 2/π), and when γ → 0, we havē D d,2 = σ 2 v . From (17), one can check that the slope of the average distortion for BPSK transmission as γ → 0 is −4σ 2 v /π 2 , whereas the slope for linear transmission, obtained from (13c), is −2σ 2 v /π. This means that linear transmission has a decline in the distortion in the low SNR regime; therefore, it outperforms BPSK transmission, in this regime.…”

Section: Digital Transmission For One-bit Front Endmentioning

confidence: 99%

“…While these works focus on reliable transmission of digital information over long blocks, in many applications, such as the Internet-of-Things (IoT), cyberphysical systems and wireless sensor networks, the low-delay transfer of analog measurements is crucial. Specific examples include uncoded video transmission techniques [13], [14], memoryless amplify-and-forward relays, wearable sensors that detect neurological impulses [15], and real-time control applications for the IoT [16], [17]. In light of these emerging applications, this work considers the zero-delay transmission of an analog Gaussian source over a real single-input multipleoutput (SIMO) additive white Gaussian noise (AWGN) channel followed by a low-resolution ADC front end.…”

Section: Introductionmentioning

confidence: 99%

Zero-Delay Source-Channel Coding With a Low-Resolution ADC Front End

Varasteh

Rassouli

Simeone

et al. 2018

IEEE Trans. Inform. Theory

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Abstract-Motivated by the practical constraints arising in emerging sensor network and Internet-of-Things (IoT) applications, the zero-delay transmission of a Gaussian measurement over a real single-input multiple-output (SIMO) additive white Gaussian noise (AWGN) channel is studied with a low-resolution analog-to-digital converter (ADC) front end. Joint optimization of the encoder and the decoder mapping is tackled under both the mean squared error (MSE) distortion and the distortion outage probability (DOP) criteria, with an average power constraint on the channel input. Optimal encoder and decoder mappings are identified for a one-bit ADC front end under both criteria. For the MSE distortion, the optimal encoder mapping is shown to be non-linear in general, while it tends to a linear encoder in the low signal-to-noise ratio (SNR) regime, and to an antipodal digital encoder in the high SNR regime. This is in contrast to the optimality of linear encoding at all SNR values in the presence of a full-precision front end. For the DOP criterion, it is shown that the optimal encoder mapping is piecewise constant and can take only two opposite values when it is non-zero. For both the MSE distortion and the DOP criteria, necessary optimality conditions are then derived for K-level ADC front ends as well as front ends with multiple one-bit ADCs. These conditions are used to obtain numerically optimized solutions. Extensive numerical results are also provided in order to gain insights into the structure of the optimal encoding and decoding mappings.

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