Configurable ZigBee-based control system for people with multiple disabilities in smart homes

Aburukba, Raafat; Al-Ali, A. R.; Kandil, Nourhan; AbuDamis, Diala

doi:10.1109/iccsii.2016.7462435

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…While WLAN/WPAN provide a short range connectivity (about 1-100 metres) to support device-to-device (D2D) communication with a high data rate, LPWAN does not require much power, nor bandwidth to operate and provides long range connectivity (up to 50 kilometres) [35]. Some typical communication technologies of WLAN/WPAN including Radio-frequency Identification (RFID) [36], Bluetooth Low Energy 4.0 [37], Zigbee [38], and Wi-Fi (IEEE 802.11) are applied in different IoT applications such as Smart Tourism [39], Smart Home [40], Connected Health [41]. LPWAN technologies including unlicensed (e.g.…”

Section: ) Communication Technologiesmentioning

confidence: 99%

Analytics Everywhere: Generating Insights From the Internet of Things

2019

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The Internet of Things is expected to generate an unprecedented number of unbounded data streams that will produce a paradigm shift when it comes to data analytics. We are moving away from performing analytics in a public or private cloud to performing analytics locally at the fog and edge resources. In this paper, we propose a network of tasks utilizing edge, fog, and cloud computing that are designed to support an Analytics Everywhere framework. The aim is to integrate a variety of computational resources and analytical capabilities according to a data life-cycle. We demonstrate the proposed framework using an application in smart transit. INDEX TERMS Descriptive analytics, diagnostic analytics, predictive analytics, edge computing, fog computing, cloud computing, Internet of Things.

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Section: ) Communication Technologiesmentioning

confidence: 99%

Analytics Everywhere: Generating Insights From the Internet of Things

2019

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“…Numerous proposals have addressed the monitoring of people with disabilities [ 2 , 3 , 4 , 5 ]; however, these share three main drawbacks. First, to the best of our knowledge, none have attempted to address the customization needed for each IoT HMS scenario.…”

Section: Introductionmentioning

confidence: 99%

“…As stated by the authors of [ 4 ], it is difficult to make a clear distinction between these groups of disabilities because individuals with one disability often suffer from other limitations or chronic health problems. Thus, research has been undertaken to attempt to address different types of disabilities; for example, [ 5 ] proposed a framework to manage a collection of devices and applications in a home environment for people with different disabilities (blind, nonspeaking, etc.). However, this approach did not have the benefits of today’s IoT systems, which allow continuous connectivity of all devices, and thus allow uninterrupted telemetries and can enable a response in real time.…”

Section: Introductionmentioning

confidence: 99%

MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities

Meliá

Nasabeh

Luján-Mora

2021

IJERPH

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The need to remotely monitor people with disabilities has increased due to growth in their number in recent years. The democratization of Internet of Things (IoT) devices facilitates the implementation of healthcare-monitoring systems (HMSs) that are capable of supporting disabilities and diseases. However, to achieve their full potential, these devices must efficiently address the customization demanded by different IoT HMS scenarios. This work introduces a new approach, called Modeling Scenarios of Internet of Things (MoSIoT), which allows healthcare experts to model and simulate IoT HMS scenarios defined for different disabilities and diseases. MoSIoT comprises a set of models based on the model-driven engineering (MDE) paradigm, which first allows simulation of a complete IoT HMS scenario, followed by generation of a final IoT system. In the current study, we used a real scenario defined by a recognized medical publication for a patient with Alzheimer’s disease to validate this proposal. Furthermore, we present an implementation based on an enterprise cloud architecture that provides the simulation data to a commercial IoT hub, such as Azure IoT Central.

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“…Meanwhile, enhancement of system on chip (SoC) and cloud computing have stimulated concerning over practical low-power networks, requiring for both security and low-power consumption, for instance, Bluetooth Low Energy (BLE), Zigbee, etc. [2]. Nowadays, NCSs are becoming more and more popular in both industry and daily life.…”

Section: Introductionmentioning

confidence: 99%

Control strategy for networked control systems with time delay and packet dropout using linear matrix inequalities

Wang

Fukushima

2020

J Wireless Com Network

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A control strategy using linear matrix inequalities towards time delay and stochastic packet dropout of networked control system is proposed in this paper. First, a reconfigured target plant of networked control system is built based on a linear time-invariant target. Before discretization of target plant, a Pade approximation is employed towards the time delay segment of reconfigured target plant, while zero-order holder compensator and an estimator are employed for packet dropout of control input and measured output, respectively. Then the control target is reformed as a programming problem of an uncertain time-variant multi-parametric state space. In order to deal with such problem, herein the time-variant state space is rewritten as a linear parametric-varying prediction model by enumerating all possible occasions. Afterwards, a novel robust model predictive controller is proposed as a constrained minimax optimization of the predictive cost function. Finally, we present two simulations based on an aircraft model and a second-order numeric model, by which step responses and explicit solutions are derived, performed and analysed. Simulation results demonstrate both the stability and robustness of the proposed control strategy.

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Configurable ZigBee-based control system for people with multiple disabilities in smart homes

Cited by 11 publications

References 8 publications

Analytics Everywhere: Generating Insights From the Internet of Things

Analytics Everywhere: Generating Insights From the Internet of Things

MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities

Control strategy for networked control systems with time delay and packet dropout using linear matrix inequalities

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