Smart Android Based Home Automation System Using Internet of Things (IoT)

Khan, Muhammad Abbas; Ahmad, Ijaz; Nordin, Anis Nurashikin; Ahmed, Awais; Mewada, Hiren; Daradkeh, Yousef Ibrahim; Rasheed, Saim; Eldin, Sayed M.; Shafiq, Muhammad

doi:10.3390/su141710717

Cited by 25 publications

(15 citation statements)

References 50 publications

(70 reference statements)

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“…The existing research shows that there have been substantial efforts to implement Edge Computing Solutions on a wide variety of platforms. Different researchers have focused on different aspects of these solutions, ranging from computational complexity to power consumption [15]. In this research, we propose a solution with reduced complexity and high classification accuracy as compared to the PC-based solution.…”

Section: Review Of Edge Computingmentioning

confidence: 99%

Real-time edge computing design for physiological signal analysis and classification

Suppiah,

Noori,

Abidi

et al. 2024

Biomed. Phys. Eng. Express

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Physiological Signals like Electromography (EMG) and Electroencephalography (EEG) can be analysed and decoded to provide vital information that can be used in a range of applications like rehabilitative robotics and remote device control. The process of acquiring and using these signals requires many compute-intensive tasks like signal acquisition, signal processing, feature extraction and machine learning. Performing these activities on a PC-based system with well-established software tools like Python and Matlab is the first step in designing solutions based upon these signals. In the application domain of rehabilitative robotics, one of the main goals is to develop solutions that can be deployed for the use of individuals who need them in improving their Acitivities-for-Daily Living (ADL). To achieve this objective, the final solution must be deployed onto an embedded solution that allows high portability and ease-of-use. Porting a solution from a PC-based environment onto a resource-constraint one such as a microcontroller poses many challenges. In this research paper, we propose the use of a ARM-based Corex M-4 processor. We explore the various stages of the design from the initial testing and validation, to the deployment of the proposed algorithm on the controller, and further investigate the use of Cepstrum features to obtain a high classification accuracy with minimal input features. The proposed solution is able to achieve an average classification accuracy of 95.34% for all five classes in the EMG domain and 96.16% in the EEG domain on the embedded board.

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Section: Review Of Edge Computingmentioning

confidence: 99%

Real-time edge computing design for physiological signal analysis and classification

Suppiah,

Noori,

Abidi

et al. 2024

Biomed. Phys. Eng. Express

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“…Ref. [36] delves into smart home system automation, managing the smart IoT devices through design functionality using real-world device data, energy consumption measurements, but excluding real-world infrastructure data, data optimisation, Fog computing, latency, and network bandwidth usage performance metrics. Ref.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, ref. [36] developed an adaptable and user-friendly home automation system that allows smart building users to control smart IoT devices using real-world device data through the various case studies presented in their research. These research studies focused on device layer real-world data parameters only.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

Lawal,

Olaniyi,

Gibson

2023

Applied Sciences

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It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices.

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“…Once the involvement of humans is minimized in the manual control of this appliances, programmable systems are introduced to make flexible the use and control of the connected loads and appliances. Home automation systems can be applied for both high and low number of electronic devices connected to electrical power depending on the design of the system [1,11]. Awareness of home automation systems has tremendously increased in the world today and is hopefully anticipated to increase in popularity in years to come in order to attract high marketing consumption.…”

Section: Introductionmentioning

confidence: 99%

Development of an Android Based Home Automation System

Okomba

Esan

Omodunbi

et al. 2023

AJERD

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As automation technology keeps advancing, the future of modern homes is continually changing from manual system into automatic ones that include remote control process. A Conventional means of accessing home electronic appliances are rigid and demands user presence to attend to different locations of such appliances before they can be used, this can be stressful for the elderly, the cripples, and provides limited ease of operation for everyone. The need for wireless communication function of the home appliances becomes very important in order to increase the flexibility of accessing them remotely by a user. The main aim of the paper is to develop a smart home automation system, for accessing and controlling devices and appliances remotely using android based phone app. An ESP8266 microcontroller was interfaced to a servo motor that controls door movement and also to an AC bulb through a relay at the signal receiver end of the system. An MIT application inventor platform was used to build a mobile app on an android phone at the signal transmitter end of the system; this enables the transmission of on and off signals to the receiver end through Wi-Fi connection to access the connected loads (door and lamp). This paper describes the hardware and software architecture of the system. The prototype of the home automation is implemented and tested on hardware and it gave the exact an expected result.

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Smart Android Based Home Automation System Using Internet of Things (IoT)

Cited by 25 publications

References 50 publications

Real-time edge computing design for physiological signal analysis and classification

Real-time edge computing design for physiological signal analysis and classification

Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

Development of an Android Based Home Automation System

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