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Alshdadi, A.A. IoT-Based Smart Home Assistance for Elderly People. Encyclopedia. Available online: https://encyclopedia.pub/entry/45629 (accessed on 24 June 2024).
Alshdadi AA. IoT-Based Smart Home Assistance for Elderly People. Encyclopedia. Available at: https://encyclopedia.pub/entry/45629. Accessed June 24, 2024.
Alshdadi, Abdulrahman A.. "IoT-Based Smart Home Assistance for Elderly People" Encyclopedia, https://encyclopedia.pub/entry/45629 (accessed June 24, 2024).
Alshdadi, A.A. (2023, June 15). IoT-Based Smart Home Assistance for Elderly People. In Encyclopedia. https://encyclopedia.pub/entry/45629
Alshdadi, Abdulrahman A.. "IoT-Based Smart Home Assistance for Elderly People." Encyclopedia. Web. 15 June, 2023.
IoT-Based Smart Home Assistance for Elderly People
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In the development of Internet-of-things (IoT)-based technology, there is a pre-programmed robot called Cyborg which is used for assisting elderly people. It moves around the home and observes the surrounding conditions. The Cyborg is developed and used in the smart home system. The features of a smart home system with IoT technology include temperature control, lighting control, surveillance, security, smart electricity, and water sensors.

smart home security KNN CYBORG sensor devices

1. Introduction

Due to the development of technology, dynamic changes have occurred in the automation and application of robotics and related systems. Nowadays, robotics plays a vital role in various applications, reducing the workload of human beings as well as errors made by humans. Robots are used in different surveillance processes such as detection of gas leaks and minimizing the risk of disaster through leakage in the chemical industry. Surveillance is the process of closely monitoring an industry, person, or group in the same and different situations. Surveillance is mainly needed in monitoring public places, border areas, companies, and industries in which the intervention of humans is difficult. This surveillance takes place with the help of an embedded system of robots. A robot is a pre-programmed electronic machine that replaces human work through automation and provides accurate results while minimizing error and improving time efficiency [1]. IoT-based devices are linked with one another by a network that connects electronic home appliances, vehicle-based electronic devices, actuators, and software, allows the exchange of information between one device and another. IoT devices can interact with other devices via Wi-Fi communication module by using the wireless sensor networks (WSN) in smart home electronic appliances and by Low Power Wireless Personal Area Networks (LoWPAN) using RFID (Radio-Frequency Identification). An IoT-based smart home environment operates sensor-based devices remotely using mobile applications [2][3].
Human Interaction Robots (HIR) are mainly used in activities with a social component, such as medicine, neuroscience, cognitive science, and robotics. In order to provide security, the need for human intervention can be replaced with Cyborg. This robot can assist elderly people who are home alone, helping them to avoid crime due to home invasion or theft. In this case, it is necessary to provide security to elderly people by implementing a smart home environment system that contains the required sensor devices and it can transmit sensor signals through a communication module in order to alert the user and allow them to take precautionary steps [4][5]. The smart home secure environment enhances the lifestyle of human beings by providing security, detecting gas leakage in the kitchen, monitoring temperature and humidity in the home, detecting intruders, and more. This can be achieved by monitoring the surroundings of the smart home using a Raspberry Pi-based wireless camera, capturing images with related information, and sending it to the server. The main components of Cyborg are DC motors, a battery, and a wheel chassis, and it can be implemented in either automatic or manual mode [6].

2. Evaluation of IoT-Based Smart Home Assistance for Elderly People Using Robot

Smart home electronic appliances based on IoT technology require automatic ON/OFF operation using a remote control-based application, voice-based technology, or fixed-time scheduling. A notification can be sent to the user by the server. This control is completely based on the activities of the user and passing the commands which can be triggered the activities through the mobile phone [7][8][9]. C. Victor et al. [10] proposed an IoT-based sensor system for monitoring the temperature in the environment. Using a temperature sensor, the system can collect sensor signals and store them in the server. Gladence et al. [11] proposed a client–server-based machine learning algorithm implemented for establishing an automated smart home environment control system able to interact with humans who send commands or triggering the smart appliances. M. Wendy et al. [12] presented a review of effective smart home technology to support elderly people in aspects related to health and security issues. Mehmood et al. [13] proposed an innovative concept involving managing a cloud storage platform, detecting hindrances, activating IoT devices by passing commands, executing those commands, and then transmitting the information to the registered users via mobile notification. To monitor health-related issues for elderly people in smart homes, various machine learning algorithms (LSTM, SVM, and RNN) can be used. IoT devices can closely observe health conditions of elderly people, analyze their symptoms, and make predictions related to disease, as well as helping patients to consult their physicians and alert them to take medicine at the proper time [14]. Sensor devices are used with wireless networks, software, and computers to detect threats which affect the smart home environment. The implementation of the CNN model produces efficient detection of threats [15]. The Cyborg system can be used to save power, as it is able to automatically switch unnecessary electronic devices into the OFF state. In addition, it can detect the presence of human beings in the external surroundings of the smart home. At the same time, it can send a notification to the resident to perform important activities such as taking medicine, watering plants, etc. The proposed smart home system interfaces with sensor devices and assists elderly people in the smart home environment based on the generated sensor signals [16]. Table 1 enumerates related works on smart home environment systems along with the technology and sensor measurements employed by the respective systems.
Table 1. List of related works on smart home environment systems and on the technologies and sensor measurement approaches employed by the respective systems.
Many earlier works demonstrated the use of IoT technology for energy efficiency, monitoring, and activity detection in a smart home environment. Below the present selected works, which are tabulated in Table 1 along with their prominent features.
In [17], the author presented a smart home remote control system based on wireless sensor networks that collect positioning information and use actuators to control electrical appliances and operate alarms. In [18], X. Gengyi applied support vector machine (SVM) in a smart-home energy monitoring system using a cloud computing-based platform. The proposed solution improves energy efficiency and makes it easier for human interaction. In [19], C. Zhou et al. proposed a design for a smart home system based on virtual reality. Virtual reality was used to improve control interaction in the smart home. Their experimental results indicated that control methods could be simplified and costs reduced by as much as twenty percent through the use of virtual reality. In [20], P. Sharma et al. proposed a design for an IoT system using NodeMCU for real-time supervision of sensor measurements, allowing the user to control electrical loads in a smart home. O. Taiwo et al. [21] proposed a smart home automation mobile application that uses an Arduino microcontroller and personal area communication technologies such as Zigbee and Bluetooth. The practicality of the system was demonstrated through a simulation of the smart home environment.
In [22], M. S. Soliman et al. proposed a smart home automation system based on Arduino and Labview that allows the user to control temperature, save energy, and detect intruders. M. Naing et al. (2019) [23] demonstrated a proposed smart home automation system through a prototype implementation employing two Arduino Nano sensors. Sensors for measuring temperature, smoke, and motion were interfaced with these microcontrollers, which in turn interfaced with actuators to control and secure the home. R. D. Manu et al. (2019) [24] proposed a smart home system able to measure and respond to human activities using long-short term memory (LSTM) deep learning-based decision-making. S. K. Saravanan et al. (2019) [25] proposed a smart home controller using Arduino and Android. A smart door actuator was secured using a multi-factor authentication mechanism. L. D. Liao et al. (2019) [26] proposed the design of a smart home system using Arduino–Uno that provides user control and monitoring through a mobile application. Temperature and motion sensors were connected and controlled by the system to demonstrate its application in a smart home environment.
D. Popa et al. (2019) [27] demonstrate a smart home application where measurements of energy consumption and other sensor data could be stored on a cloud and later analyzed using machine learning methods for improved environmental sustainability and energy efficiency.
The authors of [28] applied linear discriminant analysis to classify power quality disturbances and carry out a performance analysis using KNN, naive Bayes, support vector machine (SVM), and random forest (RF) classifiers. Their results showed that higher classification accuracy was obtained in the presence of noise. In [29], Moraes et al. used a naive Bayes algorithm to propose a structured data mining model that can predict whether a smaller enterprise can join a business association with given attributes. The proposed approach can be utilized as a decision assistance tool for business associations to choose member enterprises. In [30], the authors used four different classifiers, i.e., KNN, naive Bayes, decision tree, and random forest approaches, to distinguish between defective and non-defective metal parts using laser-induced-breakdown spectroscopy. The above-mentioned works show that machine learning algorithms can be used to make accurate predictions and to inform decisions in many situations and for a variety of data formats.
To make the literature survey more comprehensive, below researchers include several recent optimization methods for feature selection and classification. The authors of [31] proposed a hybrid feature selection method using a combination of the Butterfly optimization algorithm and the Ant Lion optimizer for breast cancer prediction. The proposed hybrid method outperforms both component methods for breast cancer diagnosis in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve.
Chakraborty et al. [32] proposed an improved whale optimization method for segmentation of chest X-Rays from patients with symptoms of COVID-19. During the global search phase, a random initialization is used to exploit after exploration. The proposed method outperformed the original method in terms of segmentation accuracy.
Sayed et al. [33] adopted a hybrid approach combining a convolutional neural network with Bald Eagle optimization to improve detection performance in melanoma skin cancer prediction. The robustness and accuracy of the proposed approach were verified as being superior through a comparison with state-of-the-art methods.
Xing et al. [34] proposed a modified whale optimization method using a quasi-Gaussian “bare bones” method. The modified method was able to promote diversity and expand the scope of the solution space.
Piri et al. [35] proposed a modified optimization method based on the Harris Hawk optimizer. This method, called multi-objective quadratic binary Harris Hawk optimization, uses a KNN classifier to extract the optimal feature subsets. The proposed methodology proved superior thanks to its better combination of fitness assessment criteria.

References

  1. Sumathi, S.; Aditya, S.; Archanaa, B.; Priya, G.L. Cyborg—A Surveillance Droid Using Raspberry Pi and Internet of Things. Int. Res. J. Eng. Technol. 2020, 7, 529.
  2. Wang, C.; Liu, Q.; Xing, L.; Guan, Q.; Yang, C.; Yu, M. Reliability analysis of smart home sensor systems subject to competing failures. Reliab. Eng. Syst. Saf. 2022, 221, 108327.
  3. Alghayadh, F.; Debnath, D. Hid-smart: Hybrid intrusion detection model for smart home. In Proceedings of the 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 6–8 January 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 0384–0389.
  4. Alghayadh, F.; Debnath, D. A hybrid intrusion detection system for smart home security based on machine learning and user behavior. Adv. Internet Things 2021, 11, 10–25.
  5. Cele, B. Quarter One Crime Statistics, South African Government. 2022. Available online: https://www.gov.za/speeches/minister-bheki-cele-quarter-one-crime-statistics-20222023-19-aug-2022-0000 (accessed on 7 April 2023).
  6. Taiwo, O.; Ezugwu, A.E. Internet of things-based intelligent smart home control system. Secur. Commun. Netw. 2021, 2021, 1–17.
  7. Dhanusha, C.; Kumar, A.S. Deep recurrent Q reinforcement learning model to predict the Alzheimer disease using smart home sensor data. In Proceedings of the IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2021; Volume 1074, p. 012014.
  8. Gupta, P.; McClatchey, R.; Caleb-Solly, P. Tracking changes in user activity from unlabelled smart home sensor data using unsupervised learning methods. Neural Comput. Appl. 2020, 32, 12351–12362.
  9. Pattamaset, S.; Choi, J.S. Irrelevant data elimination based on a k-means clustering algorithm for efficient data aggregation and human activity classification in smart home sensor networks. Int. J. Distrib. Sens. Netw. 2020, 16, 1550147720929828.
  10. Chang, V.; Martin, C. An industrial IoT sensor system for high-temperature measurement. Comput. Electr. Eng. 2021, 95, 107439.
  11. Gladence, L.M.; Anu, V.M.; Rathna, R.; Brumancia, E. Recommender system for home automation using IoT and artificial intelligence. J. Ambient. Intell. Humaniz. Comput. 2020, 1–9. Available online: https://link.springer.com/article/10.1007/s12652-020-01968-2 (accessed on 7 April 2023).
  12. Moyle, W.; Murfield, J.; Lion, K. The effectiveness of smart home technologies to support the health outcomes of community-dwelling older adults living with dementia: A scoping review. Int. J. Med Inform. 2021, 153, 104513.
  13. Mehmood, F.; Ullah, I.; Ahmad, S.; Kim, D. Object detection mechanism based on deep learning algorithm using embedded IoT devices for smart home appliances control in CoT. J. Ambient. Intell. Humaniz. Comput. 2019, 1–17. Available online: https://link.springer.com/article/10.1007/s12652-019-01272-8 (accessed on 7 April 2023).
  14. Mshali, H.; Lemlouma, T.; Moloney, M.; Magoni, D. A survey on health monitoring systems for health smart homes. Int. J. Ind. Ergon. 2018, 66, 26–56.
  15. Alghayadh, F.; Debnath, D. A hybrid intrusion detection system for smart home security. In Proceedings of the 2020 IEEE International Conference on Electro Information Technology (EIT), Chicago, IL, USA, 31 July–1 August 2020; IEEE: Bristol, UK, 2020; pp. 319–323.
  16. Thomas, A.; Joseph, G.B.; Augustine, M. CYBORG-The Smart Home Assistance Robot. Int. Adv. Res. J. Sci. Eng. Technol. 2017, 4, 118–120.
  17. Zheng, R. Indoor smart design algorithm based on smart home sensor. J. Sens. 2022, 2022, 2251046.
  18. Xiao, G. Machine learning in smart home energy monitoring system. In Proceedings of the IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2021; Volume 769, p. 042035.
  19. Zhou, C.; Huang, T.; Liang, S. Smart home R&D system based on virtual reality. J. Intell. Fuzzy Syst. 2021, 40, 3045–3054.
  20. Sharma, P.; Kantha, P. ‘Blynk’ cloud server based monitoring and control using ‘NodeMCU’. Int. Res. J. Eng. Technol. 2020, 7, 1362–1366.
  21. Taiwo, O.; Ezugwu, A.E.; Rana, N.; Abdulhamid, S.M. Smart home automation system using zigbee, bluetooth and arduino technologies. In Proceedings of the Computational Science and Its Applications–ICCSA 2020: 20th International Conference, Cagliari, Italy, 1–4 July 2020; Proceedings, Part VI 20. Springer: Berlin/Heidelberg, Germany, 2020; pp. 587–597.
  22. Soliman, M.S.; Alahmadi, A.A.; Maash, A.A.; Elhabib, M.O. Design and implementation of a real-time smart home automation system based on arduino microcontroller kit and labview platform. Int. J. Appl. Eng. Res. 2017, 12, 7259–7264.
  23. Naing, M.; Hlaing, N.N.S. Arduino based smart home automation system. Int. J. Trend Sci. Res. Dev. 2019, 3, 276–280.
  24. Manu, R.D.; Kumar, S.; Snehashish, S.; Rekha, K. Smart home automation using IoT and deep learning. Int. Res. J. Eng. Technol. 2019, 6, 1–4.
  25. Saravanan, S.; Nainar, A.; Marichamy, S. Android based smart automation system using multiple authentications. IRE J. 2019, 3, 60–65.
  26. Liao, L.D.; Wang, Y.; Tsao, Y.C.; Wang, I.J.; Jhang, D.F.; Chu, T.S.; Tsao, C.H.; Tsai, C.N.; Chen, S.F.; Chuang, C.C.; et al. Design and validation of a multifunctional android-based smart home control and monitoring system. IEEE Access 2019, 7, 163313–163322.
  27. Popa, D.; Pop, F.; Serbanescu, C.; Castiglione, A. Deep learning model for home automation and energy reduction in a smart home environment platform. Neural Comput. Appl. 2019, 31, 1317–1337.
  28. Singh, G.; Pal, Y.; Dahiya, A.K. Classification of Power Quality Disturbances using Linear Discriminant Analysis. Appl. Soft Comput. 2023, 138, 110181.
  29. Moraes, J.d.; Schaefer, J.L.; Schreiber, J.N.C.; Thomas, J.D.; Nara, E.O.B. Algorithm applied: Attracting MSEs to business associations. J. Bus. Ind. Mark. 2020, 35, 13–22.
  30. Lin, J.; Yang, J.; Huang, Y.; Lin, X. Defect identification of metal additive manufacturing parts based on laser-induced breakdown spectroscopy and machine learning. Appl. Phys. B 2021, 127, 1–10.
  31. Thawkar, S.; Sharma, S.; Khanna, M.; kumar Singh, L. Breast cancer prediction using a hybrid method based on Butterfly Optimization Algorithm and Ant Lion Optimizer. Comput. Biol. Med. 2021, 139, 104968.
  32. Chakraborty, S.; Saha, A.K.; Nama, S.; Debnath, S. COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction. Comput. Biol. Med. 2021, 139, 104984.
  33. Sayed, G.I.; Soliman, M.M.; Hassanien, A.E. A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization. Comput. Biol. Med. 2021, 136, 104712.
  34. Xing, J.; Zhao, H.; Chen, H.; Deng, R.; Xiao, L. Boosting whale optimizer with quasi-oppositional learning and Gaussian barebone for feature selection and COVID-19 image segmentation. J. Bionic Eng. 2023, 20, 797–818.
  35. Piri, J.; Mohapatra, P. An analytical study of modified multi-objective Harris Hawk Optimizer towards medical data feature selection. Comput. Biol. Med. 2021, 135, 104558.
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