Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing

Internet of Things (IoT) is a developing technology for supporting heterogeneous physical objects into smart things and improving the individuals living using wireless communication systems. Recently, many smart healthcare systems are based on the Internet of Medical Things (IoMT) to collect and analyze the data for infectious diseases, i.e., body fever, flu, COVID-19, shortness of breath, etc. with the least operation cost. However, the most important research challenges in such applications are storing the medical data on a secured cloud and make the disease diagnosis system more energy efficient. Additionally, the rapid explosion of IoMT technology has involved many cyber-criminals and continuous attempts to compromise medical devices with information loss and generating bogus certificates. Thus, the increase in modern technologies for healthcare applications based on IoMT, securing health data, and offering trusted communication against intruders is gaining much research attention. Therefore, this study aims to propose an energy-efficient IoT e-health model using artificial intelligence with homomorphic secret sharing, which aims to increase the maintainability of disease diagnosis systems and support trustworthy communication with the integration of the medical cloud. The proposed model is analyzed and proved its significance against relevant systems.

Download Full-text

Exploring Internet of Things and Artificial Intelligence for Smart Healthcare Solutions

Deep Neural Networks for Multimodal Imaging and Biomedical Applications - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-7998-3591-2.ch013 ◽

2020 ◽

pp. 205-218

Author(s):

G. Yamini

Keyword(s):

Artificial Intelligence ◽

Internet Of Things ◽

Improve Patient Care ◽

The Body ◽

Digital Data ◽

Healthcare Sector ◽

Healthcare Applications ◽

Smart Healthcare ◽

Fitness Trackers ◽

Medical Healthcare

Artificial intelligence integrated with the internet of things network could be used in the healthcare sector to improve patient care. The data obtained from the patient with the help of certain medical healthcare devices that include fitness trackers, mobile healthcare applications, and several wireless sensor networks integrated into the body of the patients promoted digital data that could be stored in the form of digital records. AI integrated with IoT could be able to predict diseases, monitor heartbeat rate, recommend preventive maintenance, measure temperature and body mass, and promote drug administration by having a review with the patient's medical history and detecting health defects. This chapter explores IoT and artificial intelligence for smart healthcare solutions.

Download Full-text

Reliable Bidirectional Data Transfer Approach for the Internet of Secured Medical Things Using ZigBee Wireless Network

Applied Sciences ◽

10.3390/app11219947 ◽

2021 ◽

Vol 11 (21) ◽

pp. 9947

Author(s):

Amjad Rehman ◽

Khalid Haseeb ◽

Suliman Mohamed Fati ◽

Jaime Lloret ◽

Lourdes Peñalver

Keyword(s):

Communication Systems ◽

Data Transfer ◽

Security Analysis ◽

Disease Diagnosis ◽

Cloud Services ◽

Smart Devices ◽

The Internet ◽

Network Simulator ◽

Data Systems ◽

Smart Healthcare

Nowadays, the Internet of Things (IoT) performs robust services for real-time applications in monitoring communication systems and generating meaningful information. The ZigBee devices offer low latency and manageable costs for wireless communication and support the process of physical data collection. Some biosensing systems comprise IoT-based ZigBee devices to monitor patient healthcare attributes and alert healthcare professionals for needed action. However, most of them still face unstable and frequent data interruption issues due to transmission service intrusions. Moreover, the medical data is publicly available using cloud services, and communicated through the smart devices to specialists for evaluation and disease diagnosis. Therefore, the applicable security analysis is another key factor for any medical system. This work proposed an approach for reliable network supervision with the internet of secured medical things using ZigBee networks for a smart healthcare system (RNM-SC). It aims to improve data systems with manageable congestion through load-balanced devices. Moreover, it also increases security performance in the presence of anomalies and offers data routing using the bidirectional heuristics technique. In addition, it deals with more realistic algorithm to associate only authorized devices and avoid the chances of compromising data. In the end, the communication between cloud and network applications is also protected from hostile actions, and only certified end-users can access the data. The proposed approach was tested and analyzed in Network Simulator (NS-3), and, compared to existing solutions, demonstrated significant and reliable performance improvements in terms of network throughput by 12%, energy consumption by 17%, packet drop ratio by 37%, end-to-end delay by 18%, routing complexity by 37%, and tampered packets by 37%.

Download Full-text

Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare

Sensors ◽

10.3390/s20133619 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3619 ◽

Cited By ~ 2

Author(s):

Gordana Gardašević ◽

Konstantinos Katzis ◽

Dragana Bajić ◽

Lazar Berbakov

Keyword(s):

Internet Of Things ◽

Energy Efficient ◽

Secure Communication ◽

Communication Technologies ◽

Healthcare Systems ◽

Healthcare Applications ◽

Privacy And Security ◽

Healthcare Data ◽

Smart Healthcare ◽

Communication Links

Future smart healthcare systems—often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.

Download Full-text

Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

IEEE Access ◽

10.1109/access.2021.3066365 ◽

2021 ◽

pp. 1-1

Author(s):

Romany F. Mansour ◽

Adnen El Amraoui ◽

Issam Nouaouri ◽

Vicente Garcia Diaz ◽

Deepak Gupta ◽

...

Keyword(s):

Artificial Intelligence ◽

Internet Of Things ◽

Disease Diagnosis ◽

Healthcare Systems ◽

Smart Healthcare ◽

Diagnosis Model

Download Full-text

Machine Learning in Agriculture

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i6.713 ◽

2018 ◽

Vol 8 (6) ◽

pp. 26 ◽

Cited By ~ 1

Author(s):

Matthew N. O. Sadiku ◽

Chandra M. M Kotteti ◽

Sarhan M. Musa

Keyword(s):

Climate Change ◽

Artificial Intelligence ◽

Machine Learning ◽

Disease Diagnosis ◽

Automated Detection ◽

Disease Prediction ◽

Paper Briefly ◽

Agriculture Sector ◽

Modern Agriculture ◽

Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data. Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change. Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Download Full-text

Exploring the Applications of Machine Learning in Healthcare

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666191220103417 ◽

2020 ◽

Vol 10 (4) ◽

pp. 458-472

Author(s):

Tausifa Jan Saleem ◽

Mohammad Ahsan Chishti

Keyword(s):

Machine Learning ◽

Disease Risk ◽

Disease Diagnosis ◽

Machine Intelligence ◽

Healthcare Applications ◽

Comprehensive Overview ◽

Machine Learning Applications ◽

Remote Healthcare ◽

Healthcare Monitoring ◽

Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

Download Full-text