Secure and energy-efficient framework using Internet of Medical Things for e-healthcare

Abstract

In various fields, the internet of things (IoT) gains a lot of popularity due to its autonomous sensors operations with the least cost. In medical and healthcare applications, the IoT devices develop an ecosystem to sense the medical conditions of the patients' such as blood pressure, oxygen level, heartbeat, temperature, etc. and take appropriate actions on an emergency basis. Using it, the healthcare-related data of patients is transmitted towards the remote users and medical centers for post-analysis. Different solutions have been proposed using Wireless Body Area Network (WBAN) to monitor the medical status of the patients based on low powered biosensor nodes, however, preventing increased energy consumption and communication costs are demanding and interesting problems. The issue of unbalanced energy consumption between biosensor nodes degrades the timely delivery of the patient's information to remote centers and gives a negative impact on the medical system. Moreover, the sensitive data of the patient is transmitting over the insecure Internet and prone to vulnerable security threats. Therefore, data privacy and integrity from malicious traffic are another challenging research issue for medical applications. This research article aims to a proposed secure and energy-efficient framework using Internet of Medical Things (IoMT) for e-healthcare (SEF-IoMT), which primary objective is to decrease the communication overhead and energy consumption between biosensors while transmitting the healthcare data on a convenient manner, and the other hand, it also secures the medical data of the patients against unauthentic and malicious nodes to improve the network privacy and integrity. The simulated results exhibit that the proposed framework improves the performance of medical systems for network throughput by 18%, packets loss rate by 44%, end-to-end delay by 26%, energy consumption by 29%, and link breaches by 48% than other states of the art solutions.

Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types

“…Machine learning techniques play a significant role in infection detection and prediction (Perveen et al, 2020;Yousaf et al, 2020). The trained-techniques can process big data at high speed to find infection cases and trends to warn the decision-makers (Sadad, Munir, Saba, & Hussain, 2018;Saba, Haseeb. et al, 2020;Saba, Mohamed, et al, 2020;Ullah et al, 2019).…”

More Is Not Enough: A Deeper Understanding of the COVID-19 Impacts on Healthcare, Energy and Environment Is Crucial

“…The improvements in terms of AI algorithms and energy efficiency have the potential to reduce electricity consumption for long-term monitoring and data analytics [ 85 ]. Practitioners have been recently trying the energy-efficient IoT-health monitoring system [ 86 ] and the energy-efficient and secure framework based on Internet of Medical Things (IoMT) [ 87 ].…”

“…The fossil energy usage, air emissions, waste treatment by incineration, and healthcare with energy support and environmental impacts are all driving factors for climate change. Reversely, the proper regulations on climate change offer constraints to force the transitions of the healthcare, energy and environment management, e.g., the IoMT-based energy-efficient e-healthcare [ 87 ], the sustainable waste management under Circular Economy [ 144 ] and the transitions of clean and renewable energy [ 145 ].…”

“…Although the world was at the atmosphere of panic, sadness, anger and anxiety due to COVID-19 [ 58 ], the new day’s sun has been rising, gratifying that we are already in the advancing stage of an era of digitalisation. There are many digital solutions for many problems, such as e-tracking for COVID-19 [ 62 ], telemedicine [ 69 ], e-healthcare [ 87 ], e-learning [ 159 ], e-office [ 160 ] and e-shopping [ 161 ]. As discussed in Section 2 to Section 4 , the digitalisation and ICT, with proper support from design, production and manufacturing, have the potential to play the crucial role in coping with the pandemics, sustainable environment management and energy efficiency improvement.…”

Application of Solar Energy Technology in Green Healthcare Camps for Fighting COVID-19 Outbreak in Saudi Arabia

“…The system was studied toward energy management and cybersecurity considerations, and machine learning was utilized to automatically attain the healthcare functions using widely distributed indicators. In [22][23][24], the internet of things (IoT) was also applied to automatically monitor the healthcare to enhance the system quality and overcome the technical challenges. Accordingly, the patient condition monitoring was proposed to smartly assess the patients' health considering medical measurements such as blood pressure, temperature, etc.…”