A secured internet of robotic things (IoRT) for long-term care services in a smart building

Abstract

Long-term care refers to any support, both medical and non-medical, provided to the elderly with a chronic illness or disability due to physical or mental conditions. Since the cost of long-term care insurance is not inexpensive, low-cost devices and sensors can be used to create medical assistance systems to reduce human maintenance costs. The requirement of security and privacy under healthcare information protection is a critical issue for internet of medical things (IoMT) data transmission. In this paper, we designed an IoMT security robot for a long-term care system. The goal of this IoMT security robot is to provide secure transmission of the residents' private information. It is composed of three layers, namely, collection, encryption, and transmission. The function of the IoMT security robot is to first collect data from the patient or the elderly, then provide efficient data encryption, and deliver secured data transmission mechanisms to send the valuable data to the cloud. This IoMT security robot also has a server authentication mechanism, and a support IoT and IoMT devices inspection function. Our evaluation results showed that even when we utilized a low power consumption device like Raspberry Pi, AES algorithm achieved an encrypt and decrypt of 100-100 K bytes under 9 ms, which is a lot better than ECC, which takes about 104 ms. Further, we found that the AES only takes 0.00015 s to decrypt 100 Bytes data, which is way faster than the ECC algorithm, which takes 0.09 s.

Secure Authentication and Data Transmission for Patients Healthcare Data in Internet of Medical Things

“…To ensure safety and privacy, patient data, including medical conditions and medical equipment used by such customers, must be protected. Professionals in the healthcare industry discreetly communicate information to research health and provide customers with treatment on time (Vandanna and Venkateshwarlu, 2020;Chang et al, 2023). Deep learning (DL) techniques are commonly used in combination with cryptosystems and fingerprint systems to authenticate, detect anomalies, and demonstrate resistance to medical systems.…”