ABSTRACT
Remote Patient Monitoring (RPM) using Electronic Healthcare (E-health) is a growing phenomenon enabling doctors predict patient health such as possible cardiac arrests from identified abnormal arrythmia. Remote Patient Monitoring enables healthcare staff to notify patients with preventive measures to avoid a medical emergency reducing patient stress. However weak authentication security protocols in IoT wearables such as pacemakers, enable cyberattacks to transmit corrupt data, preventing patients from receiving medical care. In this paper we focus on the security of wearable devices for reliable healthcare services and propose a Lightweight Key Agreement (LKA) based authentication scheme for securing Device-to-Device (D2D) communication. A Network Key Manager on the edge builds keys for each device for device validation. Device authentication requests are verified using certificates, reducing network communication costs. E-health empowered mobile devices are store authentication certificates for future seamless device validation. The LKA scheme is evaluated and compared with existing studies and exhibits reduced operation time for key generation operation costs and lower communication costs incurred during the execution of the device authentication protocol compared with other studies. The LKA scheme further exhibits reduced latency when compared with the three existing schemes due to reduced communication costs.
ABSTRACT
Electronic textiles are regarded as one of the most important computation platforms for future computer-assisted health-monitoring applications. In these novel systems, multiple batteries are used in order to prolong their operational lifetime, which is a significant metric for system usability. However, due to the nonlinear features of batteries, computing systems with multiple batteries cannot achieve the same battery efficiency as those powered by a monolithic battery of equal capacity. In this paper, we propose an algorithm aiming to maximize battery efficiency globally for the computer-assisted health-care systems with multiple batteries. Based on an accurate analytical battery model, the concept of weighted battery fatigue degree is introduced and the novel battery-scheduling algorithm called predicted weighted fatigue degree least first (PWFDLF) is developed. Besides, we also discuss our attempts during search PWFDLF: a weighted round-robin (WRR) and a greedy algorithm achieving highest local battery efficiency, which reduces to the sequential discharging policy. Evaluation results show that a considerable improvement in battery efficiency can be obtained by PWFDLF under various battery configurations and current profiles compared to conventional sequential and WRR discharging policies.
