Search

Healthcare is one of the notable areas where the integration of the Internet of Things (IoT) is highly adopted, also known as the Medical IoT (MIoT). So far, MIoT is revolutionizing healthcare because it provides many advantages for the benefit of patients and healthcare personnel. The use of MIoT is becoming a booming trend, generating a large amount of IoT data, which requires proper analysis to infer meaningful information. This has led to the rise of deploying artificial intelligence (AI) technologies, such as machine learning (ML) and deep learning (DL) algorithms, to learn the meaning of this underlying medical data, where the learning process usually occurs in the cloud or telemedicine servers. Due to the exponential growth of MIoT devices and widely distributed private MIoT data sets, it is becoming a challenge to use centralized learning AI algorithms for such tasks. In this connection, federated learning (FL) is gaining traction as a possible method of learning on devices that do not need to migrate private and sensitive data to a central cloud. The terminal equipment and the central server in FL only share learning model updates to ensure that sensitive data is always kept secret. Even though this has recently become a promising research area, no other research has been conducted on this topic recently. In this paper, we synthesize recent literature and FL improvements to support FL-driven MIoT applications and services in healthcare. The findings of this research help stakeholders in academia and industry to realize the competitive advantage of the most advanced privacy preserved MIoT systems based on federal learning. [ABSTRACT FROM AUTHOR]

In a Multicore architecture, each package consists of large number of processors. This increase in processor core brings new evolution in parallel computing. Besides enormous performance enhancement, this multicore package injects lot of challenges and opportunities on the operating system scheduling point of view. We know that multiagent system is concerned with the development and analysis of optimization problems. The main objective of multiagent system is to invent some methodologies that make the developer to build complex systems that can be used to solve sophisticated problems. This is difficult for an individual agent to solve. In this paper we combine the AMAS theory of multiagent system with the scheduler of operating system to develop a new process scheduling algorithm for multicore architecture. This multiagent based scheduling algorithm promises in minimizing the average waiting time of the processes in the centralized queue and also reduces the task of the scheduler. We actually modified and simulated the linux 2.6.11 kernel process scheduler to incorporate the multiagent system concept. The comparison is made for different number of cores with multiple combinations of process and the results are shown for average waiting time Vs number of cores in the centralized queue. [ABSTRACT FROM AUTHOR]