Search

This paper intends to provide the reader with an overview of the main processes that are introducing artificial intelligence (AI) into healthcare services. The first part is organized according to an evolutionary perspective. We first describe the role that digital technologies have had in shaping the current healthcare methodologies and the relevant foundations for new evolutionary scenarios. Subsequently, the various evolutionary paths are illustrated with reference to AI techniques and their research activities, specifying their degree of readiness for actual clinical use. The organization of this paper is based on the interplay three pillars, namely, algorithms, enabling technologies and regulations, and healthcare methodologies. Through this organization we introduce the reader to the main evolutionary aspects of the healthcare ecosystem, to associate clinical needs with appropriate methodologies. We also explore the different aspects related to the Internet of the future that are not typically presented in papers that focus on AI, but that are equally crucial to determine the success of current research and development activities in healthcare.

The need for adaptivity and scalability in telecommunication systems has led to the introduction of a software-based approach to networking, in which network functions are virtualized and implemented in software modules, based on network function virtualization (NFV) technologies. The growing demand for low latency, efficiency, flexibility and security has placed some limitations on the adoption of these technologies, due to some problems of traditional virtualization solutions. However, the introduction of lightweight virtualization approaches is paving the way for new and better infrastructures for implementing network functions. This article discusses these new virtualization solutions and shows a proposal, based on serverless computing, that uses them to implement container-based virtualized network functions for the delivery of advanced Internet of Things (IoT) services. It includes open source software components to implement both the virtualization layer, implemented through Firecracker, and the runtime environment, based on Kata containers. A set of experiments shows that the proposed approach is fast, in order to boost new network functions, and more efficient than some baseline solutions, with minimal resource footprint. Therefore, it is an excellent candidate to implement NFV functions in the edge deployment of serverless services for the IoT.

Network caching is a technique used to speed-up user access to frequently requested contents in complex data networks. This paper presents a two-layer overlay network caching system for content distribution. It is used to define some caching scenarios with increasing complexity, which refers to real situations, including mobile 5G connectivity. For each scenario our aim is to maximize the hit ratio, which leads to the formulation of NP-complete optimization problems. The heuristic solutions proposed are based on the theory of the maximization of monotone submodular functions under matroid constraints. After the determination of the approximation ratio of the greedy heuristic algorithms proposed, a numerical performance analysis is shown. This analysis includes a comparison with the Least-Frequently Used (LFU) eviction strategy adapted to the analyzed systems. Results show very good performance, under the hypotheses of either known or unknown popularity of contents.