Survey of advances and challenges in intelligent autonomy for distributed cyber‐physical systems

Abstract

With the evolution of the Internet of things and smart cities, a new trend of the Internet of simulation has emerged to utilise the technologies of cloud, edge, fog computing, and high-performance computing for design and analysis of complex cyber-physical systems using simulation. These technologies although being applied to the domains of big data and deep learning are not adequate to cope with the scale and complexity of emerging connected, smart, and autonomous systems. This study explores the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. This is expanded to look at existing computational infrastructure and how it can be used to support these applications. A detailed review is presented of advances in approaches providing and supporting intelligence as a service. Finally, some of the remaining challenges due to the explosion of data streams; issues of safety and security; and others related to big data, a model of reality, augmentation of systems, and computation are examined. 2 Emerging applications The emergence of the Internet of anything and everything [14]from IoT [26] is driving smarter and more context-aware systems and applications. These concepts augment the technologies related to cloud and edge computing [27] and allow computational power to be balanced against location which has an impact on both network latencies and security. The ubiquitous management of the computational systems and communication networks is anticipated to be augmenting and penetrating most cyber-physical systems that we interact with on a daily basis within the coming decade. One example domain is that of cooperative robotics where advances in autonomous systems [28] are enhanced with additional computational capability from the cloud. The resulting emerging field of cloud robotics combines the two research fields to provide intelligence services to robots from the cloud [29-31],

Cyber Resilience and Incident Response in Smart Cities: A Systematic Literature Review

“…The search criteria identified several non-empirical studies which provide concepts or theoretical bases to problem solutions and survey type studies that focused on consolidating the body of research related to our research scope aspects [21,[55][56][57][58][59]61,150]. Whilst survey type studies are important, enable knowledge consolidation and identify areas of future research directions, several of the selected primary studies were empirical and provided practical solutions to a range of challenges related to cyber resilience and support for DFIR using innovative techniques.…”

“…The paper focused on the IoT network model and related modelling challenges from the interconnections' perspective and briefly discussed the concept of interdependent infrastructure resilience. Another recent study investigated the autonomy, integration and level of intelligence in emerging applications related to CPSs across a range of application domains in smart cities [59] including big data challenges and data and communication security. Further, the authors explored the intelligence and interconnectivity of systems into a shared environment from a simulation perspective.…”

A Three-Level Modelling Approach for Asynchronous Speed Scaling in High-Performance Data Centres

“…Commonly, there is a trade-off [15] between EE and QoS: the more EE the system is, the more QoS is degraded, and vice versa. Management in view of the aforementioned trade-off may be done from various perspective including speed scaling [5,6,21], scheduling [23,28], sleep mode management [19] and even air conditioning [20], where the latter is motivated by a multiplicative effect of air conditioning on power usage efficiency [25]. Unlike the above-mentioned stateof-the-art approaches, in the present paper we study the effect of a less-invasive speed scaling technique on the stationary average energy consumption of the simultaneous service multi-server system performance.…”

Study on Roof‐Coal Caving Characteristics with Complicated Structure by Fully Mechanized Caving Mining

“…Although this technique has the mentioned advantages, it also has obvious shortcoming of caving problem [20][21][22], which not only directly affects the difficulty of roof caving and recovery but also determines the applicability of this technology in certain geological conditions and provides key parameters for this technology and then effectively improves the roof-coal recovery [23][24][25][26].…”