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Artificial Intelligence (AI) : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

All one needs to know about fog computing and related edge computing paradigms: A complete survey

More particularly, calculations are developed and examined to reduce the entire quantity of Wireless access points as well as their locations in almost any given atmosphere while with the throughput needs and the necessity to ensure every place in the area can achieve a minimum of k APs. This paper concentrates on using Wireless for interacting with and localizing the robot. We've carried out thorough studies of Wireless signal propagation qualities both in indoor and outside conditions, which forms the foundation for Wireless AP deployment and communication to be able to augment how human operators communicate with this atmosphere, a mobile automatic platform is developed. Gas and oil refineries could be a harmful atmosphere for various reasons, including heat, toxic gasses, and unpredicted catastrophic failures. When multiple Wireless APs are close together, there's a possible for interference. A graph-coloring heuristic can be used to find out AP funnel allocation. Additionally, Wireless fingerprinting based localization is developed. All of the calculations implemented are examined in real life situations using the robot developed and answers are promising. For example, within the gas and oil industry, during inspection, maintenance, or repair of facilities inside a refinery, people might be uncovered to seriously high temps to have a long time, to toxic gasses including methane and H2S, and also to unpredicted catastrophic failures.

/pdf/internet-of-vehicles-from-intelligent-grid-to-autonomous-pma0tn3ofd.pdf

Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

Sending data to the cloud for analysis was a prominent trend during the past decades, driving cloud computing as a dominant computing paradigm. However, the dramatically increasing number of devices and data traffic in the Internet-of-Things (IoT) era are posing significant burdens on the capacity-limited Internet and uncontrollable service delay. It becomes difficult to meet the delay-sensitive and context-aware service requirements of IoT applications by using cloud computing alone. Facing these challenges, computing paradigms are shifting from the centralized cloud computing to distributed edge computing. Several new computing paradigms, including Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet, have emerged to leverage the distributed resources at network edge to provide timely and context-aware services. By integrating end devices, edge servers, and cloud, they form a hierarchical IoT architecture, i.e., End-Edge-Cloud orchestrated architecture to improve the performance of IoT systems. This article presents a comprehensive survey of these emerging computing paradigms from the perspective of end-edge-cloud orchestration. Specifically, we first introduce and compare the architectures and characteristics of different computing paradigms. Then, a comprehensive survey is presented to discuss state-of-the-art research in terms of computation offloading, caching, security, and privacy. Finally, some potential research directions are envisioned for fostering continuous research efforts.

A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms: Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

Lightweight virtualization (LV) technologies have refashioned the world of software development by introducing flexibility and new ways of managing and distributing software. Edge computing complements today's powerful centralized data centers with a large number of distributed nodes that provide virtualization close to the data source and end users. This emerging paradigm offers ubiquitous processing capabilities on a wide range of heterogeneous hardware characterized by different processing power and energy availability. The scope of this article is to present an in-depth analysis on the requirements of edge computing from the perspective of three selected use cases that are particularly interesting for harnessing the power of the Internet of Things. We discuss and compare the applicability of two LV technologies, containers and unikernels, as platforms for enabling the scalability, security, and manageability required by such pervasive applications that soon may be part of our everyday lives. To inspire further research, we identify open problems and highlight future directions to serve as a road map for both industry and academia.

Consolidate IoT Edge Computing with Lightweight Virtualization

The growing ubiquity and pervasiveness of sensors and smart devices, with the consequent availability of a large amount of "local" and contextualized information, are giving rise to a wealth of "context-aware" applications and services. In this work we consider an opportunistic communication scheme called Floating Content (FC), which was specifically designed for serverless distributed context-aware applications. So far, the performance analysis of FC, and in particular of content lifetime and availability, was based on simplified system models and fluid system analysis. The resulting performance estimates did not account for the effects of propagation characteristics, mobility patterns, and communication protocols, all factors known to significantly affect the performance of opportunistic communication schemes. This article studies the main issues related to the performance of FC service in a realistic office setting through a first experimental evaluation of a mobile app for Android devices. Our experimental results confirm the feasibility of FC service for supporting practical context-aware mobile applications in office settings.

Experimenting with floating content in an office setting

FADES is an edge offloading architecture that empowers us to run compact, single purpose tasks at the edge of the network to support a variety of IoT and cloud services. The design principle behind FADES is to efficiently exploit the resources of constrained edge devices through fine-grained computation offloading. FADES takes advantage of MirageOS unikernels to isolate and embed application logic in concise Xen-bootable images. We have implemented FADES and evaluated the system performance under various hardware and network conditions. Our results show that FADES can effectively strike a balance between running complex applications in the cloud and simple operations at the edge. As a solid step to enable fine-grained edge offloading, our experiments also reveal the limitation of existing IoT hardware and virtualization platforms, which shed light on future research to bring unikernel into IoT domain.

https://dl.acm.org/doi/pdf/10.1145/3094405.3094412

FADES: Fine-Grained Edge Offloading with Unikernels

For road safety, detecting and reacting efficiently to road hazards is crucial and yet challenging due to practical restrictions such as limited data availability, which relies on network support. Moreover, from a system perspective we lack a computational model capable of providing to vehicles reliable and real-time assessment of the road context. As autonomous vehicles become widespread, the safety issues are further aggravated by the gap between cloud, roadside infrastructure and road users in terms of communication latency, software-hardware compatibility and data interoperability. To tackle this, we present ECCO: an orchestration framework that enables edge-cloud collaborative computing for road context assessment. ECCO can create on-demand task execution pipelines spanning multiple, potentially resource-constrained edge-nodes with the smart IoT infrastructure support. Our prototype lays the groundwork to support new services, which can use more efficiently the road infrastructure and deliver safety-critical applications for road users.

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ECCO: Edge-Cloud Chaining and Orchestration Framework for Road Context Assessment

In this paper we make the case for IoT edge offloading, which strives to exploit the resources on edge computing devices by offloading fine-grained computation tasks from the cloud closer to the users and data generators (i.e., IoT devices). The key motive is to enhance performance, security and privacy for IoT services. Our proposal bridges the gap between cloud computing and IoT by applying a divide and conquer approach over the multi-level (cloud, edge and IoT) information pipeline. To validate the design of IoT edge offloading, we developed a unikernel-based prototype and evaluated the system under various hardware and network conditions. Our experimentation has shown promising results and revealed the limitation of existing IoT hardware and virtualization platforms, shedding light on future research of edge computing and IoT.

https://dl.acm.org/doi/pdf/10.1145/3123878.3132009

Vittorio Cozzolino

Papers

Consolidate IoT Edge Computing with Lightweight Virtualization

Experimenting with floating content in an office setting

FADES: Fine-Grained Edge Offloading with Unikernels

ECCO: Edge-Cloud Chaining and Orchestration Framework for Road Context Assessment

Enabling Fine-Grained Edge Offloading for IoT