CPS/IoT Ecosystem: A Platform for Research and Education

Isakovic, Haris; Ratasich, Denise; Hirsch, Christian; Platzer, Michael; Wally, Bernhard; Rausch, Thomas; Ničković, Dejan; Krenn, Willibald; Kappel, Gerti; Dustdar, Schahram; Grosu, Radu

doi:10.1007/978-3-030-23703-5_12

Cited by 16 publications

(4 citation statements)

References 6 publications

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“…In general, nearly 60% of all mobile devices use ARM chips ( Yi et al, 2016 ). Therefore, Raspberry Pis can be considered as representative when building a fog landscape ( Isakovic et al, 2019 ). Fog nodes and fog cells are deployed on Raspberry Pi 3b+ units (Quadcore 64-bit ARM, 1 GB of RAM), which run with the Hypriot operating system.…”

Section: Discussionmentioning

confidence: 99%

FogFrame: a framework for IoT application execution in the fog

Skarlat¹,

Schulte²

2021

PeerJ Computer Science

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Recently, a multitude of conceptual architectures and theoretical foundations for fog computing have been proposed. Despite this, there is still a lack of concrete frameworks to setup real-world fog landscapes. In this work, we design and implement the fog computing framework FogFrame—a system able to manage and monitor edge and cloud resources in fog landscapes and to execute Internet of Things (IoT) applications. FogFrame provides communication and interaction as well as application management within a fog landscape, namely, decentralized service placement, deployment and execution. For service placement, we formalize a system model, define an objective function and constraints, and solve the problem implementing a greedy algorithm and a genetic algorithm. The framework is evaluated with regard to Quality of Service parameters of IoT applications and the utilization of fog resources using a real-world operational testbed. The evaluation shows that the service placement is adapted according to the demand and the available resources in the fog landscape. The greedy placement leads to the maximum utilization of edge devices keeping at the edge as many services as possible, while the placement based on the genetic algorithm keeps devices from overloads by balancing between the cloud and edge. When comparing edge and cloud deployment, the service deployment time at the edge takes 14% of the deployment time in the cloud. If fog resources are utilized at maximum capacity, and a new application request arrives with the need of certain sensor equipment, service deployment becomes impossible, and the application needs to be delegated to other fog resources. The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.

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Section: Discussionmentioning

confidence: 99%

FogFrame: a framework for IoT application execution in the fog

Skarlat¹,

Schulte²

2021

PeerJ Computer Science

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“…Isakovic et al [97] suggest that custom hardware can reduce the costs of CPS platforms for research and education. Their approach, however, does not offer an open-source solution that is independent of specific hardware and software systems.…”

Section: Flexibility For Icps Experimentationmentioning

confidence: 99%

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

2023

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Intelligent vehicular cyber-physical systems (ICPSs) increase the reliability, efficiency and adaptability of urban mobility systems. Notably, ICPSs enable autonomous transportation in smart cities, exemplified by the emerging fields of self-driving cars and advanced air mobility. Nonetheless, the deployment of ICPSs raises legitimate concerns surrounding safety assurance, cybersecurity threats, communication reliability, and data management. Addressing these issues often necessitates specialised platforms to cater to the heterogeneity and complexity of ICPSs. To address this challenge, this paper presents a comprehensive CPS to explore, develop and test ICPSs and intelligent vehicular algorithms. A customisable embedded system is realised using a field programmable gate array, which is connected to a supervisory computer to enable networked operations and support advanced multi-agent algorithms. The platform remains compatible with multiple vehicular sensors, communication protocols and human–machine interfaces, essential for a vehicle to perceive its surroundings, communicate with collaborative systems, and interact with its occupants. The proposed CPS thereby offers a practical resource to advance ICPS development, comprehension, and experimentation in both educational and research settings. By bridging the gap between theory and practice, this tool empowers users to overcome the complexities of ICPSs and contribute to the emerging fields of autonomous transportation and intelligent vehicular systems.

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“…Moreover, the authors of [9] suggest artificial intelligence for optimizing park search, but barely specify the technical details of implementation, such as specific protocols or sensor types. Likewise, the work proposed in [34], shows the use of Bluetooth Low Energy (BLE) as a protocol for connecting sensors and gateways. Bluetooth is a wireless protocol that supports connection between end-devices.…”

Section: Smart Parking Solutionsmentioning

confidence: 99%

Smart Parking: A Literature Review from the Technological Perspective

et al. 2019

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The development and high growth of the Internet of Things (IoT) have improved quality of life and strengthened different areas in society. Many cities worldwide are looking forward to becoming smart. One of the most popular use cases in smart cities is the implementation of smart parking solutions, as they allow people to optimize time, reduce fuel consumption, and carbon dioxide emissions. Smart parking solutions have a defined architecture with particular components (sensors, communication protocols, and software solutions). Although there are only three components that compose a smart parking solution, it is important to mention that each component has many types that can be used in the deployment of these solutions. This paper identifies the most used types of every component and highlights usage trends in the established analysis period. It provides a complementary perspective and represents a very useful source of information. The scientific community could use this information to decide regarding the selection of types of components to implement a smart parking solution. For this purpose, herein we review several works related to smart parking solutions deployment. To achieve this goal, a semi-cyclic adaptation of the action research methodology combined with a systematic review is used to select papers related to the subject of study. The most relevant papers were reviewed to identify subcategories for each component; these classifications are presented in tables to mark the relevance of each paper accordingly. Trends of usage in terms of sensors, protocols and software solutions are analyzed and discussed in every section. In addition to the trends of usage, this paper determines a guide of complementary features from the type of components that should be considered when implementing a smart parking solution.

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CPS/IoT Ecosystem: A Platform for Research and Education

Cited by 16 publications

References 6 publications

FogFrame: a framework for IoT application execution in the fog

FogFrame: a framework for IoT application execution in the fog

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

Smart Parking: A Literature Review from the Technological Perspective

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