Editorial “Industrial IoT as IT and OT Convergence: Challenges and Opportunities”

Giannelli, Carlo; Picone, Marco

doi:10.3390/iot3010014

Cited by 13 publications

(3 citation statements)

References 7 publications

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“…However, the convergence of runtime and development time and the need for smarter OT software systems-which we discussed in Section 1-results in the increasing use and adoption of IT in the OT domain. This trend is commonly denoted as the convergence of IT and OT and is accompanied by many open challenges [65][66][67].…”

Section: Challengesmentioning

confidence: 99%

Towards DevOps for Cyber-Physical Systems (CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric Smart CPS Facilitated by Digital Twins

Dobaj,

Riel,

Macher

et al. 2023

Machines

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The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance.

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Section: Challengesmentioning

confidence: 99%

Towards DevOps for Cyber-Physical Systems (CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric Smart CPS Facilitated by Digital Twins

Dobaj,

Riel,

Macher

et al. 2023

Machines

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show abstract

“…Although recent advancements in the miniaturisation of devices with higher computational capabilities and ultra-low power communication technologies have enabled the vast deployment of sensors and actuators everywhere, such an evolution calls for fundamental changes in hardware design, software, network architecture, data analytics, data storage, and power sources [33]. This massive amount of information has the power to revolutionise how applications and services are designed and deployed allowing them to work more efficiently and profitably [34]. Current network architectures such as Cloud computing are not adapted to provide an acceptable QoS to the large number of tiny devices that compose IoT.…”

Section: Introductionmentioning

confidence: 99%

A threshold‐based sorting algorithm for dense wireless sensor systems and communication networks

Moghaddam

2023

IET Wireless Sensor Systems

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Nowadays, time‐varying and high‐density data of wireless sensor systems and communication networks compel us to propose and realise low‐complexity and time‐efficient algorithms for searching, clustering, and sorting. A novel threshold‐based sorting algorithm applicable to dense and ultra‐dense networks is proposed in this study. Instead of sorting whole data in a large data set and selecting a certain number of them, the proposed algorithm sorts a specific number of elements that are larger or smaller than a threshold level or located between two threshold values. First, based on the mean value and standard deviation of the data, a theoretical analysis to find the exact and approximate thresholds, respectively for known (Gaussian, uniform, Rayleigh, and negative exponential) and unknown probability distributions is presented. Then, an algorithm to sort a predefined number of data is realised. Finally, the effectiveness of the proposed algorithm is shown in the view of the time complexity order, the running time, and the similarity measure. To do this, theoretical and numerical analyses are used to show the superiority of the proposed algorithm in known and unknown distributions to the well‐known conventional and gradual conventional versions of Merge, Quick, and K‐S mean‐based sorting algorithms.

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“…Wearable computing, smart metering, smart home/city, connected vehicles, and large-scale wireless sensor networks will all benefit from the fast improvement of computing systems, which will enable the sensing, capturing, aggregation, and processing of streaming data from myriad of connected equipments. These smart things, which are connected to the internet, are known as the Internet of Things (IoT) [1].…”

Section: Introductionmentioning

confidence: 99%

DLBRT: Dynamic Load-Balance Real-Time Scheduling Model in Fog-Cloud Colony

احمد¹

2022

النشرة المعلوماتیة فی الحاسبات والمعلومات

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In recent years, the massive growing of the usage of Internet of Things (IoT) applications expose different challenges in centralized cloud computing paradigm such as; network failure and inadmissible latency for real-time services. The fog layer is added to address these challenges. The nodes in fog computing layer are similar to cloud node, except that it is nearby the IoT devices. Fog node is added to supply IoT devices with the desired resources with low delay. Hence, the performance of IoT application is based on the task scheduling strategy in cloud, or fog computing systems. Hence, the scheduling strategy should maximize resource utilization and increase the resources availability. Most of the previous scheduling methods are suffer from centralization, which consequently represents a performance bottleneck and a single point of failure. Also, some of these scheduling methods ignore the urgency type of the services, which consequently will not be suitable for real-time services. This paper proposes a new load balancing model, which has two main features. The first feature is the dynamic allocation quota for the resource allocation. The second feature is decentralization in fog resource management. In another word, DLBRT manages the IoT service requests based on the service urgency level with maintaining the load among the fog node balanced. The dynamic load balancing strategy in DLBRT is based on redistribution of the less-urgent service requests to the lowest fog node load. In another word, the redistribution of the service requests is depending on the urgency of the real-time service and the workload in every fog. Finally, a simulation model is created to evaluate DLBRT in a fog-cloud colony. Also, the proposed scheduling model is examined with four scheduling models, namely; The FCFS, Max-Min, Real-Time Efficient Scheduling (RETS), and Based Autonomic Task Scheduling (PBATS). We demonstrate through thorough simulations that the performance metrics of turnaround time, waiting time, throughput, and task failure test are enhanced by our suggested approach.

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Editorial “Industrial IoT as IT and OT Convergence: Challenges and Opportunities”

Abstract: During the last decade, the advent of the Internet of Things (IoT) and its quick and pervasive evolution have significantly revolutionized the Information Technology ecosystem [...]

Cited by 13 publications

References 7 publications

Towards DevOps for Cyber-Physical Systems (CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric Smart CPS Facilitated by Digital Twins

Towards DevOps for Cyber-Physical Systems (CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric Smart CPS Facilitated by Digital Twins

A threshold‐based sorting algorithm for dense wireless sensor systems and communication networks

DLBRT: Dynamic Load-Balance Real-Time Scheduling Model in Fog-Cloud Colony

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