Modelling Industrial IoT System Complexity

Freund, Lucas; Al-Majeed, Salah

doi:10.1109/3ict51146.2020.9311942

Cited by 6 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That is, no least upper bound exists. (2) There exist more than one meets between a, b in Set {a, b}. That is, no greatest upper bound exists.…”

Section: Theory: N:2-latticementioning

confidence: 99%

“…Note that cardinality implies the number of the main actor, and that capacity implies the numbers of other actors that can get involved in the interaction. For example, A 1 1 implies that one Ambulance can hold one Patient, and A 2 1,1 implies that each of the two Ambulances can hold one Patient for each Ambulance.…”

Section: Step 3: Abstract Behaviorsmentioning

confidence: 99%

“…However, since the systems consist of hundreds, thousands, or even tens of thousands of Smart IoTs, interacting with each other with communication and control while moving in some geographically distributed area, autonomously or heteronomously, with some critical missions, there are pressing needs to handle the size and complexity of the systems [2]. Particularly, the abstraction methods that handle the exponential growth problem of system states caused by interactions among IoTs in the systems, known as a state-explosion problem [3], must be presented [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS

Song

Karagiannis

Lee

2022

Sensors

View full text Add to dashboard Cite

This paper presents a new modeling method to abstract the collective behavior of Smart IoT Systems in CPS, based on process algebra and a lattice structure. In general, process algebra is known to be one of the best formal methods to model IoTs, since each IoT can be represented as a process; a lattice can also be considered one of the best mathematical structures to abstract the collective behavior of IoTs since it has the hierarchical structure to represent multi-dimensional aspects of the interactions of IoTs. The dual approach using two mathematical structures is very challenging since the process algebra have to provide an expressive power to describe the smart behavior of IoTs, and the lattice has to provide an operational capability to handle the state-explosion problem generated from the interactions of IoTs. For these purposes, this paper presents a process algebra, called dTP-Calculus, which represents the smart behavior of IoTs with non-deterministic choice operation based on probability, and a lattice, called n:2-Lattice, which has special join and meet operations to handle the state explosion problem. The main advantage of the method is that the lattice can represent all the possible behavior of the IoT systems, and the patterns of behavior can be elaborated by finding the traces of the behavior in the lattice. Another main advantage is that the new notion of equivalences can be defined within n:2-Lattice, which can be used to solve the classical problem of exponential and non-deterministic complexity in the equivalences of Norm Chomsky and Robin Milner by abstracting them into polynomial and static complexity in the lattice. In order to prove the concept of the method, two tools are developed based on the ADOxx Meta-Modeling Platform: SAVE for the dTP-Calculus and PRISM for the n:2-Lattice. The method and tools can be considered one of the most challenging research topics in the area of modeling to represent the collective behavior of Smart IoT Systems.

show abstract

“…That is, no least upper bound exists. (2) There exist more than one meets between a, b in Set {a, b}. That is, no greatest upper bound exists.…”

Section: Theory: N:2-latticementioning

confidence: 99%

Section: Step 3: Abstract Behaviorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS

Song

Karagiannis

Lee

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Interested readers can access the complete source code of the developed extension publicly available on GitHub repository. 1 The main contributions of this paper can be summarized as follows:…”

Section: -10mentioning

confidence: 99%

“…The complexity of industrial IoT systems is deemed to increase due to the growing need for data acquisition, processing, and storage techniques. Moreover, the rapid penetration of advanced machine-to-machine communications in cyber-physical systems triggers even more complexity in IoT production systems [1]. The design complexity of such systems has to consider different layers, including the behaviour of the building components, their inter-connectivity, not to mention the message heterogeneity [2,3].…”

Section: Introductionmentioning

confidence: 99%

Towards a modeling and analysis environment for industrial IoT systems

Ihirwe,

Di Ruscio,

Mazzini

et al. 2021

Preprint

View full text Add to dashboard Cite

The development of Industrial Internet of Things systems (IIoT) requires tools robust enough to cope with the complexity and heterogeneity of such systems, which are supposed to work in safety-critical conditions. The availability of methodologies to support early analysis, verification, and validation is still an open issue in the research community. The early real-time schedulability analysis can help quantify to what extent the desired system's timing performance can eventually be achieved. In this paper, we present CHESSIoT, a model-driven environment to support the design and analysis of industrial IoT systems. CHESSIoT follows a multi-view, component-based modelling approach with a comprehensive way to perform event-based modelling on system components for code generation purposes employing an intermediate ThingML model. To showcase the capability of the extension, we have designed and analysed an Industrial real-time safety use case.

show abstract

A complexity assessment framework with structure entropy for a cloud‐edge collaborative manufacturing system

Shi

Sun

et al. 2023

IET Collab Intel Manufact

View full text Add to dashboard Cite

The Industrial Internet of Things (IIoT), along with 5G and beyond networks, is driving a new era of revolution in intelligent manufacturing. However, the integration of more heterogeneous entities and intricate communication protocols complicates the enhanced manufacturing system, posing challenges for quantitatively assessing its complexity. To tackle this issue, a complexity assessment framework for the IIoT-enabled collaborative manufacturing system is proposed by combining the complex network and information entropy theory. Firstly, industrial entities in the physical space are mapped into a two-tier complex network taking into account the weights of various access communications. Secondly, an importance-aware structure entropy is introduced to capture the complexity of industrial networks from the communication perspective in the system. The experiments conducted on various network topological structures validate the proposed method and provide guidance for system design.

show abstract

Modelling Industrial IoT System Complexity

Cited by 6 publications

References 9 publications

Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS

Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS

Towards a modeling and analysis environment for industrial IoT systems

A complexity assessment framework with structure entropy for a cloud‐edge collaborative manufacturing system

Contact Info

Product

Resources

About