Industrial Internet of Things

doi:10.7324/ijasre.2017.32538

Cited by 13 publications

(7 citation statements)

References 4 publications

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“…The IIoT paradigm promises to increase the visibility and availability of lifecycle data (Jeschke et al, 2017). Via IIoT, real-time data of the whole lifecycle can be gathered and analyzed, to improve maintenance and service decisions.…”

Section: Providing Predictive Maintenance Service and Intelligent Spamentioning

confidence: 99%

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

Ren

Zhang

Liu

et al. 2019

Journal of Cleaner Production

376

189

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Section: Providing Predictive Maintenance Service and Intelligent Spamentioning

confidence: 99%

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

Ren

Zhang

Liu

et al. 2019

Journal of Cleaner Production

376

189

View full text Add to dashboard Cite

“…We claim that our proposal is useful for supporting engineering activities potentially for all IoT application domains, including the field of Industrial Internet of Things (IIoT) systems [31]. As identified by Koziolek and colleagues [32], distributed control systems are currently evolving towards IIoT.…”

Section: Introductionmentioning

confidence: 92%

“…By supporting answering these questions, IoTDraw aids modelers to make more successful architectural decisions and to verify their impact on the IoT application behavior/performance. We claim that our proposal is useful for supporting engineering activities potentially for all IoT application domains, including the field of Industrial Internet of Things (IIoT) systems [31]. As identified by Koziolek and colleagues [32], distributed control systems are currently evolving towards IIoT.…”

Section: Introductionmentioning

confidence: 92%

Towards the adoption of OMG standards in the development of SOA-based IoT systems

Costa

Pires

Delicato

2020

Journal of Systems and Software

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A common feature of the Internet of Things (IoT) is the high heterogeneity, regarding network protocols, data formats, hardware and software platforms. Aiming to deal with such a degree of heterogeneity, several frameworks have applied the Model-Driven Development (MDD) to build IoT applications. On the software architecture viewpoint, the literature has shown that the Service-Oriented Architecture (SOA) is a promising style to address the interoperability of entities composing these solutions. Some features of IoT make it challenging to analyze the impact of design decisions on the SOAbased IoT applications behavior. Thus, it is a key requirement to simulate the model to verify whether the system performs as expected before its implementation. Although the literature has identified that the SOA style is suitable for addressing the interoperability, existing modelling languages do not consider SOA elements as first-class citizens when designing IoT applications. Furthermore, although existing MDD frameworks provide modeling languages comprising well-defined syntax, they lack execution semantics, thus, are not suitable for model execution and analysis. This work aims at addressing these issues by introducing IoTDraw. The framework provides a fully OMG-compliant executable modeling language for SOA-based IoT systems; thus, its specifications can be implemented by any tool implementing OMG standards. Keywords-internet of things, application, model-driven development, service-oriented architecture given rise to the recent paradigm known as Sensor-Cloud or Cloud of Things, which aims to make the most of both technologies. IoT-generated data that requires a lot of processing or long-term storage can be sent to the cloud, which in turn extends its service portfolio to encompass sensing and actuation capabilities. Cloud platforms follow a service-based resource provisioning model. Cloud platforms providing infrastructure for IoT systems naturally adopt the services approach (e.g., [17], [18]). In a recent survey that investigated middleware technologies for Cloud of Things [19], the authors have identified that SOA is the main architectural style in CoT-based Middleware, due to is capability of promoting interoperability and reusability in IoT systems. 1.1 Issues in designing MDD & SOA-based IoT Systems Any DSL must provide a coherent set of concepts related to the domain of interest [8]. That is, the DSL must be expressive, aiming to allow the precise identification of multiple aspects regarding the application domain. Otherwise, if the DSL lacks essential elements of the underlying domain, the models may be at least ambiguous, or the modelers may not be able to represent all components of the applications. Although the literature has identified the suitability of the SOA style for addressing the interoperability in the IoT domain, to the best of our knowledge, there is no DSL tailored for the modeling of SOA-based IoT applications. SOA is a proven architectural pattern, with a well-known set of concepts. Existing DSLs ...

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“…The author believes that the lightweight communication techniques can solve the problems of a very complex Industrial IoT (IIoT) system [17], [18]. The proposed lightweight communication techniques are in fact the communication patterns of point-to-point data transfers leveraging the programming constructs of Java used in similar situations, e.g., Java Remote Method Invocation (RMI) [33].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Coordination Patterns for Applications of the Internet of Things

Mufti

2020

Applied Computer Systems

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Applications of the Internet of Things (IoT) are famously known for connecting devices via the internet. The main purpose of IoT systems (wireless or wired) is to connect devices together for data collection, buffering and data gateway. The collected large size of data is often captured from remote sources for automatic data analytics or for direct decision making by its users. This paper applies the programming pattern for Big Data in IoT systems that makes use of lightweight Java methods, introduced in the recently published work on ClientNet Distributed Cluster. Considering Big Data in IoT systems means the sensing of data from different resources, the network of IoT devices collaborating in data collection and processing; and the gateways servers where the resulting big data is supposed to be directed or further processed. This mainly involves resolving the issues of Big Data, i.e., the size and the network transfer speed along with many other issues of coordination and concurrency. The computer network that connects IoT may further include techniques such as Fog and Edge computing that resolve much of the network issues. This paper provides solutions to these problems that occur in wireless and wired systems. The talk is about the ClientNet programming model and its application in IoT systems for orchestration, such as coordination, data communication, device identification and synchronization between the gateway servers and devices. These devices include sensors attached with appliances (e.g., home automations, supply chain systems, light and heavy machines, vehicles, power grids etc.) or buildings, bridges and computers running data processing applications. As described in earlier papers, the introduced ClientNet techniques prevent from big data transfers and streaming that occupy more resources (hardware and bandwidth) and time. The idea is motivated by Big Data problems that make it difficult to collect it from different resources through small devices and then redirecting it. The proposed programming model of ClientNet Distributed Cluster stores Big Data on the nearest server coordinated by the nearest coordinator. The gateways and the systems that run analytics programs communicate by running programs from other computers when it is essentially required. This makes it possible to let Big Data rarely move across a communication network and allow only the source code to move around the network. The given programming model greatly simplifies data communication overheads, communication patterns among devices, networks and servers.

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Industrial Internet of Things

Cited by 13 publications

References 4 publications

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

Towards the adoption of OMG standards in the development of SOA-based IoT systems

Lightweight Coordination Patterns for Applications of the Internet of Things

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