Industrial internet of things: Recent advances, enabling technologies and open challenges

Khan, Wazir Zada; Rehman, Muhammad Habib ur; Zangoti, Hussein Mohammed; Afzal, Muhammad Khalil; Armi, Nasrullah; Salah, Khaled

doi:10.1016/j.compeleceng.2019.106522

Cited by 360 publications

(155 citation statements)

References 20 publications

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“…In another research paper [ 19 ], the authors concluded based on conducted researches that the success of IIoT can be hindered for different reasons such as challenging collaboration between various heterogeneous IIoT systems, efficient data management, large, solid, and flexible data technologies, IIoT protocols, operating systems, reliable IIoT systems but also the coexistence of wireless technologies.…”

Section: Related Workmentioning

confidence: 99%

“…A definition of the IIoT is provided in [ 19 ], where IIoT is defined as “the network of intelligent and highly connected industrial components that are deployed to achieve high production rate with reduced operational costs through real-time monitoring, efficient management and controlling of industrial processes, assets and operational time”.…”

Section: Introductionmentioning

confidence: 99%

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A Software Architecture for the Industrial Internet of Things—A Conceptual Model

Ungurean

Gaitan

2020

Sensors

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The Internet of Things (IoT) is an emerging concept that has revolutionized the use of new technologies in everyday life. The economic impact of IoT becoming very important, and it began to be used in the industrial environment under the name of the Industrial Internet of Things (IIoT) concept, which is a sub-domain of IoT. The IIoT changes the way industrial processes are controlled and monitored, increasing operating efficiency. This article proposes a software architecture for IIoT that has a low degree of abstraction compared to the reference architectures presented in the literature. The architecture is organized on four-layer and it integrates the latest concepts related to fog and edge computing. These concepts are activated through the use of fog/edge/gateway nodes, where the processing of data acquired from things is performed and it is the place where things interact with each other in the virtual environment. The main contributions of this paper are the proposal and description of a complete IIoT software architecture, the use of a unified address space, and the use of the computing platform based on SoC (System on Chip) with specialized co-processors in order to be able to execute in real-time certain time-critical operations specific to the industrial environment.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Software Architecture for the Industrial Internet of Things—A Conceptual Model

Ungurean

Gaitan

2020

Sensors

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“…An IoT device is always connected through a communication network, ranging from short range networks (e.g., Bluetooth, Zigbee, near-field communication (NFC)), to medium range networks (e.g., Wi-Fi, Digi Mesh), to large range networks (e.g., LoRaWan, cellular, WiMax). Today, IoT devices are usually expected to collect sensor data, communicate with each other, and make decisions without human intervention [4][5][6][7]. Some examples of IoT devices include smart traffic lights, smart parking meters, smart home meters, smartphones, and wearable devices [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities

Cao

2020

IJGI

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The proliferation of Internet of Things (IoT) systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices to sense themselves and their surroundings on multiple spatio-temporal scales, interact with each other across a vast geographical area, and perform automated analytical tasks everywhere and anytime. Currently, most of the geospatial applications of IoMT systems are developed for abnormal detection and control monitoring. However, it is expected that, in the near future, optimization and prediction tasks will have a larger impact on the way citizens interact with smart cities. This paper examines the state of the art of IoMT systems and discusses their crucial role in supporting anticipatory learning. The maximum potential of IoMT systems in future smart cities can be fully exploited in terms of proactive decision making and decision delivery via an anticipatory action/feedback loop. We also examine the challenges and opportunities of anticipatory learning for IoMT systems in contrast to GIS. The holistic overview provided in this paper highlights the guidelines and directions for future research on this emerging topic.

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“…Wireless sensor networks (WSNs) with mobile sink have been widely considered as a promising solution to replace people for discovering and collecting the sensing data within a high dangerous region [1]- [5]. The mobile sink can be an intelligent moving robot or unmanned aircraft that enters into the sensing area for collecting the sensing data.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sink

Chang

Jeng

Sheu

et al. 2020

Preprint

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Wireless sensor networks with mobile sink provide a mobile device to move into the sensing area for collecting the sensing data. It increases the flexibility and convenience of data gathering in such systems. Taking into account the energy consumption of mobile sink, the moving distance of mobile sink must be reduced efficiently. Hence, it is important and necessary to develop an efficient path planning scheme for the mobile sink in large-scale wireless sensor networks systems. According to the greedy-based algorithms, we adopt an angle bisector concept to create the moving path for the mobile sink. In this paper, a novel efficient data collection path planning scheme with an inner center approach is proposed to reduce the moving distance and to prolong the lifetime of mobile sink in wireless sensor networks. The relationship among moving path, moving distance, and number of sensor nodes are analyzed and discussed. Considering the communication range limitation of sensor nodes and the sensing area with obstacles, the proposed scheme makes an adaptive decision for creating the moving path of mobile sink. Simulation results demonstrate that the reasonable moving path planning can be achieved and the moving distance can be reduced for a mobile sink in wireless sensor networks.

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Industrial internet of things: Recent advances, enabling technologies and open challenges

Cited by 360 publications

References 20 publications

A Software Architecture for the Industrial Internet of Things—A Conceptual Model

A Software Architecture for the Industrial Internet of Things—A Conceptual Model

A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sink

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