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Mobile Networks and Applications

, Volume 23, Issue 4, pp 806–808 | Cite as

Editorial: Industrial Internet of Things (I2oT)

  • Leandros Maglaras
  • Lei Shu
  • Athanasios Maglaras
  • Jianmin Jiang
  • Helge Janicke
  • Dimitrios Katsaros
  • Tiago J. Cruz
Article

Editorial:

Industrial Internet of Things (I2oT) consists of a variety of objects (e.g., sensors, RFID tags, actuators, mobile devices, appliances, industrial equipment), which could communicate and cooperate with each other to perform information sensing and automatically control, fulfilling various industrial applications (e.g., intelligent manufacturing, product quality inspection, equipment diagnostics, intelligent logistics). The essence of the I2oT is to provide companies with the opportunity to enhance their computing paraphernalia for use in industry, which in turn will render them more proficient and more profitable. By virtue of transforming embedded computers into intelligent systems, companies can improve the throughput of their processes and workforces, boost their productivity and hence enter new horizons. Numerous world-leading companies have discovered the great potential to benefit from I2oT. The realization of I2oT involves establishing smart connected infrastructures and remotely interconnecting the industrial applications with users by utilizing technologies such as distributed computing, ubiquitous computing and Cloud. However, there are still quite a number of critical issues (e.g., sensor deployment, big data and analysis, information management, information services, query processing, network security, software system) which could strongly influence the successful realization of I2oT.

This special issue solicits high-quality original research papers which focus on the design, development, analysis, standardization and application of I2oT. Some information about these papers follows.

In article “Smart Behavioural Filter for Industrial Internet of Things” by Giovanni Corbò, Chiara Foglietta, Cosimo Palazzo and Stefano Panzieri, a Smart Behavioural Filter (SBF) able to detect and block commands that are anomalous from a logic point of view is presented. The SBF that is a passive firewall and a local reaction module for mitigating risk of cyber attacks, can be the connection point between IDS, firewall and reaction strategies based on Software-Defined Network. The actual implementation of the SBF is a proof-of-concept which demonstrates the feasibility of the proposed architecture.

In article “Frame Conversion Schemes for Cascaded Wired/Wireless Communication Networks of Factory Automation” by Steven Dietrich, Gunther May, Johannes von Hoyningen-Huene, Andreas Mueller and Gerhard Fohler, authors present a general frame model that enables the timing analysis of any specific implementation in Cascaded communication networks. With the use of this model authors were able to represent different existing frame conversion concepts with their properties regarding latency and jitter.

In article “A Low Duty Cycle Efficient MAC Protocol Based on Self-Adaption and Predictive Strategy” by Degan Zhang, Hui Ge, and Yameng Tang, a low duty cycle energy-efficient MAC protocol for WSN is presented. The proposed protocol, titled AP-MAC, can be adaptively updated based on the prediction nodes’ wake-up time. Through extensive simulations, authors showed that the proposed protocol reduces the network energy consumption, improves the reliable transmission of data, and improves the adaptability of the network.

In article “A Queue Model for Reliable Forecasting of Future CPU Consumption” by Hugo Lewi Hammer, Anis Yazidi, Alfred Bratterud, Hårek Haugerud and Boning Feng, a statistical model that replicates the properties of real CPU consumption data is presented. The model outperforms both the HMM CPU model and ARIMA model in forecasting future CPU consumption. The proposed queue model, can forecast the instantaneous CPU consumption at any time point in the future.

In article “Improving Vehicle Localization in a Smart City with Low Cost Sensor Networks and Support Vector Machines” by Belhajem, Yann Ben Maissa and Ahmed Tamtaoui, a novel approach that uses EKF and Support Vector Machines (SVM) in order to estimate vehicle positions is presented. The proposed method, can be used in order to estimate the position of the vehicle when no GPS information is available with high precision.

In the last article “Efficient Spatial Keyword Query Processing in the Internet of Industrial Vehicles” by Yanhong Li, Changyin, Luo Rongbo Zhu, Yuanfang Chen and Huacheng Zeng, a novel air index, titled ESKIV which supports both network space pruning and textual pruning simultaneously, is presented. Based on ESKIV, efficient algorithms that deal with these two types of SKQ respectively are also proposed. Extensive simulations are conducted that demonstrate the efficiency of ESKIV index and the corresponding query processing algorithms.

Notes

Acknowledgements

The guest editors are thankful to our reviewers for their effort in reviewing the manuscripts. We also thank the Editor-in-Chief, Dr. Imrich Chlamtac for his supportive guidance during the entire process.

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Leandros Maglaras
    • 1
  • Lei Shu
    • 2
  • Athanasios Maglaras
    • 3
  • Jianmin Jiang
    • 4
  • Helge Janicke
    • 1
  • Dimitrios Katsaros
    • 5
  • Tiago J. Cruz
    • 6
  1. 1.De Montfort UniversityLeicesterUK
  2. 2.Guangdong University of Petrochemical TechnologyMaoming ShiChina
  3. 3.Technological Educational Institute of ThessalyLarissaGreece
  4. 4.Shenzhen UniversityShenzhen ShiChina
  5. 5.University of ThessalyVolosGreece
  6. 6.University of CoimbraCoimbraPortugal

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