A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data

The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication and encryption, that are made necessary by this development. Thus, industrial IT-security is needed. In this work, emulated industrial network data is transformed into a time series and analysed with three different algorithms. The data contains labeled attacks, so the performance can be evaluated. Matrix Profiles perform well with almost no parameterisation needed. Seasonal Autoregressive Integrated Moving Average performs well in the presence of noise, requiring parameterisation effort. Long Short Term Memory-based neural networks perform mediocre while requiring a high training- and parameterisation effort.Comment: Extended version of a publication in the 2018 IEEE International Conference on Data Mining Workshops (ICDMW

Survey on wireless technology trade-offs for the industrial internet of things

Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment

Characteristics of aggregated traffic in LoRaWAN

Abstract. Over the past few years, internet-of-Things (IoT) request a large number of smart devices to communicate and exchange information without direct human assistance. As the number of IoT nodes are increasing rapidly, the massive machine-type communication (mMTC) in 5G enables us to integrate the IoT massive traffic and applications without affecting the traditional services. Low-Power Wide-Area Networks (LPWAN) are emerging commercially and considered as fundamental enablers of IoT, Industrial Internet-of-Things (IIoT), and industrial revolution 4.0 because of their license free frequency bands, long range, low power consumption, and low cost. In recent years, LoRaWAN is appearing as one of the most leading LPWAN technologies. The main contribution of this work is examining the characteristics and modeling the aggregated traffic of a large and dense LoRa Network that is deployed as a monitoring system inside Tellus Innovation Arena, University of Oulu, with the concept of IoT-based digital campus as a Wireless Access IoT service of 5GTN. To understand the traffic behaviour, we analyzed the inter-arrival times of the transmissions for different weeks, days, and hours. The statistical presentation of data reveals that the trend of transmissions is exponential, that shows that most of the transmissions were within the inter-arrival time of less than 10 seconds while few of them have inter-arrival time over 20 seconds. After that, we fitted inter-arrival times into the exponential distribution, which helped us to find the mean inter-arrival time of the 5GTN traffic which is further used for the modeling of aggregated traffic. Finally, we performed the transmission compression from a gateway to the Network Server that will be beneficial to efficiently utilize the resources and bandwidth. The results demonstrate that the proposed aggregation mechanism increases the system goodput

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion alongside the number of attacks types detected. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.Comment: ACM Computing Surveys, 2022, 35 pages, 10 Figures, 8 Table