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peer-reviewed The next industrial revolution is said to be paved by the use of novel Internet of Things (IoT) technology. One important aspect of the modern IoT infrastructures is decentralised communication, often called Peer-to-Peer (P2P). In the context of industrial communication, P2P contributes to resilience and improved stability for industrial components. Current industrial facilities, however, still rely on centralised networking schemes which are considered to be mandatory to comply with security standards. In order to succeed, introduced industrial P2P technology must maintain the current level of protection and also consider possible new threats. The presented work starts with a short analysis of well-established industrial communication infrastructures and how these could benefit from decentralised structures. Subsequently, previously undefined Information Technology (IT) security requirements are derived from the new cloud based decentralised industrial automation model architecture presented in this paper. To meet those requirements, state-of-the-art communication schemes and their open source implementations are presented and assessed for their usability in the context of industrial IoT. Finally, derived building blocks for industrial IoT P2P security are presented which are qualified to comply with the stated industrial IoT security requirements.

peer-reviewed As companies begin to move towards Industry 4.0 and implement Smart Manufacturing techniques, flexible manufacturing comes to the fore. This allows a facility to adapt to changing needs and demands, with less impact on production, when compared to rigid production lines. Traditionally, the introduction of new equipment into a production line is somewhat straightforward, as the equipment is generally static. In recent times however, Autonomous Intelligent Vehicles (AIVs) have become a pivotal component of smart production lines. Given there may be in excess of twenty AIVs working simultaneously, the integration challenge becomes even more complex. This paper identifies several challenges associated with the integration of an AIV into a manufacturing environment controlled by a Manufacturing Execution System (MES). Further to this a discussion of the solutions being implemented currently on a live project is provided.

peer-reviewed Process automation has become a norm within industry with cheap and easily accessible automation technology becoming a standard option available to manufacturing firms. However, without the implementation of flexibility in manufacturing by the employment of intelligent systems, the technology will be limited in application. The development of smart sensing technologies has allowed for modularity and versatility to become familiar terms on a manufacturing floor, notwithstanding, it is still widely recognised that a human employee is the most valuable and flexible asset a company may have. Automation falls short in terms of flexibility due to its lack of independence during operations with high levels of variance, such as varying target position from cycle to cycle. Processes with high levels of variance disallow employment at a satisfactory level of standard or more traditional automation methods due to the lack of ability of current systems to deal with the unexpected. This paper aims to examine the technology used, and that can be potentially used in processes with high levels of variance, specifically, vision systems used in collaboration with an algorithmic comparison to compare an obtained image to an image or 3D model of the target for target recognition/object identification. While there have been copious experiments to employ imaging technology for object identification, some systems do currently occupy the factory floors of manufacturing facilities for recognition. Many of these systems run on RFID, barcode or fiducial marker. These technologies, while operational, require a pre-emptive effort to be made to ensure all products or objects to be recognised have an identifying marker attached before recognition is possible. This substantially limits the flexibility of manufacturing as the versatility of a process line to adapt to different products needs to be a possibility without the necessity of rebranding or retagging each object or product, causing a decelerative rate of production. The first section in this paper identifies the most commonly used methods of object recognition and the necessary modules required for each different algorithmic architecture. The need for particular architectures, depending on object illumination, shape, texture etc., will be addressed in the second section, as well as the differences in the construction of these architectures by use of different combinations of modules. Finally, the third section aims to address the best industrial practice and the opportunities currently being offered by research. It is the aim of the authors to determine where there are gaps to be filled between industry and research. This paper will identify areas of research that need to be examined in order to close the gap between theory and practice in research and industry respectively, to allow industry 4.0 to become a reality across the board for manufacturing

peer-reviewed Within the manufacturing industry, predictive maintenance is a well-established concept, dating back to the 1990‘s [1]. Practice has shown it to have a proven track record of minimising unnecessary machine downtime. The methods of predictive maintenance have varied widely, including visual inspection (i.e. human monitoring), thermal imaging, ultrasonic analysis, vibration analysis, power consumption, acoustic emission, to name a few. As manufacturing technologies have developed, maintenance in general has become a more complex task, presenting many challenges for researchers, engineers and scientists. These challenges have been met through research and development of new technologies and methods of maintenance. Some of these methods currently involve installing intricate sensor systems which are placed on, or in close proximity to the system Practice is now moving towards using remote monitoring systems (RMS) as a possible method to reduce some of these issues. Thisis due to the ability to carry out monitoring without having to install the monitoring system on the structure of the SUT, hence minimising the potential for damage to the sensor systems. This paper aims to describe the importance of predictive maintenance (PdM) over other maintenance methods (e.g. Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019 under test (SUT). Although some of these monitoring methods have been slow to catch on within industry, much of the reason for this can be accredited to the high cost of these sensors along with the high probability of damage to and the replacement of them. reactive, corrective etc.), the importance of PdM for the metal cutting industry (focusing on cutting tool wear), while also discussing some common methods of predictive maintenance monitoring system methods already being utilised within industry. The final method discussed is remote monitoring systems used to monitor transmitted sound, while also identifying how this monitoring system could be integrated within the smart manufacturing environment that is being driven by Industry 4.0.

An envelope-filtered method of enhancement of non-invasive Hemoglobin (Hb) monitoring is described. Results using a four-wavelength two-wavelength Photoplethysmography (PPG) probe gathered from five randomly selected healthy subjects demonstrate that the measurement of Hb coefficient, based on the novel envelope method filtered PPG, has a superior performance on minimization of the motion artefact than that from band-pass filtered PPG data in both stationary and non-stationary scenarios. Hemodynamic pressure variation near the sensor probe is also induced from a vertical height difference (VHD) between the finger sensor probe and the heart level. Results of this study show that the Hb coefficients determined using the proposed filtering method based on the envelope algorithm are also capable of minimization of VHD variations similar to the traditional band-pass filter method.

The work presented here describes a Bump-In-The-Wire (BITW) security solution for the provision of secure communications for a Marine ROV Control Application. The targeted marine application involves controlling a ‘Remotely Operated Vehicle (ROV)’ from a remote control station through standard Internet. A BITW solution is required as communication through the Internet is inherently insecure and open to signal modification or tampering. BITW technology is an implementation approach that places a security mechanism outside and independent of the system that is to be protected, in this case that is a ROV and its remotely located control station. Secure communications between the ROV and the remote control station is necessary to ensure that only authorised persons can issue control commands to the ROV and that no unauthorised individual can understand/sniff the communications between the ROV and control station. The proposed BITW security solution involves an efficient implementation of the AES cryptographic algorithm on a Field Programmable Gate Array (FPGA) platform. The extra delay introduced into the remote control application was well within the allowable time window of 50 ms. Other security mechanisms can also be implemented in the same way depending upon the availability of hardware resources.

Many medical applications set new demands on sensor network designs. They often involve highly variable data rates, multiple receivers and security. Most existing sensor network designs do not adequately support these requirements, focusing instead on aggregating small amounts of data from nodes without security. In this paper, we present a software design for medical sensor networks. This framework provides a set of protocols and services specifically tailored for this application domain. It includes a secure communications model, an interface for periodic collection of sensor data, a dynamic sensor discovery protocol and protocols that monitor and save up to 70% of the energy of a node. The framework is built in TinyOS and a JAVA based user interface is provided to debug the framework and display the measured data. An extensive evaluation of the framework of a 6-node sensor test-bed is presented, measuring scalability and robustness as the number of sensors and the per node data rate are varied. The results show that the proposed framework is a scalable, robust, reliable and secure solution for medical applications.

This paper considers the impact of the provision of security for a Java enabled wireless sensor networking platform. In 2007, Sun Microsystems, Inc. released a Java programmable platform; namely Sun Small Programmable Object Technology (Sun SPOT). Given the more intuitive application development environment, it is envisaged that real applications will emerge at a more rapid pace, and as such the importance of the provision of security remains to the fore. The only provision of security, to-date, is that of an industry standard TLS (Transport Layer Security) implementation at the Application Layer; rooted from an ECC (Elliptic Curve Cryptography) based public-key cryptosystem. This work demonstrates that the employment of TLS results in a reduction of 70% of network lifetime; a significant cost to provide security. Given this cost, alternative methods of securing a Sun SPOT based sensor network are considered, culminating in the specification of a cross-layer, hybrid security architecture.