James Brusey

Requirements on unique identifiers for managing product lifecycle information: comparison of alternative approaches

Managing product information for product items during their whole lifetime is challenging, especially during their usage and end-of-life phases. The main difficulty is to maintain a communication link between the product item and its associated information as the product item moves over organizational borders and between different users. As network access will typically not be continuous during the whole product-item lifecycle, it is necessary to embed at least a globally unique product identifier (GUPI) that makes it possible to identify the product item anytime during its lifecycle. A GUPI also has to provide a linking mechanism to product information that may be stored in backend systems of different organizations. GUPIs are thereby a cornerstone for enabling the Internet of Things, where ‘intelligent products’ can communicate over the Internet. In the current paper, we analyse and compare the three main currently known approaches for achieving such functionality, i.e. the EPC Network, DIALOG and WWAI.

Implementing Industrial Multi-agent Systems Using JACK TM

JACKTM is an implementation of the Belief/Desire/Intention model of rational agency with extensions to support the design and execution of agent systems where team structures, real-time control, repeatability and linkage with legacy code are critical. This chapter presents the JACKTM multi-agent systems platform. The chapter begins with a discussion of agent programming concepts as they relate to JACKTM, and then presents experiences from the development of two industrial applications that made use of JACKTM (a meteorological alerting environment and a responsive manufacturing set-up).

Edge Mining the Internet of Things

This paper examines the benefits of edge mining -data mining that takes place on the wireless, battery-powered, and smart sensing devices that sit at the edge points of the Internet of Things. Through local data reduction and transformation, edge mining can quantifiably reduce the number of packets that must be sent, reducing energy usage, and remote storage requirements. In addition, edge mining has the potential to reduce the risk in personal privacy through embedding of information requirements at the sensing point, limiting inappropriate use. The benefits of edge mining are examined with respect to three specific algorithms: linear Spanish inquisition protocol (L-SIP), ClassAct, and bare necessities (BN), which are all instantiations of general SIP. In general, the benefits provided by edge mining are related to the predictability of data streams and availability of precise information requirements; results show that L-SIP typically reduces packet transmission by around 95% (20-fold), BN reduces packet transmission by 99.98% (5000-fold), and ClassAct reduces packet transmission by 99.6% (250-fold). Although energy reduction is not as radical because of other overheads, minimization of these overheads can lead up to a 10-fold battery life extension for L-SIP, for example. These results demonstrate the importance of edge mining to the feasibility of many IoT applications.

Effective RFID-based object tracking for manufacturing

Automated identification and, in particular, radio frequency identification (RFID) promises to assist with the automation of mass customized production processes by simplifying the retrieval, tracking and usage of highly specialized components. RFID has long been used to gather a history or trace of object movements, but its use as an integral part of the automated control process is yet to be fully exploited. Such (automated) use places stringent demands on the quality of the sensor data collected and the method used to interpret that data. In particular, this paper focuses on the issue of correctly identifying, tracking and dealing with aggregated objects in customized production with the use of RFID. In particular, this work presents approaches for making best use of RFID data in this context. The presented approach is evaluated in the context of a laboratory manufacturing system that produces customized gift boxes.

Wireless Sensor Networks: Deployments and Design Frameworks

The twentieth century ended with the vision of smart dust: a network of wirelessly connected devices whose size would match that of a dust particle, each one a se- containedpackageequippedwithsensing,computation,communication,andpower. Smart dust held the promise to bridge the physical and digital worlds in the most unobtrusive manner, blending together realms that were previously considered well separated. Applications involved scattering hundreds, or even thousands, of smart dust devices to monitor various environmental quantities in scenarios ranging from habitat monitoring to disaster management. The devices were envisioned to se- organize to accomplish their task in the most ef?cient way. As such, smart dust would become a powerful tool, assisting the daily activities of scientists and en- neers in a wide range of disparate disciplines. Wireless sensor networks (WSNs), as we know them today, are the most no- worthy attempt at implementing the smart dust vision. In the last decade, this ?eld has seen a fast-growing investment from both academia and industry. Signi?cant ?nancial resources and manpower have gone into making the smart dust vision a reality through WSNs. Yet, we still cannot claim complete success. At present, only specialist computerscientists or computerengineershave the necessary background to walk the road from conception to a ?nal, deployed, and running WSN system.

GLOBALLY UNIQUE PRODUCT IDENTIFIERS – REQUIREMENTS AND SOLUTIONS TO PRODUCT LIFECYCLE MANAGEMENT

Abstract Managing product information for product items during their whole lifetime is challenging, especially during their usage and end-of-life phases. A major challenge is how to keep a link between the product item and its associated information, which may be stored in backend systems of different organisations. In this paper, we analyse and compare three approaches for addressing this task, i.e. the EPC Network, DIALOG and WWAI.

Postural activity monitoring for increasing safety in bomb disposal missions

In enclosed suits, such as those worn by explosive ordnance disposal (EOD) experts, evaporative cooling through perspiration is less effective and, particularly in hot environments, uncompensable heat stress (UHS) may occur. Although some suits have cooling systems, their effectiveness during missions is dependent on the operatives posture. In order to properly assess thermal state, temperature-based assessment systems need to take posture into account. This paper builds on previous work for instrumenting EOD suits with regard to temperature monitoring and proposes to also monitor operative posture with MEMS accelerometers. Posture is a key factor in predicting how body temperature will change and is therefore important in providing local or remote warning of the onset of UHS. In this work, the C4.5 decision tree algorithm is used to produce an on-line classifier that can differentiate between nine key postures from current acceleration readings. Additional features that summarize how acceleration is changing over time are used to improve average classification accuracy to around 97.2%. Without such temporal feature extraction, dynamic postures are difficult to classify accurately. Experimental results show that training over a variety of subjects, and in particular, mixing gender, improves results on unseen subjects. The main advantages of the on-line posture classification system described here are that it is accurate, does not require integration of acceleration over time, and is computationally lightweight, allowing it to be easily supported on wearable microprocessors.

The Practical Feasibility of Using RFID in a Metal Environment

Passive radio frequency identification (RFID) has revolutionized the way in which products are identified. This paper considers the effect of metals on the performance of RFID at ultra high frequency (UHF). The paper establishes read patterns in space, highlighting the interference of RF waves due to three different metals, one ferrous and the other two non ferrous, when placed behind a transponder. The effect of thickness of the metal plate is also examined. Different metals have been found to have different interference effects although there are some similarities in their read patterns related to their material properties. Also experiments have been carried out to identify and establish various methods of improving this performance. Finally, differences between performance-measuring parameters, namely attenuating transmitted power and calculating read rate at a fixed attenuation are established and possible reasons of these observations are presented.

Techniques for Obtaining Robust, Real-Time, Colour-Based Vision for Robotics

An early stage in image understanding using colour involves recognizing the colour of target objects by looking at individual pixels. However, even when, to the human eye, the colours in the image are distinct, it is a challenge for machine vision to reliably recognize the whole object from colour alone, due to variations in lighting and other environmental issues. In this paper, we investigate the use of decision trees as a basis for recognizing colour. We also investigate the use of colour space transforms as a way of eliminating variations due to lighting.

Leveraging Knowledge From Physiological Data: On-Body Heat Stress Risk Prediction With Sensor Networks

The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1 ± 2.9% and 94.4 ± 2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.