Traceability applications based on Discovery Services

RFID, in its different forms, but especially following EPCglobal standards, has become a key enabling technology for many applications. An essential component to develop track and trace applications in a complex multi-vendor scenario are the Discovery Services. Although they are already envisaged as part of the EPCglobal network architecture, the functional definition and standardization of the Discovery Service is still at a very early stage. Within the scope of the BRIDGE project, a specification for the interfaces of Discovery Services has been developed, together with a prototype to validate the design and different models to enhance supply-chain control through track and trace applications. © 2008 IEEE.

Security considerations in the design and peering of RFID discovery services

There is growing interest in Discovery Services for locating RFID and supply chain data between companies globally, to obtain product lifecycle information for individual objects. Discovery Services are heralded as a means to find serial-level data from previously unknown parties, however more realistically they provide a means to reduce the communications load on the information services, the network and the requesting client application. Attempts to design a standardised Discovery Service will not succeed unless security is considered in every aspect of the design. In this paper we clearly show that security cannot be bolted-on in the form of access control, although this is also required. The basic communication model of the Discovery Service critically affects who shares what data with whom, and what level of trust is required between the interacting parties. © 2009 IEEE.

Assessment of visibility restriction mechanisms for RFID data Discovery Services

RFID is a technology that enables the automated capture of observations of uniquely identified physical objects as they move through supply chains. Discovery Services provide links to repositories that have traceability information about specific physical objects. Each supply chain party publishes records to a Discovery Service to create such links and also specifies access control policies to restrict who has visibility of link information, since it is commercially sensitive and could reveal inventory levels, flow patterns, trading relationships, etc. The requirement of being able to share information on a need-to-know basis, e.g. within the specific chain of custody of an individual object, poses a particular challenge for authorization and access control, because in many supply chain situations the information owner might not have sufficient knowledge about all the companies who should be authorized to view the information, because the path taken by an individual physical object only emerges over time, rather than being fully pre-determined at the time of manufacture. This led us to consider novel approaches to delegate trust and to control access to information. This paper presents an assessment of visibility restriction mechanisms for Discovery Services capable of handling emergent object paths. We compare three approaches: enumerated access control (EAC), chain-of-communication tokens (CCT), and chain-of-trust assertions (CTA). A cost model was developed to estimate the additional cost of restricting visibility in a baseline traceability system and the estimates were used to compare the approaches and to discuss the trade-offs. © 2012 IEEE.

Discovery of a new 2.3 s isomer in neutron-rich 174Tm

A new program of K-isomer research has been initiated with the 8π spectrometer sited at the ISAC facility of TRIUMF. We discuss in this paper the identification of a new 2.3 s isomer in 174Tm and its implications. © Società Italiana di Fisica / Springer-Verlag 2005.

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art. We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels. We present a method for searching over this space of structures which mirrors the scientific discovery process. The learned structures can often decompose functions into interpretable components and enable long-range extrapolation on time-series datasets. Our structure search method outperforms many widely used kernels and kernel combination methods on a variety of prediction tasks.