A framework for distributed managing uncertain data in RFID traceability networks

The ability to track and trace individual items, especially through large-scale and distributed networks, is the key to realizing many important business applications such as supply chain management, asset tracking, and counterfeit detection. Networked RFID (radio frequency identification), which uses the Internet to connect otherwise isolated RFID systems and software, is an emerging technology to support traceability applications. Despite its promising benefits, there remains many challenges to be overcome before these benefits can be realized. One significant challenge centers around dealing with uncertainty of raw RFID data. In this paper, we propose a novel framework to effectively manage the uncertainty of RFID data in large scale traceability networks. The framework consists of a global object tracking model and a local RFID data cleaning model. In particular, we propose a Markov-based model for tracking objects globally and a particle filter based approach for processing noisy, low-level RFID data locally. Our implementation validates the proposed approach and the experimental results show its effectiveness.Jiangang Ma, Quan Z. Sheng, Damith Ranasinghe, Jen Min Chuah and Yanbo W

A new framework for the management of returnable "containers" within open supply networks

International audienceNew logistics models – physical internet, pooling, control towers, re-usable containers management – require an item-level traceability of physical shipping units that is independent of the partners involved in the supply chains. Current information systems architectures match this need by interfacing heter-ogeneous systems with each other. Such architecture can't meet the challenges brought by new and shared logistics models. We demonstrate here how the re-cent EPCglobal® standards and related technologies are settled in a multi-firm open network, applied to the management of reusable pallets, taken here as de-monstrators of Open Tracing Containers (OTC). Material and methods for cap-turing data and structuring information are proposed and implemented in the Fast Moving Consumer Goods flows. Results illustrate the reach of that "Intra-net of things" prototype, leading to interoperable logistic services, throughout various levels: from identifier tag level up to the piloting of each partner's lo-gistics networks. We highlight limits and perspectives in terms of technical track and trace solutions and assets management in this environment

Auto-ID enabled tracking and tracing data sharing over dynamic B2B and B2G relationships

RFID 2011 collocated with the 2011 IEEE MTT-S International Microwave Workshop Series on Millimeter Wave Integration Technologies (IMWS 2011)Growing complexity and uncertainty are still the key challenges enterprises are facing in managing and re-engineering their existing supply chains. To tackle these challenges, they are continuing innovating management practices and piloting emerging technologies for achieving supply chain visibility, agility, adaptability and security. Nowadays, subcontracting has already become a common practice in modern logistics industry through partnership establishment between the involved stakeholders for delivering consignments from a consignor to a consignee. Companies involved in international supply chain are piloting various supply chain security and integrity initiatives promoted by customs to establish trusted business-to-customs partnership for facilitating global trade and cutting out avoidable supply chain costs and delays due to governmental regulations compliance and unnecessary customs inspection. While existing Auto-ID enabled tracking and tracing solutions are promising for implementing these practices, they provide few efficient privacy protection mechanisms for stakeholders involved in the international supply chain to communicate logistics data over dynamic business-to-business and business-government relationships. A unified privacy protection mechanism is proposed in this work to fill in this gap. © 2011 IEEE.published_or_final_versio

Meta-data alignment in open Tracking & Tracing systems

In Tracking and Tracing systems, attributes of objects (such as location, time, status and temperature) are recorded as these objects move through a supply chain. In closed, dedicated systems, the attributes to record and store are determined at design time. However, in open Tracking and Tracing systems, the attributes are not known beforehand, as the type of objects and the set of stakeholders may evolve over time. Many supply chains require open Tracking and Tracing systems. The participants in the supply chain are individual companies, spread over many countries. Their trading relations change constantly. Usually they participate in multiple supply chains. E.g., a company producing chemicals may serve the chemical industry, the food industry and the textile industry at the same time. Transport companies carry goods for multiple industry sectors. Yet, they play a role in the traceability of all goods they produce or carry. Open tracking and Tracing systems are not dedicated for a certain type of product or object nor for a specific industry sector. They simply record the location, time and other attributes of the identified objects, and store that information in the data store of the object owner, based on the identification (e.g. RFID) tag. What attributes are to be stored is determined by stakeholders, such as (end) users of the object. In some cases (e.g. food) legislation prescribes what to record. An open Tracking and Tracing system therefore needs to be able to dynamically handle the set of attributes to be recorded and stored. In this chapter, a method is presented that enables components of Tracking and Tracing systems to negotiate at run time what attributes may be stored for a particular object type. Components may include scanning equipment, data stores and query clients. Attributes may be of any data type, including time, location, status, temperature and ownership. Apart from simple attributes, associations between objects may be recorded and stored, e.g. when an object is packed in another object, loaded in a truck or container or assembled to be a new object. The method makes use of findings in ontology engineering and of type theory. New types are based on existing types, with some restrictions. Both the range of values of a type and its meta‐attributes (such as cardinality) may be restricted to define a new type. Programmatically, concepts of co‐ and contra variance are used to make the method implementable. The method was developed in two European funded research projects: TraSer and ADVANCE. In TraSer, a truly open and extensible Tracking and Tracing system was developed (TraSer project consortium, 2006; Monostori et al., 2009). In ADVANCE, a distributed management information system for logistics operations was designed and implemented, that makes use of Tracking and Tracing information (ADVANCE project consortium, 2010; Kemény et al., 2011a)

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed