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Über dieses Buch

The development of low-cost, compact digital storage, sensors and radio modules allows us to embed digital memories into products to record key events. Such computationally enhanced products can perceive and control their environment, analyze their observations, and communicate with other smart objects and human users. Digital product memories (DPMs) will play a key role in the upcoming fourth industrial revolution based on cyber-physical production systems, resulting in improvements in traceability and quality assurance, more efficient and flexible production, logistics, customization, and recycling, and better information for the consumer.

SemProM was a major industrial and academic research project that examined all aspects of the design and implementation of semantic product memories, and this book is a comprehensive assessment of the results achieved. The introductory chapters explain the fundamental ideas and the organization of the related project, while the remaining parts explain how to build, model and process DPMs, multimodal interaction using them, and selected applications.

This work is inherently multidisciplinary and the related ideas, technologies, and implementations draw on results in fields such as semantic technologies, machine-to-machine communication, intelligent sensor networks, instrumented environments, embedded systems, smart objects, RFID technology, security, and privacy. The contributing authors are leading scientists and engineers, representing key academic teams and companies.

The book explains successful deployment in applications such as manufacturing, green logistics, retail, healthcare, and food distribution, and it will be of value to both researchers and practitioners.





The Semantic Product Memory: An Interactive Black Box for Smart Objects

Low-cost and compact digital storage, sensors and radio modules make it possible to embed a digital memory into a product for recording all relevant events throughout the entire lifecycle of the artifact. By capturing and interpreting ambient conditions and user actions, such computationally enhanced products have a data shadow and are able to perceive and control their environment, to analyze their observations and to communicate with other smart objects and human users about their lifelog data. In the introductory section of this chapter, we illustrate the innovation and application potential offered through the concept of semantic product memories by an imaginative scenario. Then we provide a taxonomy of the wide variety of digital object memories: from mobile cyber-physical systems to semantic product memories in open-loop applications. We show that extended customer information, traceability and increased quality assurance have been the drivers for the rudimentary forerunners of product memories in the food industry. Then we discuss the benefits and risks of semantic product memories for producers as well as consumers. We argue that active semantic product memories will play a key role in the upcoming fourth industrial revolution based on cyber-physical production systems. Finally, we provide an overview of the structure and content of the remainder of this book.
Wolfgang Wahlster

SemProM—Dissemination and Impact

In the SemProM project “Products Keep a Diary” smart labels give products a memory and support intelligent logistics. Within the ICT 2020 research program of the German Federal Ministry of Education and Research the Innovation Alliance “Digital Product Memory” (IA DPM) is developing key technologies for the Internet of Things in the cooperative project SemProM. Through the use of integrated sensors, relations in the production process become transparent and supply chains as well as environmental influences traceable. The producer is supported and the consumer better informed about the product.
Anselm Blocher

Towards an Integrated Framework for Semantic Product Memories

This chapter provides a general overview concerning the technical concept of the SemProM project. The notion of a Digital Product Memory (DPM) is explained, and the technical requirements resulting from its characteristics are outlined. Based on a review of the state of the art, the basic building blocks and steps for the specification and realization of an overall framework for semantic product memories are discussed. The central elements of the developed approach are presented in more detail. This includes a layered architecture model for SemProM incarnations and a generalized architecture conception for the required technical infrastructure and middleware components. Last but not least, the practical application of the SemProM framework is also considered. Experimental results from building integrated system prototypes illustrate the potential of the novel approach.
Gerd Herzog, Alexander Kröner

Platforms for Building a Digital Product Memory


Hardware Requirements for Digital Product Memories

Although the focus of the SemProM project is clearly on the consistent information concept of digital product memories, some kind of hardware is needed when ideas and concepts become reality. As stated in Blocher (2013), the SemProM project investigated a wide range of use cases, because one of the goals was to identify areas with high potential for improvement using Digital Product Memories (DPMs). As a result, a large number of hardware requirements were accumulated. In this chapter the requirements are clustered and summarized. Two specific applications exemplify the requirements.
Jörg Neidig

The Smart SemProM

The Smart SemProM is one of the different hardware categories defined in the project. In short, it is a compact, self-contained, embedded device with limited computing power that is designed to perform a few dedicated functions and to interact with its environment. In this chapter it is described how such a device was developed and constructed. The aim was to provide a flexible testbed for a large number of Smart SemProM applications and use cases. Starting from the requirements derived from use case descriptions, a hardware prototype was designed. To create a flexible software environment, an application framework was developed to control the different applications and allow running of concurrent tasks. The potential of the resulting device is illustrated by two application examples.
Jörg Neidig, Thomas Grosch, Ulrike Heim

A Robotic Platform for Building and Exploiting Digital Product Memories

This chapter presents the design of the robotic platform AILA, a mobile dual-arm robot system developed as a research platform for investigating aspects of the currently booming multidisciplinary area of mobile manipulation. The robot integrates and allows in a single platform performance of research in most of the areas involved in autonomous robotics: navigation, mobile and dual-arm manipulation planning, active compliance and force control strategies, object recognition, scene representation, and semantic perception. AILA has 32 degrees of freedom (DOF), including 7-DOF arms, a 4-DOF torso, a 2-DOF head, and a mobile base equipped with 6 wheels, each of them with 2 degrees of freedom. Additionally, the left hand of the robot was equipped with a RFID reader in order to receive the information coming from the DPM. This chapter provides an overview of the design, the variety of sensors incorporated in the system, and its required control software.
Johannes Lemburg, Dennis Mronga, Achint Aggarwal, José de Gea Fernández, Marc Ronthaler, Frank Kirchner

Capturing Sensor Data in the SemProM Automotive Scenario

This chapter presents the concept and implementation of a smart SemProM to capture and process sensor data in the context of the automotive DPM architecture called the DPM Sensor Platform. Two exemplary use cases of the development process were chosen to illustrate the evolutionary and also revolutionary use of the DPM Sensor Platform. The first one shows how existing systems can evolve by replacing parts of them with this platform, and the resulting benefits. The second one highlights the advantages of the integration of this platform in the holistic automotive DPM architecture and serves as an example for our proposal for a new paradigm for handling sensor data in the automotive context. Based on these use cases the requirements are derived, system, hardware, and software concepts are presented, and the prototypes are described.
Young-Jae Cho, Jörg Preißinger

Modeling and Processing Digital Product Memories


The SemProM Data Format

Based on recently emerged technologies such as Radio Frequency Identification (RFID), 2D matrix codes, and embedded devices, products can be uniquely identified and tracked throughout the entire lifecycle. Data acquired along a product lifecycle can be associated to single items and unique instances of a product. Today, significant parts of these data can be stored directly on the item itself.
Within the research in the Innovation Alliance “Digital Product Memory” (DPM), a container format for such a product memory was developed. It enables usage of the same storage media for different block data (multipart) and provides a lean metadata structure for current technologies. Relations in the production process and supply chains, as well as environmental influences, become retraceable. The producer is supported and the consumer better informed about the product.
The SemProM container format focuses mainly on a binary format for resource-limited memory technologies, but the concept is in principle usable as an XML representation in upper layers or API definitions, too.
Sven Horn, Alexander Claus, Jörg Neidig, Bruno Kiesel, Thorbjørn Hansen, Jens Haupert

DPM Mapper: A Concept to Bridge the Gap Between XML-Based Digital Product Memories and Their Binary Representation

In this chapter we introduce a concept that improves the use of Digital Product Memory (DPM) on industrial embedded control systems to control decentralized production processes. The core of this concept is an XML schema that supports the specification of machine-readable mappings between an XML-based and binary DPM representation. This supports the separate description of the DPM information used for production control and its binary memory representation, e.g., on RFID tags. A server that stores those XML mapping documents and processes the address queries from the embedded production control systems has been developed. To demonstrate the feasibility of the approach this mapping technology was implemented in a demonstration module which was presented at Hannover Messe 2010.
Marc Seißler, Peter Stephan, Jochen Schlick, Ines Dahmann

A Digital Product Memory Architecture for Cars

The architecture of the car, acting as an integrated Digital Product Memory (DPM) with many sources, is introduced in this chapter. Static and nomadic sources such as sensors and data stores which are connected by a highly complex and heterogeneous communication network need an architecture adapted for the requirements of the automotive domain. Requirements and concepts are presented in detail. Consumer advantages and fulfilled requirements are summarized.
Young-Jae Cho, Florian Kuttig, Markus Strassberger, Jörg Preißinger

The Object Memory Server for Semantic Product Memories

The SemProM format was basically designed for on-product RFID-based memories. Furthermore, some use cases demand centralized storage or data backups that cannot be achieved with on-product storage. For these cases (e.g., cheap products with very small labels, very large memories), a server-based solution might be more suitable. We developed the Object Memory Server (OMS) as an index server for product memories, based on the same set of metadata as the block format. The actual payload is outsourced to servers in the web. The URL used for accessing an OMS memory can be stored in simple and cheap RFID labels. Due to the large processing power of a server-based approach, the OMS can handle all SemProM incarnations, ranging from Reference SemProMs to Smart SemProMs. The conceptual ideas of the OMS were also transformed to provide a server-based solution for memories based on the W3C XG OMM format.
Jens Haupert, Michael Schneider

The Block Interface: Accessing Digital Product Memories

The block interface defines the access to the different information blocks inside the Digital Product Memory (DPM). It is applicable for a range of devices starting from passive data stores (Storage SemProM, e.g., RFID) up to intelligent devices with a dedicated processing unit (Smart SemProM, e.g., motes). This chapter gives an in-depth introduction to the requirements for such an interface and its implementation. The application of the interface is illustrated by an example.
Bruno Kiesel, Jörg Neidig

Distributed Digital Product Memories

Auto ID technologies based on RFID, 2D matrix codes, and barcodes allow identification not only of product types but also objects at item level. Information acquired along the product lifecycle can consequently be associated to items. The acquired data can be stored either at the item itself on product-embedded storage or remotely, for example, on a server. As current RFID tags cannot store all relevant data due to the limited space, a solution is required to store product information remotely, providing access for all relevant lifecycle actors.
This chapter provides an overview of selected approaches to related distributed information management for that purpose. The focus is on managing information on products throughout the entire product lifecycle. Peer-to-Peer (P2P) networks are identified to be a promising solution. So P2P networks become a scalable option to flexibly store and distribute product information and are particularly considered.
A second challenge for the storage, organization, and retrieval of Digital Product Memories (DPMs) are composite products that contain subcomponents with their own DPMs, e.g., cars and manufacturing plants. We present a simple and general model for the structure of products with several DPMs that is independent of the considered product, domain, application, and company. It provides a standardized basis to distribute and query the data of subcomponents. The model also supports composite DPMs, where a DPM can store the information of its subcomponents.
Sven Horn, Barbara Schennerlein, Anne Pförtner, Thorbjørn Hansen

Multimodal Interaction with the Digital Product Memory


Supporting Interaction with Digital Product Memories

On its way along the supply chain, a product may be exposed to physical actors with very different requirements for the interaction with a DPM. For instance, while human users may precisely perceive a given product’s visual shape, they have to rely on a “mediating device” in order to create and apply content stored in a DPM. In contrast, robots may directly access the data stored in a DPM, but may require specific data in order to get a better “understanding” of a physical interaction task. Finally, DPMs may have to interact with other DPMs in their surroundings, for instance, in order to delegate communication tasks. This chapter reviews components of the access layer, a part of the SemProM interaction architecture which has been introduced to support tasks particularly common to the interaction of humans, robots, and DPMs with DPMs.
Alexander Kröner, Jens Haupert, José de Gea Fernández, Rainer Steffen, Christian Kleegrewe, Martin Schneider

Controlling Interaction with Digital Product Memories

Creating intelligent interactive applications based on DPMs is a challenging task. The definition of system reactions, independent interface behaviors, and necessary context data requires expert programmers at various representation levels and often results in hard-to-maintain code. We use the Visual SceneMaker authoring tool, a visual authoring approach which was initially designed for the creation of interactive applications with virtual characters and has been extended to serve as a dialog and interaction manager for our DPM interactive applications. In SceneMaker a clean separation of content (scenes) and logic (sceneflow) is enforced. In order to access high-level context information, the SceneMaker tool provides interfaces to the Object Memory Server (OMS) and to knowledge deduction systems, such as the Java Expert System Shell (JESS). The tool also supports concurrency, variable scoping, and interaction history to facilitate modeling of multiple interaction modalities, robust data access, and flexible interruption policies as they occur in intelligent interactive environments with DPMs. Moreover, the version used allows real-time sceneflow visualization and modification at runtime to facilitate rapid prototyping and code maintenance. In the context of the SemProM project we rely on interactive virtual characters in several application setups in order to provide users with a compelling interaction experience with DPMs.
Patrick Gebhard

Interaction Modalities for Digital Product Memories

Interacting with Digital Product Memories (DPMs) along the supply chain occurs in a variety of scenarios with different users in different locations with different tasks. This chapter discusses solutions for the modality layer, which establishes the end point of a communication channel between an actor and the DPM, connecting the user to the dialog logic of an application based on DPMs.
Michael Schmitz, Boris Brandherm, Jörg Neidig, Stefanie Schachtl, Matthias Schuster

Applications of Digital Product Memories


Applying Digital Product Memories in Industrial Production

Industrial production and supply chains face increased demands for mass customization and tightening regulations on the traceability of goods, leading to higher requirements concerning flexibility, adaptability, and transparency of processes. Technologies for the “Internet of Things” such as smart products and semantic representations pave the way for future factories and supply chains to fulfill these challenging market demands. In this chapter a backend-independent approach for information exchange in open-loop production processes based on Digital Product Memories (DPMs) is presented. By storing order-related data directly on the item, relevant lifecycle information is attached to the product itself. In this way, information handover between several stages of the value chain with focus on the manufacturing phase of a product has been realized. In order to report best practices regarding the application of DPM in the domain of industrial production, system prototype implementations focusing on the use case of producing and handling a smart drug case are illustrated.
Peter Stephan, Markus Eich, Jörg Neidig, Martin Rosjat, Roberto Hengst

Using Basic RFID-Based Digital Product Memories for Protection against Counterfeit Goods in Manufacturing Plants

This chapter describes an examplary implementation based on digital product memories in the field of protection against counterfeit goods in manufacturing plants in the SemProM project.
Jörg Neidig

A SemProM Use Case: Tracking & Tracing for Green Logistics and Integrity Control

This chapter addresses how visibility solutions based on Digital Product Memories (DPMs) developed in the SemProM project can be demonstrated in the logistics domain to guarantee carbon offset of transport and integrity control within supply chains. A demonstration system is presented to illustrate how a DPM can be used for computing, assessing, and reducing a product’s carbon footprint. In addition, semi-active and active RFID and sensor solutions developed to monitor product integrity are described. Finally, the SemProM browser is presented as a system for end-users to access product information and get visibility over product integrity.
Markus Kückelhaus, Carsten Magerkurth, Jörg Baus

Enhancement of Consumer Support in Retail Scenarios by Utilization of Semantic Product Memories

In this chapter, the utilization of Digital Product Memories (DPMs) in the retail domain is discussed. We demonstrate a complete retail-related usage scenario from a consumer perspective that begins with the preparation of a shopping list before the physical in-store shopping activity. Throughout the entire shopping process in the retail store, the DPM infrastructure effectively supports the consumer with appropriate product recommendations. It provides explicit and implicit access to the consumer’s user profile with different interaction modalities, e.g., via a display on the shopping cart or the consumer’s cell phone, and supports the payment and checkout process based on digital user representations stored on the consumer’s physical items such as his car key. In order to fully close the circle, we then conclude the discussion of DPM-based consumer support by analyzing the way home by car, as a DPM infrastructure in a car provides ample opportunities for driver assistance, including but not limited to monitoring the temperature in the car or finding an optimal route home that avoids roadworks in order to address the extra need for a shock-free ride for bought items such as raw eggs or bottles of champagne.
Gerrit Kahl, Carsten Magerkurth, Jörg Preißinger, Patrick Gebhard, Benjamin Weyl

A SemProM Use Case: Health Care and Compliance

Smart package solutions may support a patient in the intake of medication—for instance, through the package’s form factor or by linking of the package with digital data and services built upon these data. This chapter discusses the application potential of a so-called digital product memory in this domain. The starting point is a visionary scenario in which the interplay of intelligent environments and medicine packages equipped with digital product memories allows for unobtrusive support of a patient in dealing with situations that might affect his or her therapy compliance. In this chapter, components of a technical infrastructure of particular relevance to the envisioned kind of support are described. This discussion is complemented with a review of prototype implementations of interaction mechanisms taken from the scenario.
Boris Brandherm, Michael Schmitz, Robert Neßelrath, Frank Lehmann

A SemProM Use Case: Maintenance of Factory and Automotive Components

Maintenance is essential to guarantee the availability of any technical equipment, but is the dominant cost factor during the equipment’s operating phase. In this chapter it is shown how Digital Product Memories (DPMs) can be used to optimize different maintenance tasks. Therefore, the analysis is focused on the requirements of two domains: industrial manufacturing and automobiles.
Jörg Neidig, Jörg Preißinger

A Summary of End-User Feedback on Digital Product Memories

Digital product memories enable novel item-centric ways of communication along a product’s lifecycle. With respect to the open nature of the scenario ranging from manufacturer to consumer, the expectations for such digital product memories are diverse. This chapter gives a brief summary of empirical studies with (potential) end-users of digital product memories. It combines information we acquired at three different public IT and industrial fairs in 2009 and 2010 with a total of 515 visitors, a user study conducted with 27 participants of a shopping cart scenario at the Innovative Retail Lab and an experiment with 12 students concerning working group support from so-called artifact memories. We compiled our observations into five hypotheses concerning aspects of digital product memory ranging from technical constraints to preferred applications.
Gerrit Meixner, Alexander Kröner, Gerrit Kahl
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