Stefan Bosse

Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift

Integration of sensors into various kinds of products and machines provides access to in-depth usage information as basis for product optimization. Presently, this large potential for more user-friendly and efficient products is not being realized because (a) sensor integration and thus usage information is not available on a large scale and (b) product optimization requires considerable efforts in terms of manpower and adaptation of production equipment. However, with the advent of cloud-based services and highly flexible additive manufacturing techniques, these obstacles are currently crumbling away at rapid pace. The present study explores the state of the art in gathering and evaluating product usage and life cycle data, additive manufacturing and sensor integration, automated design and cloud-based services in manufacturing. By joining and extrapolating development trends in these areas, it delimits the foundations of a manufacturing concept that will allow continuous and economically viable product optimization on a general, user group or individual user level. This projection is checked against three different application scenarios, each of which stresses different aspects of the underlying holistic concept. The following discussion identifies critical issues and research needs by adopting the relevant stakeholder perspectives.

Distributed Agent-Based Computing in Material-Embedded Sensor Network Systems With the Agent-on-Chip Architecture

Distributed material-embedded systems like sensor networks integrated in sensorial materials require new data processing and communication architectures. Reliability and robustness of the entire heterogeneous environment in the presence of node, sensor, link, data processing, and communication failures must be offered, especially concerning limited service of material-embedded systems after manufacturing. In this paper, multiagent systems with state-based mobile agents are used for computing in unreliable mesh-like networks of nodes, usually consisting of a single microchip, introducing a novel design approach for reliable distributed and parallel data processing on embedded systems with static resources. An advanced high-level synthesis approach is used to map the agent behavior to multiagent systems implementable entirely on microchip-level supporting agent-on-chip (AoC) processing architectures. The agent behavior, interaction, and mobility are fully integrated on the microchip using a reconfigurable pipelined communicating process architecture implemented with finite-state machines and register-transfer logic. The agent processing architecture is related to Petri Net token processing. A reconfiguration mechanism of the agent processing system achieves some degree of agent adaptation and algorithmic selection. The agent behavior, interaction, and mobility features are modeled and specified with an activity-based agent behavior programming language. Agent interaction and communication is provided by a simple tuple-space database implemented on node level and signals providing remote inter-node level communication and interaction.

Design and Simulation of Material-Integrated Distributed Sensor Processing with a Code-Based Agent Platform and Mobile Multi-Agent Systems

Multi-agent systems (MAS) can be used for decentralized and self-organizing data processing in a distributed system, like a resource-constrained sensor network, enabling distributed information extraction, for example, based on pattern recognition and self-organization, by decomposing complex tasks in simpler cooperative agents. Reliable MAS-based data processing approaches can aid the material-integration of structural-monitoring applications, with agent processing platforms scaled to the microchip level. The agent behavior, based on a dynamic activity-transition graph (ATG) model, is implemented with program code storing the control and the data state of an agent, which is novel. The program code can be modified by the agent itself using code morphing techniques and is capable of migrating in the network between nodes. The program code is a self-contained unit (a container) and embeds the agent data, the initialization instructions and the ATG behavior implementation. The microchip agent processing platform used for the execution of the agent code is a standalone multi-core stack machine with a zero-operand instruction format, leading to a small-sized agent program code, low system complexity and high system performance. The agent processing is token-queue-based, similar to Petri-nets. The agent platform can be implemented in software, too, offering compatibility at the operational and code level, supporting agent processing in strong heterogeneous networks. In this work, the agent platform embedded in a large-scale distributed sensor network is simulated at the architectural level by using agent-based simulation techniques.

Distributed computing and reliable communication in sensor networks using multi-agent systems

There is a growing demand for robust distributed computing and systems in sensor networks. Interaction between nodes is required to manage and distribute information. One common interaction model is the mobile agent. An agent approach provides stronger autonomy than a traditional object or remote-procedure-call based approach. Agents can decide for themselves, which actions are performed, and they are capable of flexible behaviour, reacting on the environment and other agents, providing some degree of robustness. The focus of the application scenario lies on sensor networks and low-power, resource-aware single System-On-Chip designs, i.e., for use in sensor-equipped technical structures and materials. We propose and compare two different data processing and communication architectures for the implementation of mobile agents in sensor networks consisting of single microchip low-resource nodes. Furthermore, a reliable smart communication protocol for incomplete and irregular networks are introduced. Two case studies show the suitability of agent-based approaches for distributed computing.

Unified Distributed Computing and Co-ordination in Pervasive/Ubiquitous Networks with Mobile Multi-Agent Systems using a Modular and Portable Agent Code Processing Platform.

Abstract A novel and unified approach for reliable distributed and parallel computing using mobile agents is introduced. The agents can be deployed in large scale and hierarchical network environments crossing barriers transparently. The networks can consist of high- and low-resource nodes ranging from generic computers to microchips, and the supported network classes range from body area networks to the Internet including any kind of sensor and ambient network. Agents are represented by mobile program code that can be modified at run-time. The presented approach enables the development of sensor clouds and smart systems of the future integrated in daily use computing environments and the Internet. Agents can migrate between different hardware and software platforms by migrating the program code of the agent, embedding the state and the data of an agent, too. The entire information exchange and coordination of agents with other agents and the environment is performed by using a tuple space database. Beside architecture specific hardware and software implementations of the agent processing platform, there is a JavaScript (JS) implemen- tation layered on the top of a distributed management layer. The JS platform enables the integration of Multi-agent Systems (MAS) in Internet server and application environments (e.g., WEB browser). Agents can migrate transparently between hardware-level sensor networks and WEB browser applications or network servers and vice versa without any transformation required.

Design of Material-integrated Distributed Data Processing Platforms with Mobile Multi-agent Systems in Heterogeneous Networks

An agent processing platform suitable for distributed computing in sensor networks consisting of low-resource (e.g., material-integrated) nodes is presented, providing a unique distributed programming model and enhanced robustness of the entire heterogeneous environment in the presence of node, sensor, link, data processing, and communication failures. In this work multi-agent systems with mobile activity-based agents are used for sensor data processing in unreliable mesh-like networks of nodes, consisting of a single microchip with limited low computational resources. The agent behaviour, interaction, and mobility (between nodes) can be efficiently integrated on the microchip using a configurable pipelined multi-process architecture based on Petri-Nets. Additionally, software implementations and simulation models with equal functional behaviour can be derived from the same source model. Hardware and software platforms can be directly connected in heterogeneous networks. Agent interaction and communication is provided by a simple tuple-space database and signals providing remote inter-node level communication and interaction. A reconfiguration mechanism of the agent processing system offers activity graph changes at run-time.

Hardware-software-co-design of parallel and distributed systems using a behavioural programming and multi-process model with high-level synthesis

A new design methodology for parallel and distributed embedded systems is presented using the behavioural hardware compiler ConPro providing an imperative programming model based on concurrently communicating sequential processes (CSP) with an extensive set of interprocess-communication primitives and guarded atomic actions. The programming language and the compiler-based synthesis process enables the design of constrained power- and resourceaware embedded systems with pure Register-Transfer-Logic (RTL) efficiently mapped to FPGA and ASIC technologies. Concurrency is modelled explicitly on control- and datapath level. Additionally, concurrency on data-path level can be automatically explored and optimized by different schedulers. The CSP programming model can be synthesized to hardware (SoC) and software (C,ML) models and targets. A common source for both hardware and software implementation with identical functional behaviour is used. Processes and objects of the entire design can be distributed on different hardware and software platforms, for example, several FPGA components and software executed on several microprocessors, providing a parallel and distributed system. Intersystem-, interprocess-, and object communication is automatically implemented with serial links, not visible on programming level. The presented design methodology has the benefit of high modularity, freedom of choice of target technologies, and system architecture. Algorithms can be well matched to and distributed on different suitable execution platforms and implementation technologies, using a unique programming model, providing a balance of concurrency and resource complexity. An extended case study of a communication protocol used in high-density sensor-actuator networks should demonstrate and compare the design of a hardware and software target. The communication protocol is suited for high-density intra-and interchip networks.

Tool chain for harvesting, simulation and management of energy in Sensorial Materials

The continuing decrease in size and energy demand of electronic sensor circuits allows endowing engineering structures and, to an increasing degree, materials with integrated sensing and data processing capabilities. Materials that adhere to this description are designated as Sensorial Materials. Their development is multidisciplinary and requires knowledge beyond materials science in fields like sensor science, computer science, energy harvesting, microsystems technology, low-power electronics, energy management, and communication. Development of such materials will benefit from systematic support for bridging research area boundaries. The present article introduces the backbone of an easy-to-use toolbox for layout of the energy supply of smart sensor nodes within a sensorial material. The fundamental approach is transferred from rapid control development, where a comparable MATLAB/Simulink tool chain is already in use. The main goal is to manage limited power resources without unacceptably compromising functionality in a given application scenario. The toolbox allows analysis of the modeled system in terms of energy and power and allows analyzing factors such as energy harvesting, use of predictive power estimation, power saving (e.g. sleep modes), model-based cognitive data reduction methods, and energy aware algorithm switching. It is linked to a simulation environment allowing analysis of energy demand and production in a specific application scenario. Its initial version presented here supports single self-powered sensor nodes. A broad set of application cases is used to develop scenario-dependent solutions with minimum energy needs and thus demonstrate the use of the toolbox and the associated development process. The initial test case is a large-scale sensor network with optical fiber–based data and energy transmission, for which optimization of energy consumption is attempted. The toolbox can be used to improve the power-aware design of sensor nodes on digital hardware level using advanced high-level synthesis approaches and provides input for sensor node and sensor network level.

Smart energy management and low-power design of sensor and actuator nodes on algorithmic level for self-powered sensorial materials and robotics

We propose and demonstrate a design methodology for embedded systems satisfying low power requirements suitable for self-powered sensor and actuator nodes. This design methodology focuses on 1. smart energy management at runtime and 2. application-specific System-On- Chip (SoC) design at design time, contributing to low-power systems on both algorithmic and technology level. Smart energy management is performed spatially at runtime by a behaviour-based or state-action-driven selection from a set of different (implemented) algorithms classified by their demand of computation power, and temporally by varying data processing rates. It can be shown that power/energy consumption of an application-specific SoC design depends strongly on computation complexity. Signal and control processing is modelled on abstract level using signal flow diagrams. These signal flow graphs are mapped to Petri Nets to enable direct high-level synthesis of digital SoC circuits using a multi-process architecture with the Communicating-Sequential-Process model on execution level. Power analysis using simulation techniques on gatelevel provides input for the algorithmic selection during runtime of the system, leading to a closed-loop design flow. Additionally, the signal-flow approach enables power management by varying the signal flow and data processing rates depending on actual energy consumption, estimated energy deposit, and required Quality-of-Service.

Design and Simulation of a Low-Resource Processing Platform for Mobile Multi-agent Systems in Distributed Heterogeneous Networks

The design and simulation of an agent processing platform suitable for distributed computing in heterogeneous sensor networks consisting of low-resource nodes is presented, providing a unique distributed programming model and enhanced robustness of the entire heterogeneous environment in the presence of node, sensor, link, data processing, and communication failures. In this work multi-agent systems with mobile activity-based agents are used for sensor data processing in unreliable mesh-like networks of nodes, consisting of a single microchip with limited low computational resources, which can be integrated into materials and technical structures. The agent behaviour, based on an activity-transition graph model, the interaction, and mobility can be efficiently integrated on the microchip using a configurable pipelined multi-process architecture based on the Petri-Net model and token-based processing. A new sub-state partitioning of activities simplifies and optimizes the processing platform significantly. Additionally, software implementations and simulation models with equal functional behaviour can be derived from the same program source. Hardware, software, and simulation platforms can be directly connected in heterogeneous networks. Agent interaction and communication is provided by a simple tuple-space database. A reconfiguration mechanism of the agent processing system offers activity graph changes at run-time. The suitability of the agent processing platform in large scale networks is demonstrated by using agent-based simulation of the platform architecture at process level with hundreds of nodes.