Dirk Lehmhus, Stefan Bosse, Walter Lang, P.C. Chao, F.
Chang, Guest Editorial Special Issue on Material-Integrated Sensing,
Data Processing and Communication, IEEE Sensors, 14 (7), 2014,
DOI: 10.1109/JSEN.2014.2330133. Paper PDF
TODAY, trends like the Internet of Things are nearing large-scale implementation. They rely on solutions enabling objects to become cyber-physical systems capable of perceiving their environment or their internal state. Sensing provides the information gateway to achieve this coupling between object and environment, as well as their interaction. This understanding has fuelled considerable research efforts on intelligent and sensor-equipped structures. Among the obstacles that impede their introduction are economic ones. Material rather than component-integrated sensing and intelligence has the potential to circumnavigate some of the obstacles by paving the way to economy-of-scale effects.
Stefan Bosse, Distributed Agent-based Computing in
Material-Embedded Sensor Network Systems with the Agent-on-Chip Architecture, IEEE Sensors Journal, Special Issue MIS, 2014,
DOI: 10.1109/JSEN.2014.2301938. Paper PDFPaper Online
[IEEE Sensors Top 25 Downloads in May/June 2014] 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 work multi-agent 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 behaviour to multi-agent systems implementable entirely on microchip-level supporting Agent-On-Chip processing architectures (AoC). The agent behaviour, 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 behaviour, interaction, and mobility features are modelled and specified with an activity-based agent behaviour programming language (AAPL). Agent interaction and communication is provided by a simple tuplespace database implemented on node level and signals providing remote inter-node level communication and interaction.
Stefan Bosse, Armin Lechleiter, Structural Health and Load Monitoring with Material-embedded Sensor Networks and Self-organizing Multi-Agent Systems, Procedia Technology, Proceeding of the SysInt 2014 Conference, 2-4 July 2014, Bremen, Germany, 2014,
DOI:10.1016/j.protcy.2014.09.039. Paper PDFPublisher PDF
One of the major challenges in Structural Health Monitoring and load monitoring of mechanical structures is the derivation of meaningful information from sensor data. This work investigates a hybrid data processing approach for material-integrated SHM and LM systems by using self-organizing mobile multi-agent systems (MAS), with agent processing platforms scaled to microchip level which offer material-integrated real-time sensor systems, and inverse numerical methods providing the spatial resolved load information from a set of sensors embedded in the technical structure. Inverse numerical approaches usually require a large amount of computational power and storage resources, not suitable for resource constrained sensor node implementations. Instead, off-line computation is performed, with on-line sensor processing by the agent system.
Stefan Bosse, Processing of Mobile Multi-Agent Systems with a Code-based Agent Platform in Material-Integrated Distributed Sensor Networks, 1st International e-conference on Sensors and Applications, Section D: Sensor Networks, 2014, 2014,
DOI:10.3390/ecsa-1-d010. Paper PDFPublisher PDF
[Best Presentation Award winner] Multi-agent systems (MAS) can be used for a decentralized and self-organizing approach of data processing in a distributed system like a sensor network, enabling information extraction, for example, based on pattern recognition, decomposing complex tasks in simpler cooperative agents. MAS-based data processing approaches can aid the Material-integration of Structural-Health-Monitoring applications, with agent processing platforms scaled to microchip level which offer material-integrated real-time sensor processing.The behaviour model of mobile agents suitable for sensor network operations bases on an activity-transition graph (ATG) and is implemented with stack-based program code holding the control and data state of an agent, which can be modified by the agent itself using code morphing techniques, and which is capable to migrate 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. The agent processing platform used for the execution of the agent code is a pipelined multi-stack virtual machine with a zero-operand instruction format, leading to small sized agent program code, low system complexity, and high system performance. Agents processed on one particular network node can interact by using a tuple-space database provided by each sensor node. Remote interaction is provided by propagating signals carrying data. This approach provides a high degree of computational independency from the underlying platform and other agents, and enhanced robustness of the entire heterogeneous environment in the presence of node, sensor, link, data processing, and communication failures. Support for heterogeneous networks considering hardware (System-on-Chip designs) and software (microprocessor) platforms is covered by one design and high-level synthesis flow including functional behavioural simulation.An even-based sensor data processing MAS is used as a test case for the proposed agent processing platform and a microchip level implementation. The sensor data pre-processing MAS delivers sensor data event-based if a change of the sensors was detected (based on pattern recognition), reducing network activity and energy consumption significantly.
Stefan Bosse, Design of Material-integrated Distributed Data Processing Platforms with Mobile Multi-Agent Systems in Heterogeneous Networks, Proc. of the 6’th International Conference on Agents and Artificial Intelligence ICAART 2014, 2014,
DOI:10.5220/0004817500690080. Paper PDFPublisher
[Nominated for Best Paper Award] 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.