Abstract
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.
Highlights
Introduction andState-of-the-ArtStructural monitoring of mechanical structures allows deriving not just loads, and their effects on the structure, its safety and its functioning from sensor data
Multi-agent systems (MAS)-based data processing approaches can aid the material-integration of structural health monitoring applications, with agent processing platforms scaled to the microchip level, which offer material-integrated real-time sensor processing
The processing platform can be implemented efficiently in software with code and operational compatibility, enabling deployment in heterogeneous network environments, inter-connecting hardware and software platforms executed on generic microprocessors
Summary
Structural monitoring of mechanical structures allows deriving not just loads, and their effects on the structure, its safety and its functioning from sensor data. MAS-based data processing approaches can aid the material-integration of structural health monitoring applications, with agent processing platforms scaled to the microchip level, which offer material-integrated real-time sensor processing. This work is based on an earlier data processing architecture described in [12] using virtual stack machines and mobile program code based on the FORTHinstruction set and which can migrate between different VMs and nodes of a distributed (sensor) network. A token-based pipelined multi-core stack VM architecture for the agent processing (PAVM), which is suitable for hardware microchip implementations on register-transfer level and system-on-a-chip architectures, offers optimized computational resources and exceptional speed, requiring less than. There is improved scaling in large heterogeneous network applications, due to low host platform and communication dependencies of the VM and the agent FORTH programming model
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