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.