Energy harvesting battery-free embedded devices rely only on ambient energy harvesting enabling stand-alone and sustainable IoT applications. These devices execute intermittent programs when the harvested ambient energy in their energy reservoir is sufficient to operate and stop execution abruptly (and start charging) otherwise. They need to provide useful outputs within a reasonable time despite erratic, random, or irregular energy availability, which causes inconsistent execution, loss of service, and power failures.
Understanding energy availability and its volatility in the deployment environment is a key insight for developers using batteryless platforms. Considering the capabilities and constraints of the embedded devices, the accuracy of the computation can be sacrificed to save harvested energy. Adapting program execution (degrading or upgrading computation complexity) based on available or predicted power/energy seems promising to stave off power failures, meet deadlines, or increase program throughput. However, it is challenging to decide what and when to adapt due to constrained resources and limited local information.
What you will do:
- You will investigate metaheuristic algorithms to generate adaptation heuristics automatically for intermittently powered batteryless sensors. The basic idea behind the envisioned approach is to derive the adaptation heuristics which adapt the given intermittent program while maximizing the program throughput with minimum accuracy drop.
- You will assess the developed techniques for various real-world applications in diverse energy environments.
What you will learn\Qualifications:
- You are expected to have or develop knowledge in metaheuristic algorithms, embedded system programming, and simulation environments.