In battery-powered embedded systems, the energy budget management is a critical aspect. For systems using unreliable power sources, e.g. solar panels, the continuous system operation is a challenging requirement. In such scenarios, effective management policies must rely on accurate energy estimations. In this paper we propose a measurement-based probabilistic approach to address the worst-case energy consumption (WCEC) estimation, coupled with a job admission algorithm for energy-constrained task scheduling. The overall goal is to demonstrate how the proposed approach can introduce benefits also in mission-critical systems, where unsafe energy budget estimations cannot be tolerated
We report an optimum operation decision method when a system load varies probabilistically and NO_x ...
Integration of renewable energy resources in microgrids has been increasing in recent decades. Due t...
[EN] The number of (edge) devices connected to the IoT is on the rise, reaching hundreds of billions...
In battery-powered embedded systems, the energy budget management is a critical aspect. For systems ...
In this work, we present a formal study on optimizing the energy consumption of energy harvesting em...
When the energy-harvesting embedded system (EHES) is running, its available energy (harvesting energ...
The strict requirements on the timing correctness biased the modeling and analysis of real-time sys...
The advent of autonomous power-limited systems poses a new challenge for system verification. Powerf...
Wireless systems such as satellites and sensor networks are often battery-powered. To operate optima...
Abstract—This paper addresses an approach for accurately measuring energy consumption on battery-pow...
The trade-off between system performance and energy efficiency (service time) is critical for batter...
Abstract—The fact that energy is a scarce resource in many embedded real-time systems creates the ne...
The increasing number of embedded systems spawns applications with critical constraints in both exec...
Recently, there has been a substantial interest in the design of systems that receive their energy f...
Reliability-aware power management (RAPM) schemes have been recently studied to save energy while pr...
We report an optimum operation decision method when a system load varies probabilistically and NO_x ...
Integration of renewable energy resources in microgrids has been increasing in recent decades. Due t...
[EN] The number of (edge) devices connected to the IoT is on the rise, reaching hundreds of billions...
In battery-powered embedded systems, the energy budget management is a critical aspect. For systems ...
In this work, we present a formal study on optimizing the energy consumption of energy harvesting em...
When the energy-harvesting embedded system (EHES) is running, its available energy (harvesting energ...
The strict requirements on the timing correctness biased the modeling and analysis of real-time sys...
The advent of autonomous power-limited systems poses a new challenge for system verification. Powerf...
Wireless systems such as satellites and sensor networks are often battery-powered. To operate optima...
Abstract—This paper addresses an approach for accurately measuring energy consumption on battery-pow...
The trade-off between system performance and energy efficiency (service time) is critical for batter...
Abstract—The fact that energy is a scarce resource in many embedded real-time systems creates the ne...
The increasing number of embedded systems spawns applications with critical constraints in both exec...
Recently, there has been a substantial interest in the design of systems that receive their energy f...
Reliability-aware power management (RAPM) schemes have been recently studied to save energy while pr...
We report an optimum operation decision method when a system load varies probabilistically and NO_x ...
Integration of renewable energy resources in microgrids has been increasing in recent decades. Due t...
[EN] The number of (edge) devices connected to the IoT is on the rise, reaching hundreds of billions...