A framework of concurrent task scheduling and dynamic voltage and frequency scaling in real-time embedded systems with energy harvesting

Abstract

Energy harvesting is a promising technique to overcome the limitation imposed by the finite energy capacity of batteries in conventional battery-powered embedded systems. In particular, the question of how one can achieve full energy autonomy (i.e., perpetual, battery-free operation) of a real-time embedded system with an energy harvesting capability (RTES-EH) by applying a global control strategy is investigated. The energy harvesting module is comprised of a Photovoltaic (PV) panel for harvesting energy and a supercapacitor for storing any excess energy. The global controller performs optimal operating point tracking for the PV panel, state-ofcharge management for the supercapacitor, and energy-harvestingaware real-time task scheduling with dynamic voltage and frequency scaling (DVFS) in the embedded load device. The controller, which accounts for dynamic V-I characteristics of the PV panel, terminal voltage variation and self-leakage of the supercapacitor, and power losses in voltage converters, employs a cascaded feedback control structure with an inner control loop determining the V-I operating point of the PV panel and an outer supervisory control loop performing real-time task scheduling and setting the voltage and frequency level in the embedded load device (to keep the state-ofcharge of the supercapacitor in a desirable range). Experimental results show that the proposed global controller lowers the task drop rate in a RTES-EH by up to 60% compared with baseline controller within the same service time.

Ambient energy harvesting nonvolatile processors

“…Different from the conventional power supply system, the nonvolatile sensor nodes are powered by the storageless and converter-less power supply system (e.g., [28], [23]). Present algorithms (e.g., LSA [35], DVFS [36], etc.) are based on inter-task scheduling and focus on the single period, which are not suitable for the NVP-based sensor nodes.…”

Joint Management of Energy Harvesting, Storage, and Usage for Green Wireless Sensor Networks

“…Reference [18] proposes a photovoltaic-thermoelectric hybrid power system, in which the energy management is to improve the energy efficiency and reliability. Reference [19] designs a realtime task scheduling framework based on the energy harvesting system, which contains a solar panel and a supercapacitor. And there are some other researches, for example, [20][21][22][23][24].…”

Harvesting-aware adaptive energy management in solar-powered embedded systems

“…In the context of harvesting-aware real-time systems, EDF-based scheduling algorithms were proposed in [3,9,10]. Scheduling algorithms exploiting DVFS function of CPU have been studied in [11][12][13]. These works use the number of missed deadlines as QoS metrics and are not able to guarantee a desired distribution of missed deadlines.…”

“…These works use the number of missed deadlines as QoS metrics and are not able to guarantee a desired distribution of missed deadlines. [13] employs a harvesting-aware control algorithm for DVFS. However, the timing and energy overhead of such control algorithm limit its applicability in real-time systems.…”

“…Each level q specifies a ( , ) constraint for task , where ≤ ′ if q≤q', mandating that task must meet at least out of every continuous task instances. Previous work [13] shows that when changing QoS levels, instances of task still meet ( , ) constraint of lower ratio , i.e., lower level q if task instances are scheduled in evenly-distributed pattern [16]. Energy consumption for each QoS level is estimated using a nominal speed.…”