An energy management framework for energy harvesting embedded systems
ACM Journal on Emerging Technologies in Computing Systems (JETC)
Dynamic power management in environmentally powered systems
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Maximum utility rate allocation for energy harvesting wireless sensor networks
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Energy harvesting and power management for autonomous sensor nodes
Proceedings of the 49th Annual Design Automation Conference
Sensor activation and radius adaptation (SARA) in heterogeneous sensor networks
ACM Transactions on Sensor Networks (TOSN)
A novel approach to multiparametric quadratic programming
Automatica (Journal of IFAC)
A distributed smart application for solar powered WSNs
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part II
Energy-efficient and reliable data delivery in wireless sensor networks
Wireless Networks
Solar harvest prediction supported by cloud cover forecasts
Proceedings of the 1st International Workshop on Energy Neutral Sensing Systems
Comparison of energy update models for wireless sensor nodes with supercapacitors
Proceedings of the 1st International Workshop on Energy Neutral Sensing Systems
| Hi-index | 14.98 |
Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that minimize the power consumption subject to performance constraints, we are concerned with optimizing the performance of an application while respecting the limited and time-varying amount of available power. In this paper, we address power management of, e.g., wireless sensor nodes which receive their energy from solar cells. Based on a prediction of the future available energy, we adapt parameters of the application in order to maximize the utility in a long-term perspective. The paper presents a formal model of the corresponding optimization problem including constraints concerning buffer sizes, timing, and rates. Instead of solving the optimization problem online which may be prohibitively complex in terms of running time and energy consumption, we apply multiparametric programming to precompute the application parameters offline for different environmental conditions and system states. In order to guarantee sustainable operation, we propose a hierarchical software design which comprises a worst-case prediction of the incoming energy. As a further contribution, we suggest a new method for approximate multiparametric linear programming which substantially lowers the computational demand and memory requirement of the embedded software. Our approaches are evaluated using long-term measurements of solar energy in an outdoor environment.