Battery capacity measurement and analysis using lithium coin cell battery
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Contiki - A Lightweight and Flexible Operating System for Tiny Networked Sensors
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
Micro power meter for energy monitoring of wireless sensor networks at scale
Proceedings of the 6th international conference on Information processing in sensor networks
A Flexible Scheduling Framework for Deeply Embedded Systems
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Accurate prediction of power consumption in sensor networks
EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
Software-based on-line energy estimation for sensor nodes
Proceedings of the 4th workshop on Embedded networked sensors
PVS: passive voltage scaling for wireless sensor networks
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
Energy Metering for Free: Augmenting Switching Regulators for Real-Time Monitoring
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Quanto: tracking energy in networked embedded systems
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
A case for opportunistic embedded sensing in presence of hardware power variability
HotPower'10 Proceedings of the 2010 international conference on Power aware computing and systems
Flexible online energy accounting in TinyOS
REALWSN'10 Proceedings of the 4th international conference on Real-world wireless sensor networks
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Energy is the crucial factor for the lifetime of wireless sensor networks. Nonlinear battery effects and nonuniform workload distribution can lead to early node failures. This makes it necessary to manage energy consumption. But to manage energy it is essential to know how much energy is spent by the system. Additionally, for a more fine-grained management it is necessary, to know where the energy is spent. This can be a complicated task, since nodes are not identical due to device variations and the consumption can change over time. In this paper we present an online energy accounting approach which focuses on simplicity instead on fine granularity and timing accuracy. We argue that the efficacy of an energy accounting model depends more on the input consumption data than on exact timing, especially when the real consumption varies between nodes and in time. Results show that this approach is capable of correctly accounting the energy that nodes spend in scenarios with deviating environment conditions.