Accurate prediction of power consumption in sensor networks

By Olaf Landsiedel, Klaus Wehrle, S. Gotz. The Second IEEE Workshop on Embedded Networked Sensors, 2005. EmNetS-II., 2005

310 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

MUSTER: Adaptive Energy-Aware Multisink Routing in Wireless Sensor Networks (2011, methodology): “We can estimate the average current draw Iavg by using external hardware devices [19], energy accounting [34], or software-based power profilers [17].”
Software-based on-line energy estimation for sensor nodes (2007, background): “There are many sensor network simulators with energy estimation abilities [4, 5].”
Avrora: scalable sensor network simulation with precise timing (2005, methodology): “[12] have added a highly accurate energy model to Avrora, enabling power”
MMSPEED: multipath Multi-SPEED protocol for QoS guarantee of reliability and. Timeliness in wireless sensor networks (2006, background): “However, according to previous studies [31], [32], [33], the dominant source of power consumption is the radio module.”
Edge-computing-driven Internet of Things: A Survey (2022, background): “In TinyOS, which supports high-power listening (HPL), TinyOS estimates the overall load of the sensing nodes and then dynamically allocates the required energy to the sensing nodes [93].”
On the lifetime of wireless sensor networks (2009, methodology): “The nodes used energy only for communication, and the power consumption values were taken from measurements on Mica2 nodes presented in [78].”