Dynamic power management using adaptive learning tree
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
System architecture directions for networked sensors
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Dynamic Power Management: Design Techniques and CAD Tools
Dynamic Power Management: Design Techniques and CAD Tools
Dynamic Power Management in Wireless Sensor Networks
IEEE Design & Test
LICS '96 Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science
Dynamic Power Management in Wireless Sensor Networks: An Application-Driven Approach
WONS '05 Proceedings of the Second Annual Conference on Wireless On-demand Network Systems and Services
Design Considerations for Ultra-Low Energy Wireless Microsensor Nodes
IEEE Transactions on Computers
An Efficient Dynamic Power Management Policy on Sensor Network
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Sentry-based power management in wireless sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Improving energy saving in wireless systems by using dynamic power management
IEEE Transactions on Wireless Communications
IEEE Communications Magazine
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Wireless sensor networks play a key role in monitoring remote or inhospitable physical environments. One of the most important constraints is the energy efficiency problem. Power conservation and power management must be taken into account at all levels of the sensor networks system hierarchy. Especially, DPM (Dynamic Power Management) technology, which shuts down the devices when not needed and wake them up when necessary, has been widely used in sensor networks. In this paper, we modify the sleep state policy developed by Simunic and Chdrakasan in [1] and deduce a new threshold satisfies the sleep-state transition policy. Nodes in deeper sleep states consume lower energy while asleep, but require longer delays and higher latency costs to awaken. Implementing dynamic power management with considering the battery status and probability of event generation will reduce the energy consumption and prolong the whole lifetime of the sensor networks. The sensor network consumed less energy in our simulation than that in [1].