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Energy adaptation techniques to optimize data delivery in store-and-forward sensor networks
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Source Conference On Embedded Networked Sensor Systems archive
Proceedings of the 4th international conference on Embedded networked sensor systems table of contents
Boulder, Colorado, USA
POSTER SESSION: Posters table of contents
Pages: 405 - 406  
Year of Publication: 2006
ISBN:1-59593-343-3
Authors
Pei Zhang  Princeton University
Margaret Martonosi  Princeton University
Sponsors
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGCOMM: ACM Special Interest Group on Data Communication
SIGOPS: ACM Special Interest Group on Operating Systems
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
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ABSTRACT

Wireless sensor networks are severely-energy constrained devices. Energy-related issues are one of the common failure modes in sensor deployments. One challenge in systemwide energy management is that individual nodes in a sensor network often have widely varying energy profiles due to the amount of data transmitted, hardware construction, and other environmental effects. These differences result in unpredictable node and system lifetimes. As a result, sensor network bit-rate and reliability may degrade prematurely. Our research explores and evaluates an easily implemented dynamic scheduling policy supported by a battery gauge aimed to solve this problem.The dynamic scheduling policy presented here operates in a slotted manner. The decision for each node to communicate is based on the available energy of that node. Our policy guarantees a minimum communication bandwidth, while allowing nodes with more energy to increase their available bandwidth by a factor related to the amount of "extra" energy they have. We present real-system results measured on test nodes in several different network scenarios. The results show our scheduling, when compared to a fixed schedule, guarantees a longer usable system lifetime by preventing premature degradation of connections. In addition to improving connectivity, it reduces data delay by as much as 50% for intermittently connected nodes, with no added communication overhead.



Collaborative Colleagues:
Pei Zhang: colleagues
Margaret Martonosi: colleagues