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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kansal, A. Srivastava, M.B. |
| Copyright Year | 2003 |
| Description | Author affiliation: Dept. of Electr. Eng., California Univ., Los Angeles, CA, USA (Kansal, A.; Srivastava, M.B.) |
| Abstract | Energy constrained systems such as sensor networks can increase their usable lifetimes by extracting energy from their environment. However, environmental energy will typically not be spread homogeneously over the spread of the network. We argue that significant improvements in usable system lifetime can be achieved if the task allocation is aligned with the spatio-temporal characteristics of energy availability. To the best of our knowledge, this problem has not been addressed before. We present a distributed framework for the sensor network to adaptively learn its energy environment and give localized algorithms to use this information for task sharing among nodes. Our framework allows the system to exploit its energy resources more efficiently, thus increasing its lifetime. These gains are in addition to those from utilizing sleep modes and residual energy based scheduling mechanisms. Performance studies for an experimental energy environment show up to 200% improvement in lifetime. |
| Sponsorship | ACM SIGDA IEEE Circuits & Syst. Soc. IEEE Solid-State Circuits Soc. IEEE Electron Devices Soc |
| Starting Page | 481 |
| Ending Page | 486 |
| File Size | 666813 |
| Page Count | 6 |
| File Format | |
| ISBN | 158113682X |
| DOI | 10.1109/LPE.2003.1231958 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-08-27 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Association for Computing Machinery, Inc. (ACM) |
| Subject Keyword | Sensor phenomena and characterization Batteries Sensor systems Processor scheduling Computer networks Distributed computing Scheduling algorithm Algorithm design and analysis Permission Routing protocols |
| Content Type | Text |
| Resource Type | Article |
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