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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pinto, A.R. Montez, C. |
| Copyright Year | 2010 |
| Description | Author affiliation: PGEAS - Universidade Federal de Santa Catarina - UFSC, Brazil (Pinto, A.R.; Montez, C.) |
| Abstract | Wireless Sensor Networks (WSN) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) in each node can not be easily replaced. One solution is to deploy a large number of sensor nodes, since the lifetime and dependability of the network can be increased through cooperation among nodes. In addition to energy consumption, applications for WSN may also have other concerns, such as, meeting deadlines and maximizing the quality of information. In this paper, two autonomic approaches for dense WSN are presented. The first approach is a Genetic Machine Learning algorithm aimed at applications that make use of trade-offs between different metrics. Simulations were performed on random topologies assuming different levels of faults. GMLA showed a significant improvement when compared with the use of IEEE 802.15.4 protocol. Moreover, an approach that autonomically provides QoS for dense WSN called VOA ( Variable Offset Algorithm) is presented. Experimental results had showed that VOA can significantly improve communication efficiency in dense WSN. |
| Starting Page | 1 |
| Ending Page | 10 |
| File Size | 467495 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781424472062 |
| ISSN | 10893539 |
| e-ISBN | 9781424472079 |
| DOI | 10.1109/TEST.2010.5699288 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-11-02 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Electrical and Electronic Engineering |
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