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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Das, A. Gorthi, R.P. |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Comput. Sci. & Eng., LNM Inst. of Inf. Technol., Jaipur, India (Das, A.; Gorthi, R.P.) |
| Abstract | Routing in a Wireless Sensor Network (WSN) is an important function for performance of the system in terms of energy efficiency. The WSN nodes have limited computing and processing power. For successful delivery of information in the event/data driven network of sensor nodes, distributive and/or collaborative approach is having high importance. In this paper, we have presented a set of mechanisms by which routing decision and path optimization can be decided. In this paper we have described briefly about Fuzzy based routing, Neural Network and modified Q-Learning based routing. We have shown that choice of action may give reward in the long run. We have also presented a modified ant routing algorithm, where link probabilities are randomly assigned. The probabilities will update by the backward ant process. However, the updating process is different from that of traditional Ant colony optimization (ACO) based routing. Our results are useful in this area of research. |
| Starting Page | 35 |
| Ending Page | 38 |
| File Size | 226107 |
| Page Count | 4 |
| File Format | |
| e-ISBN | 9781467370165 |
| DOI | 10.1109/CICSyN.2015.17 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-06-03 |
| Publisher Place | Latvia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural Network Q learning Communication systems Wireless Sensor Network energy efficiency intelligent routing Fuzzy ACO Computational intelligence |
| Content Type | Text |
| Resource Type | Article |
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