The Mobile Version of the Predicted Energy Efficient Bee-Inspired Routing (PEEBR)
Journal Title: International Journal of Advanced Research in Artificial Intelligence(IJARAI) - Year 2016, Vol 5, Issue 5
Abstract
In this paper, the previously proposed Predictive Energy Efficient Bee-inspired Routing (PEEBR) family of routing optimization algorithms based on the Artificial Bees Colony (ABC) Optimization model is extended from a random static mobility model, as employed by its first version (PEEBR-1), into a random mobility model in its second version (PEEBR-2). This random mobility model used by PEEBR-2 algorithm is proposed and described. Then, PEEBR-2’s was simulated in order to compare its performance relative to the first version (PEEBR-1) in terms of predicted optimal path energy consumption, nodes batteries residual power and fitness. The simulation results have shown that PEEBR-2’s optimal path is predicted to consume less energy and realizing higher fitness. On the other hand, PEEBR-1’s optimal paths nodes possess higher batteries residual power. At last, the impact of mobile nodes speeds was studied for PEEBR-2 in terms of optimal path’s predicted energy consumption and path nodes batteries residual power showing its performance stability relative to nodes mobility speed.
Authors and Affiliations
Imane Fahmy, Hesham Hefny, Laila Nassef
Keywords
Related Articles
- EP ID EP149094
- DOI 10.14569/IJARAI.2016.050505
- Views 112
- Downloads 0
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