The Mobile Version of the Predicted Energy Efficient Bee-Inspired Routing (PEEBR)

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 (PEEBR1), into a random mobility model in its second version (PEEBR2). 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. Keywords—PEEBR; PEEBR-1; PEEBR-2; Energy Efficient Routing; Bee-inspired; Artificial Bee Colony (ABC) optimization; Random Mobility Model