شيماء قاسم محمد
Abstract
Radio frequency identification (RFID) is an automatic identification technique that facilitates data exchange and transmission between a reader and a tag using radio frequency waves. In large-scale environments, it is observed that RFID devices are susceptible to signal collision. This collision can occur between many entities, such as tags, readers, and the between readers and tags. Furthermore, the limited range of identification associated with it leads to increased expenses in terms of deployment. Hence, the primary concerns in organizations revolve around the elimination of RFID collision and the reduction of deployment costs.
This study introduces a hybrid BIRCH Chaotic Particle Swarm Optimization (BCPSO) algorithm that seeks to optimize four contradictory objective functions (maximizing tag coverage, minimizing readers interference, reducing power transmission consumption, and minimizing the variance of load balancing). The MATLAB R2020a package and the computer hardware specifications of Intel Core i5-1135G7 with 8.00 GB of RAM and CPU of 2.40GHz were used.
The proposed hybrid BIRCH Chaotic Particle Swarm Optimization (BCPSO) algorithm improves the coverage of tags by 100% with the fewest readers while avoiding interference. It also improves load balancing in three different cases with respect to the number of tags: case1 when the number of tags is 30 by 99.89%, 99.98%, 99.97% and 99.9%; case2 when the number of tags is 50 by 80.18%, 43.94%, 50.15%, and 70.39%; case3 when the number of tags is 100 by 63.47%, 23.90%, 51.67%, and 86.83% according to the comparison results between the proposed (BCPSO) algorithm and the (PSO, PS2O, VNPSO- RNP, and CSP-RNP) algorithms in [67], respectively. We also compared the proposed hybrid BCPSO algorithm with the HPSO-RNP algorithm in [17] and, the comparison results demonstrate that the proposed hybrid (BCPSO) algorithm improves the power transmission consumption in case2 when the number of tags is 50 by 54.60%, and 45.66% and case3 when the number of tags is 100 by 49.24%, and 42.58%, respectively.
