Adaptive Chaotic Shuffled Frog Leaping Algorithm for QoS Routing in Wireless Image Sensor Networks

In order to effectively improve the efficiency of multi-constrained QoS routing and reduce the energy consumption of data on the transmission path an efficient routing algorithm needs to be designed. Aiming at the problem of constrained QoS routing, an adaptive chaotic shuffled frog leaping algorithm is designed, a graph theory model of wireless image sensor network is established, and a corresponding fitness function is derived to find the path with the least energy consumption. Added new adaptive operator and chaotic operator to improve the global search ability. In the simulation, the adaptive chaotic shuffled frog leap algorithm is compared with evolutionary algorithm and particle swarm optimization. The experimental results prove that compared with evolutionary algorithm and particle swarm optimization the adaptive chaotic shuffled frog leap algorithm can be effectively accelerate convergence speed and reduce the energy loss of data on the transmission path.

[1]  Yuansheng Lou,et al.  A Task Scheduling Algorithm Based on Genetic Algorithm and Ant Colony Optimization Algorithm with Multi-QoS Constraints in Cloud Computing , 2015, 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[2]  Danhong Zhang,et al.  Research on Improved Strategy of Shuffled Frog Leaping Algorithm , 2019, 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[3]  MengChu Zhou,et al.  A Transaction and QoS-Aware Service Selection Approach Based on Genetic Algorithm , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[4]  Priyanka Roy,et al.  Implementation of genetic algorithm and modified shuffled frog leaping algorithm for transmission loss minimum re-scheduling , 2011, 2011 Annual IEEE India Conference.

[5]  Junfeng Chen,et al.  An improved shuffled frog leaping algorithm for robot path planning , 2014, 2014 10th International Conference on Natural Computation (ICNC).

[6]  Ni Jianjun,et al.  An improved shuffled frog leaping algorithm for robot path planning , 2014, ICNC 2014.

[7]  Daniele Trinchero,et al.  Ad-hoc multilevel wireless sensor networks for distributed microclimatic diffused monitoring in precision agriculture , 2015, 2015 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet).

[8]  Ming Li An adaptive quantum genetic QoS routing algorithm for wireless sensor networks , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[9]  Amar Ramdane-Cherif,et al.  QoS multicast routing based on a hybrid quantum evolutionary algorithm with firefly algorithm , 2017, 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B).

[10]  Mithilesh Kumar,et al.  Optimization of Wireless Sensor Networks inspired by Small World Phenomenon , 2015, 2015 IEEE 10th International Conference on Industrial and Information Systems (ICIIS).

[11]  Xiaodan Zhang An Opposition-Based Chaos Shuffled Frog Leaping Algorithm , 2016, 2016 2nd International Conference on Frontiers of Signal Processing (ICFSP).