| Title: | Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs |
| Author(s): | Li X; Keegan B; Mtenzi F; |
| Address: | "Department of Computer Science, Hefei University, Hefei 230601, China. d13128518@mydit.ie. School of Computing, Dublin Institute of Technology, Dublin 8, Ireland. d13128518@mydit.ie. School of Computing, Dublin Institute of Technology, Dublin 8, Ireland. brian.x.keegan@dit.ie. School of Computing, Dublin Institute of Technology, Dublin 8, Ireland. Fredrick.Mtenzi@dit.ie" |
| ISSN/ISBN: | 1424-8220 (Electronic) 1424-8220 (Linking) |
| Abstract: | "Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime" |
| Keywords: | ant colony optimisation artificial fish swarm algorithm hybrid routing protocol wireless sensor networks; |
| Notes: | "PubMed-not-MEDLINELi, Xinlu Keegan, Brian Mtenzi, Fredrick eng 16YQ08RC/Science Foundation for Youths of Hefei University/ Switzerland 2018/10/10 Sensors (Basel). 2018 Oct 8; 18(10):3351. doi: 10.3390/s18103351" |
