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.
Highlights
Wireless Sensor Networks (WSNs) comprise a self-managed network system, which consist of numerous distributed sensor nodes
In our proposed hybrid routing algorithm based on Artificial Fish Swarm Algorithm (AFSA) and Ant Colony Optimisation (ACO), we use the heuristic information from AFSA as an initial pheromone value in the early stage in the ACO route discovery process, which is expected to avoid chaos and falling into a local optimum
ACO-based routing protocols are prone to premature, stagnation phenomenon, which hinders their further route discovery
Summary
Wireless Sensor Networks (WSNs) comprise a self-managed network system, which consist of numerous distributed sensor nodes. Researchers have utilised several schemes to select node clusters and to fuse the sensor data in each cluster and relay the data towards the base station These routing protocols combine energy-efficient cluster-based routing with application-specific data aggregation, achieve improved lifetime and achieve Quality of Service (QoS) requirements for WSNs [4,5]. An Energy-Efficient Ant-Based Routing Algorithm (EEABR) [10] considers the energy factor of wireless sensor nodes on the basis of an ACO mechanism to extend network lifetime. Many species in nature show similar self-organisation and decentralised behaviour to that of an ant colony Inspired by such behaviour, researchers presented many Swarm Intelligence (SI) optimisation algorithms [11].
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