Abstract
An energy consumption optimization algorithm based on ant colony algorithm is proposed for wireless sensor network. The proposed algorithm allows each node in wireless sensor network to save the distance and residual energy of neighbor nodes. Furthermore, in terms of probability selection of the nodes and the pheromone update, this algorithm focuses on the next hop node through the comparison of distance between the nodes and the residual energy, which ensures less possibility of nodes with low energy selected as the next hop. Therefore, the proposed algorithm improves energy load balancing, stability of wireless sensor network and, eventually, extends the life span of the wireless sensor network. The simulation results show that the improved ant colony algorithm avoids too much energy consumption of a certain local node resulting in more uniform energy consumption for each node.
Highlights
A wireless sensor network (WSN) is composed of many nodes with low computing and data processing capabilities
The proposed method utilizes the distance between the nodes and the residual energy of the nodes in order to select the hop node and assigns certain weights to the energy consumption and the pheromone concentration
If the residual energy of the node is less than 20% of the initial energy, the weight of the residual energy of the node increases in the selection probability highlighting the importance of the residual energy of the nodes
Summary
A wireless sensor network (WSN) is composed of many nodes with low computing and data processing capabilities. In recent years, it has been widely used in military, medical, and other fields. Selection of a shortest path for data transmission plays an important role in reducing the energy consumption of the nodes and improving the stability of the WSN. The ant colony algorithm was initially proposed by Dorigo[2] in accordance with the ants’ natural ability to find the shortest path between the nest and the food. The proposed algorithm eases the huge consumption of nodes in search of the best path and balances the WSN node energy consumption
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