One of the main challenges in Wireless Sensor Networks is the limited energy of nodes which can cause to reduce the lifetime of nodes and whole network respectively. Transmissions between the nodes consumes most of the nodes' energy so minimization of unnecessary transmissions can led to reduction of energy consumption. Therefor routing protocols designed based on optimal energy consumption are necessary.
When the sensor nodes are deployed densely, data sensed by the nodes may, to some extent, be the same or similar and so are unnecessary. Energy dissipation and high traffic are costs that must be paid for transmission of repeated data from the source nodes to the base station. In order to solve these issues and optimize the energy consumption in communication, information gathering is considered as an effective technique. Redundancy in the raw data can be deleted in the relay nodes. The only useful information is retrieved and sent to the base station. Therefore, the number of data packets received at the base station, will be reduced, and thus save energy consumption and bandwidth.
Optimal gathering of data can be determined in term of total energy consumed to transfer data from nodes to the base station. Conventional routing protocols which use data collection techniques, can be divided into two categories based on network architecture: tree protocol and clustering protocols. In this study, we focus on the second type of network architecture, the tree protocol. In these protocols, sensor nodes are organized in a tree and aggregation is done at intermediate nodes in the junction tree branches. Gathered data packets are then transmitted to the root which is the base station. Tree protocols that are suitable for applications dealing with the aggregation of data within the network. One of the main characteristics of tree protocols is optimizing the data aggregation tree structure based on energy consumption which we do this benefiting a swarm intelligence algorithm called River Formation Dynamics. Simulation results show that our proposed algorithm will give better results in terms of network lifetime compared with ant colony algorithm.
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