Improving energy consumption in wireless sensor networks using fruit fly algorithm and fuzzy logic.

Information transmitters in wireless sensor networks have limited storage and energy. One of the most critical issues in the design of these networks is the optimal use of energy since it is almost impossible to charge or replace batteries in sensor nodes. In order to solve energy limitations in sensor networks, the use of clustering algorithms can play an effective role. In fact, these algorithms help to balance the network load with proper clustering and selection of optimal cluster heads, which will reduce energy consumption and subsequently increase the network's lifespan. Accordingly, in this article, in order to select the best nodes as cluster heads, a new method based on the fruit fly algorithm and fuzzy logic is proposed. In the proposed protocol, fuzzy logic is used to calculate the odor intensity parameter in the fruit fly algorithm. Candidate nodes for clustering use the three parameters of the distance to the sink, the amount of remaining battery energy, and the distance to the center of the cluster as fuzzy logic input (to calculate the smell intensity). By simulating the proposed method and comparing it with the well-known AFSRP protocol, it can be seen that the proposed protocol has a much better performance in terms of energy consumption, data transmission delay, media access delay, and signal-to-noise ratio than AFSRP.