In wireless sensor networks, congestion control is a major issue. When incoming packets increase beyond the actual ability of the network, otherwise node, it leads to congestion in the network. Network congestion can reduce bandwidth, increase network delay, as well as increase packet loss along with sensor power loss. Therefore, better methods are needed to deal with congestion. There is a need to understand congestion as well as supervise congestion resources in the wireless network to improve network performance. In recent years, various methods for recognizing and preventing congestion have been introduced. To address some of these issues, biodiversity monitoring system called LionFuzzyBee is proposed . The LionFuzzyBee is compared to a lion and a bee methods by a variety of metrics, for instance packet delivery ratio, detection accuracy, average throughput along with energy consumption etc . Evaluation outputs demonstrated that the LionFuzzyBee provides enhanced outputs than the Lion as well as Bee algorithm.
In wireless sensor networks (WSNs), congestion can occur due to more data traffic. This leads to increased packet delay, loss of packets, less accuracy. Transmission speed is a factor that contributes to the operation of WSN. We presented a mechanism for adjusting transmission speed to solve this problem. In this work, biodiversity-based monitoring system called BatFuzzyBee is presented. It defines WSN bio definitions that use location information to manage congestion. New cluster-based WSNs are also used in this work to reduce packet loss and save energy. BatFuzzyBee can find the best or closest reliable route, when the network has nodes with different transmission ranges with the least power consumption. The proposed BatFuzzyBee is compared to other methods by considering multiple indicators, such as packet delivery ratio, accuracy and energy consumption. The results from the simulation show that the proposed method gives better performance when compared with the other methods. In this way, the effectiveness of the BatFuzzyBee approach is determined.
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