Improving The Performances of WSN Using Data Scheduler and Hierarchical Tree

Abstract

Users of data-intensive implementation needs intelligent services and schedulers that will provide models and strategies to optimize their data transfer jobs. Normally sensor nodes are connected to consecutive sensor nodes depending on frequent transmission. To enhance end-to-end data flow parallelism for throughput optimization in high speed WSNs. The major objective is to maximize the WSNs throughput, minimizing the model overhead, avoiding disputation among users and using minimum number of end-system resources. Data packets are broadcasted from sender node to target node. Though, all nodes operate concurrently in various communications, the analysis shows that more packet latencies are occurred and priority-based transmission tasks are performed. Then the proposed Bearing parallelism-based Data Scheduler (BPDS) is used for data scheduling to enhance the end-to-end throughput input parameter. Sensor nodes are fast working node, it verifies each and every node before allocating packet transmission for that node. Busy resources are monitored to inform the nodes that are in processing, based on the schedule it allocates various paths to particular node and monitors the node capacity. Sampling algorithm supports for fixing threshold value, based on the values, they are further allocated to communicate between channels. It assigns the routing path with minimum resources and reduces end to end delay, to improve throughput, and network lifetime.

Energy efficient data fusion approach using squirrel search optimization and recurrent neural network

“…Rather, the outputs value of surrounding sensor node was utilized together with confidence factor, i.e., 𝐶 𝑗𝑖 𝑦 𝑖𝑗 (𝑘). Hence, the RNN sensor node models were given as in [17], [28], [29]. The signal strength among node i and the neighbours determines the confidence factors for i [30]- [32].…”