Enhanced end to end data flow parallelism for throughput optimization and reduction of energy usage in high speed wsn

Abstract

Wireless Sensor Networks (WSN) provides great benefits due to their low-cost, small-scale factor and smart sensor nodes. In recent years, wireless sensor networks have become an emerging technology in industries, environment monitoring, health care monitoring systems and so on. A WSN is deployed with a large number of sensor nodes in a wide geographical area. WSN contains a collection of network devices, which is called as the sensor nodes. These nodes collect information depending on the type of the application and transmit the data towards the sink node. When a large number of sensor nodes are engaged in transmitting data, there is a possibility of congestion in the network. However, a sensor node is a resourceconstrained device in terms of memory, bandwidth, and energy. These constraints impose congestion in the network, leading to a large number of packet drops, low throughput and significant wastage of energy due to retransmission. These drawbacks have to be addressed and reduced in a WSN. In addition, an energy-efficient routing which finds the best forwarding node for data transmission is also essential to increase the network lifetime. An Enhanced End-To-End Data-Flow Parallelism for Throughput Optimization in High-Speed Wireless Sensor Networks is presented in this research. Commonly, small sensor nodes are associated together with sensor nodes, in which, data packets are communicated from the sender node to the objective node. However all nodes operate simultaneously in different communications, the present analysis indicates more packet latencies happen and priority-based transmission assignments are performed. Thus a Bearing Parallelism based Data Scheduler (BPDS) fordata scheduling, to improve the end-to-end throughput input parameter has been proposed. Congestion is one of the critical issues in WSN because it has a direct impact on the energy efficiency of sensor nodes, and throughput. Congestion degrades the overall channel capacity and increases the packet loss rate. In order to handle these pro