Abstract :
Energy consumption is one of the limitations in Wireless Sensor Networks (WSNs). The routing protocols are the hot areas to address quality-of-service (QoS) related issues viz. Energy consumption, network lifetime, network scalability and packet overhead. The key issue in WSN is that these networks suffer from the packet overhead, which is the core cause of more energy consumption and damage the QoS in sensor networks. In WSN, there are several routing protocols which are used to improve the performance of the network. Out of those protocols, Dynamic Source Routing (DSR) protocol is more appropriate in terms of small energy density, but sometimes when the mode of a node changes from active to sleep, the effectiveness decreases as the data packets needs to wait at the initial point where the packet has been sent and this increases the waiting time and end to end interruption of the packets which leads to increase in energy consumption. Our problem is to recognize the dead nodes and to choose another suitable path so that the data transmission becomes smoother and less energy gets preserved. In order to resolve these issues, we propose directional transmission-based energy aware routing protocol named as PDORP. The proposed protocol PDORP has the characteristics of both Power Efficient Gathering Sensor Information System (PEGASIS) and DSR routing protocols. In addition, hybridization of Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) is applied to proposed routing protocol to identify energy efficient optimal paths. The performance analysis, comparison through a hybridization approach of the proposed routing protocol gives better result comprising less bit error rate, less delay, less energy ingesting and better throughput which leads to better QoS and prolong the lifetime of the network. Moreover, the Computation Model is adopted to evaluate and compare the performance of the both routing protocols using soft calculating techniques.
Keyword :
Wireless Sensor Networks; DSR; PEGASIS; PDORP; OD-PRRP; LEACH; Optimization; Hybridization Computation Model.