Energy consumption is one of the constraints 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 root cause of more energy consumption and degrade the QoS in sensor networks. In WSN, there are several routing protocols which are used to enhance the performance of the network. Out of those protocols, Dynamic Source Routing (DSR) protocol is more suitable in terms of small energy density, but sometimes when the mode of a node changes from active to sleep, the efficiency 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 delay of the packets which leads to increase in energy consumption. Our problem is to identify the dead nodes and to choose another suitable path so that the data transmission becomes smoother and less energy gets conserved. 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 consumption 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 computing techniques.

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INTRODUCTION: Wireless Sensor Network (WSN) has caught the interest of researchers due to the rising popularity of Internet of things(IOT) based smart products and services. In challenging environmental conditions, WSN employs a large number of nodes with limited battery power to sense and transmit data to the base station(BS). Direct data transmission to the BS uses a lot of energy in these circumstances. Selecting the CH in a clustered WSN is considered to be an NP-hard problem. OBJECTIVES: The objective of this work to provide an effective cluster head selection method that minimize the overall network energy consumption, improved throughput with the main goal of enhanced network lifetime. METHODS: In this work, a meta heuristic based cluster head selection technique is proposed that has shown an edge over the other state of the art techniques. Cluster compactness, intra-cluster distance, and residual energy are taken into account while choosing CH using multi-objective function. Once the CHs have been identified, data transfer from the CHs to the base station begins. The residual energy of the nodes is finally updated during the data transmission begins. RESULTS: An analysis of the results has been performed based on average energy consumption, total energy consumption, network lifetime and throughput using two different WSN scenarios. Also, a comparison of the performance has been made other techniques namely Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Atom Search Optimization (ASO), Gorilla Troop Optimization (GTO), Harmony Search (HS), Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Biogeography Based Optimization (BBO). The findings show that AVOA's first node dies at round 1391 in Scenario-1 and round 1342 in Scenario-2 which is due to lower energy consumption by the sensor nodes thus increasing lifespan of the WSN network. CONCLUSION: As per the findings, the proposed technique outperforms ABC, ACO, ASO, GTO, HS, WHO, PSO, FA, and BBO in terms of performance evaluation parameters and boosting the reliability of networks over the other state of art techniques.

Although general quality models are available, it is possible to construct a custom quality model. The set of metrics obtained from such quality model can then be used to evaluate candidates, whether designs, architectures or systems using a quality computation model. This paper adapts a quality computation model for this purpose, and discusses an example to demonstrate the same.

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