International Journal of Computer Networks and Applications

Number V-7-I-4, 2020

93 - 102
Taylor Based Grey Wolf Optimization Algorithm (TGWOA) For Energy Aware Secure Routing Protocol

Authors: Robbi Rahim, S. Murugan, S. Priya, S. Magesh, R.Manikandan

Abstract

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Wireless Sensor Network (WSN) design to be efficient expects better energy optimization methods as nodes in WSN are operated only through batteries. In WSN, energy is a challenging one in the network during transmission of data. To overcome the energy issue in WSN, Taylor based Grey Wolf Optimization algorithm proposed, which is the integration of the Taylor series with Grey Wolf Optimization approach finding optimal hops to accomplish multi-hop routing. This paper shows the multiple objective-based approaches developed to achieve secure energy-aware multi-hop routing. Moreover, secure routing is to conserve energy efficiently during routing. The proposed method achieves 23.8% of energy, 75% of Packet Delivery Ratio, 35.8% of delay, 53.2% of network lifetime, and 84.8% of scalability.

103 - 115
Support Vector Regressive Dragonfly Optimized Shift Invariant Deep Neural Learning Based Handover for Seamless Data Delivery in Heterogeneous Network

Authors: D. Somashekhara Reddy, C. Chandrasekhar

Abstract

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Number of views: 152

In a Wireless Sensor Network (WSN), seamless mobility management can change the current mobile node’s location point to another network devoid of any link failure during communication. The seamless mobility system is very useful to detect the nearest base station over the wireless network without any distinct interference. In this paper, a novel technique called Support Vector Regressive Dragonfly Optimization based Shift Invariant Deep Neural Learning (SVRDO-SIDNL) is introduced for improving the seamless data transmission with minimum delay. The Shift Invariant Deep Neural Learning comprises of many layers to learn the series of input. For each layer, the different processes are carried out to accomplish the traffic optimized seamless data delivery. The input layer of the deep neural learning receives mobile nodes with coverage region and then is sent to the hidden layer. The mobile nodes' signal strength is analyzed by applying the support vector regression at the hidden layer. Then, the node with weak signal strength is identified and performs the handover. Through oppositional learned multi-objective dragonfly optimization technique, recognition of nearby attachment points with greater bandwidth availability is performed for the handover process. Then, the mobile node connection is altered from the existing attachment point to a new attachment point without losing connectivity. The simulation results reveal that the SVRDO-SIDNL technique offers a greater delivery rate, throughput with lesser packet loss at less delay.

116 - 125
Multipath Routing for Internet of Vehicles using Master of Controller in Road Awareness (MRMOC-IOV)

Authors: Piyush Chouhan, Swapnil Jain

Abstract

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Number of views: 160

Internet of Vehicles (IOV) is the recent advancement in the field of transportation which contains smart devices to make transportation easy and reliable. Road awareness also makes use of the internet of vehicles with the help of a master of control, so it needs a secure routing approach to provide the scalability to the architecture and to increase the flexibility of the system as well. This work proposed a multipath routing with extension (MDR-Ext) approach for road awareness by making use of the master of control (MOC) for establishing the network of vehicles. The system works by getting the frequent information of the vehicle details in the network by the brink controller (BC). The BC receives this data from vehicles that proceed to the MOC controller to calculate the shortest path for communication between vehicles with data packets. The path is interpreted by the MOC controller and path updating is done by the values receiving from BC at a regular interval. The proposed approach works well and the shattering of the network due to mobility is overcome through the BC. In this proposed approach packet delivery ratio could be achieved up to 95.7% better throughput up to 4244.456 kbps, low normalized routing overhead up to 8.88 bytes with less energy consumption up to 31.38 joules for multipath routing with extension approach. It is clear that MDR-EXT for MOC with the brink controller gives better performance as compared to TDR-Ext and DODR-Ext.