Security-Aware Routing on Wireless Communication for E-Health Records Monitoring Using Machine Learning

An ad hoc structure is self-organizing, self-forming, and system-free, with no nearby associations. One of the significant limits we must focus on in frameworks is leading. As for directions, we can send the packet or communications from the sender to the recipient node. AODV Routing Protocol, a short display that will make the tutorial available on demand. Machine Learning (ML) based IDS must be integrated and perfected to support the detection of vulnerabilities and enable frameworks to make intrusion decisions while ML is about their mobile context. This paper considers the combined effect of stooped difficulties along the way, problems at the medium get-right-of-area to impact layer, or pack disasters triggered by the remote control going off route. The AODV as the Routing MANET protocol is used in this work, and the process is designed and evaluated using Support Vector Machine (SVM) to detect the malicious network nodes.

EVALUATION OF THE PERFORMANCE OF RE-AODV ROUTING PROTOCOL WITH DIFFERENT MOBILITY MODELS IN WIRELESS SENSOR NETWORKS

This paper aims to present a detailed study of different mobility models applicable for Wireless Sensor Networks (WSN). Wireless Sensor Networks (WSN) have evolved dramatically in mobile networks, providing the key advantage of offering access to information without considering a user's spatial and topological characteristics. Due to the exponential advancement of the Internet and the development of small handheld devices as a source of connectivity and data sharing, the wireless network has almost exploded over the past few years. As a routing protocol for WSN in different studies, the Ad-hoc On-demand Distance-vector routing protocol (AODV) has shown better performance than different routing protocols. It offers quick adaptation to dynamic link conditions, low processing, low memory overheads, and low network utilization. To develop an optimized routing protocol, in our previous work, we had proposed an enhancement of the AODV routing protocol to increase the performance of the classic AODV protocol by optimizing the energy consumption and automatically maximizing the network lifetime. In this paper, we present a detailed study of mobility models applicable for WSN. We describe various mobility models representing mobile nodes whose movements are independent (individual mobility models) and dependent (group mobility models). Furthermore, we will focus on studying the behavior of our optimized version of AODV that we named RE-AODV with different existing mobility models so that we can, in the end, select the best mobility model. In terms of network efficiency, simulation results in this Work demonstrate that the type of mobility model used makes the difference and influences the behavior of nodes.

Spectrum Handoff Aware AODV Routing Protocol for Cognitive Radio Vehicular Ad Hoc Networks

End-to-end application performance and throughput of vehicular cognitive transport control protocol depend on how efficiently the segments (TCP header) are being transmitted from source to destination. One way to enhance the performance of vehicular cognitive TCP protocol is by reducing the packet drops in between the source and destination. In general, packet drops occur in between the source and destination of Cognitive Radio Vehicular Ad Hoc Network (CR-VANET) because of spectrum handoff or cognitive node handoff, or network congestion. In this paper, we focus on enhancing the performance of cognitive TCP protocol through “cognitive AODV routing protocol with the spectrum handoff mechanism.” In the proposed work, channel-route control messages of cognitive AODV routing protocol are updated with the support of spectrum handoff which helps to provide the backup opportunistic channel during PU active and helps to reduce the end-to-end spectrum handoff packet drops. Simulation results reveal that the overall performance of the vehicular cognitive TCP protocol with the proposed spectrum handoff aware cognitive AODV routing protocol is enhanced as compared to the existing cognitive TCP protocol.