NIAINov 3, 2023

A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis

arXiv:2311.01842v16 citationsh-index: 13
Originality Synthesis-oriented
AI Analysis

This addresses routing efficiency for mobile ad hoc networks, but appears incremental as it builds on existing on-demand source routing systems.

The paper tackles the problem of finding optimal packet routing in mobile ad hoc networks (MANETs) by proposing an Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy, which improves network lifetime by reducing node energy consumption and end-to-end delay.

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors.

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