Optimized Node Clustering based on Received Signal Strength with Particle Ordered-filter Routing Used in VANET

VANET is a critical and demanding mission. Numerous methods exist, but none profits in a distributed fashion from physical layer parameters. This paper describes a method that enables individual nodes to estimate node density, independent of beacon messages, and other infrastructure-based information, of their surrounding network. In this paper, a discrete simulator of events was proposed to estimate the average number of simultaneously transmitting nodes, a functional channel model for the VANETs system, and a method of estimating node density. Proposed based on some equations to allow individual nodes to estimate their surrounding node density in real-time Optimized Node Cluster Algorithm with Network Density in which the composition of a cluster is triggered adjacent, these traffic signals is the same and has been predicated mostly on the position a vehicle might well take after crossing. Additional Ordered Tracking with Particle -Filter Routing in which receives simultaneous signal intensity versus node transmission and node density transmission. Conduct multiple location-related analyzes to test the plausibility of the neighboring single-hop nodes on mobility data. The system is designed to operate in the most complex situations where nodes have little knowledge of network topology and the results, therefore, indicate that the system is fairly robust and accurate.

A systematic investigation of the gap in various Barabási–Albert and Erdös–Rényi networks

On Barabási–Albert networks (BA) and variations as well as on Erdös–Rényi (ER) random graphs, we study the occurrence of a gap in the neighbor numbers [Formula: see text] versus node index [Formula: see text] (with [Formula: see text]) at [Formula: see text], 4, 6, 10, 50, 100 and 150 and with up to [Formula: see text] nodes. Here, we call “gap” a jump in the neighbor numbers [Formula: see text] when [Formula: see text] equals the initial number [Formula: see text] of neighbors; [Formula: see text] is also the number of neighbors randomly selected by a newly added node. The size of the gap depends on the value of [Formula: see text] and causes a deformation in the structure [Formula: see text] of the networks studied here. We give a systematic investigation of the gap in all types of BA networks known to us, and only the undirected BA (UBA) network and the ER graphs show no gap for [Formula: see text].