Abstract
The mobile ad hoc network (MANET) is a kind of dynamic, easy to construct and universal network, which has been widely concerned by a large number of researchers. Graph theory provides an effective theoretical tool for MANETs modeling and analysis. Clustering is one of the most effective methods to measure network performance with different attributes. This paper gives the basic concept of graph kernel and discusses the principle of optimizing graph kernel and multi-graph kernel. In this paper, we propose a Graph Kernel based Clustering Algorithm in MANETs (GKCA). The GKCA algorithm gives the basic concept of graph kernel, discusses the principle of optimizing graph kernel and multi-graph kernel, and proposes the basic principle based on $d$ -hop graph kernel. GKCA algorithm uses shortest path (SP) to connect different cluster head nodes for packet transmission. The performance of GKCA algorithm, such as the control packets ratio, packets loss ratio, and average end-to-end delay are experimentally evaluated using network simulation (NS2) software. Experimental analysis shows that the proposed approach is efficient, and its performance advantage in dynamic mobile networks is promising.
Highlights
A mobile ad hoc network (MANET) is an Internet made up of mobile nodes that are not supported by any base stations or infrastructure
The purpose of this paper is to provide a complete knowledge of graph kernel theory, as well as the design of clustering algorithm
The major contributions of our work are as follows: Firstly, we develop the basic concept of graph kernel, discuss the principle of optimizing graph kernel and multi-graph kernel, and put forward a basic principle based on d-hop graph kernel
Summary
A mobile ad hoc network (MANET) is an Internet made up of mobile nodes that are not supported by any base stations or infrastructure. The famous postman problem [7] can be modeled as an undirected weighted graph, whose vertices are cities, edges are roads, and edge weights are road lengths It can be used for analyzing the valuable attributes hidden in the graph or mining information patterns, such as small world events, global optimization, etc. The authors attempt to analyze and discuss the clustering performance by using the graph kernel theory, considering the properties between the cluster-head node and the surrounding node of MANET. The purpose of this paper is to provide a complete knowledge of graph kernel theory, as well as the design of clustering algorithm. This paper is organized as follows: Section II reviews the research work of MANET and graph kernel.
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