Open Access

ARTICLE

A Novel Multi-Hop Algorithm for Wireless Network with Unevenly Distributed Nodes

Yu Liu¹, Zhong Yang², Xiaoyong Yan³, Guangchi Liu⁴, Bo Hu^5,*

1 School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, China.
2 School of Intelligence Science and Control Engineering, Jinling Institute of Technology, Nanjing, 211169, China.
3 School of Modern Post & Institute of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China.
4 Stratifyd Inc., Charlotte, NC 28208, USA.
5 Center of Information Construction and Management, Nanjing Normal University of Special Education, Nanjing, 210038, China.

* Corresponding Author:* Correspondering Author: Bo Hu. Email: .

Computers, Materials & Continua 2019, 58(1), 79-100. https://doi.org/10.32604/cmc.2019.03626

Abstract

Node location estimation is not only the promise of the wireless network for target recognition, monitoring, tracking and many other applications, but also one of the hot topics in wireless network research. In this paper, the localization algorithm for wireless network with unevenly distributed nodes is discussed, and a novel multi-hop localization algorithm based on Elastic Net is proposed. The proposed approach is formulated as a regression problem, which is solved by Elastic Net. Unlike other previous localization approaches, the proposed approach overcomes the shortcomings of traditional approaches assume that nodes are distributed in regular areas without holes or obstacles, therefore has a strong adaptability to the complex deployment environment. The proposed approach consists of three steps: the data collection step, mapping model building step, and location estimation step. In the data collection step, training information among anchor nodes of the given network is collected. In mapping model building step, the mapping model among the hop-counts and the Euclidean distances between anchor nodes is constructed using Elastic Net. In location estimation step, each normal node finds its exact location in a distributed manner. Realistic scenario experiments and simulation experiments do exhibit the excellent and robust location estimation performance.

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Citations