research-article

WSNs-assisted opportunistic network for low-latency message forwarding in sparse settings

Authors:

Giancarlo Fortino,

Yongsheng YangAuthors Info & Claims

Volume 91, Issue C

Pages 223 - 237

https://doi.org/10.1016/j.future.2018.08.031

Published: 01 February 2019 Publication History

Abstract

How to shorten time delay and enhance delivery ratio in sparse settings is still an open issue in the study of opportunistic networks. Most proposals are trying to deal with this issue by introducing infrastructures; however, although related research has been proved to be useful in improving the routing performance of the network, there is still room for further improvement. In this paper, inspired by the powerful message synchronization capability of wireless sensor networks (WSNs), we propose a new opportunistic network framework called WON that introduces WSNs into the opportunistic network. With the support of WSNs, low latency delivery and high delivery ratio can be achieved. To make the best use of WSNs, we present a comprehensive routing mechanism that is able to cover all the message-forwarding activities (i.e., mobile-to-mobile, mobile-to-stationary, stationary-to-mobile and stationary-to-stationary) in WON. To tackle the challenging issues caused by constrained energy and storage space, we develop a storage management scheme and a message control scheme. The experimental results show that WON can improve the success ratio while lowering the latency compared with the existing architectures. Furthermore, it is shown that WON is a highly effective solution for the rapid deployment of a low-latency communication network in the disaster-relief scenarios.

Highlights

•

A WSN-assisted opportunistic network framework called WON is proposed.

•

An opportunistic routing mechanism for WON is designed.

•

Schemes for storage management and message control are presented.

•

The performance of WON and proposed routing mechanism are evaluated.

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Published In

cover image Future Generation Computer Systems

Future Generation Computer Systems Volume 91, Issue C

Feb 2019

634 pages

ISSN:0167-739X

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2019

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https://dl.acm.org/doi/10.1007/s42979-024-02841-1
Sachdeva RDev A(2024)Kernel Extreme Learning Machine-Based Optimization Model for Routing in Opportunistic NetworksWireless Personal Communications: An International Journal10.1007/s11277-024-11298-7137:1(49-67)Online publication date: 1-Jul-2024
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