Modeling and analysis of data flow in MAC layer of WSN-MCN convergence network

Swain, Anita; Ray, Arun Kumar; Swain, Prasanta Kumar

doi:10.1109/scopes.2016.7955693

Cited by 2 publications

(3 citation statements)

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“…The sparse and underdeveloped SE Colorado communication infrastructure between remote grassfire detective technology and fire stations exacerbates risk (Crosby & Vafa, 2013;DOLA, 2019;nPerf, 2021). The literature review's latter component described research that offers fertile ground for a converged WSN-MCN solution (Swain & Ray, 2020;Swain et al, 2016).…”

Section: Review Of the Literaturementioning

confidence: 99%

“…Wireless sensor networks (WSN) are ideal tools to address SE Colorado's grassfire threat thanks to their ability to detect fires (Kadir et al, 2018) and the ease with which one can deploy them (Swain et al, 2016). WSNs form the essential elements of many wildfire detection and alerting systems (Kaur & Manshahia, 2017).…”

Section: Review Of the Literaturementioning

confidence: 99%

“…An MCN tower within range of a fire-detection WSN's last-hop node offers the ability to propel a remote fire alert to firefighters. MCNs boast wide network coverage areas, robust endpoints, and resilient networks with many capabilities (Swain et al, 2016). The 4G-LTE infrastructure prevalent throughout rural SE Colorado (nPerf, 2021), complemented by its extensive transmission range (Cox, 2012, p. 302), provided an ideal mechanism for WSN-MCN convergence.…”

Section: Review Of the Literaturementioning

confidence: 99%

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A Cybersecurity Study: Examining the Relationship between Converged-Network Architecture and Remote Grassfire Alert Transmission Delay in Southeast Colorado

P Wagoner,

Sambasivam

2022

InSITE Conference

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Aim/Purpose: The purpose of the present study was to examine the relationship between a converged wireless-sensor/cellular network architecture and cybersecurity, in terms of transmission delay, to deliver remote grassfire alerts to firefighters in Southeast (SE) Colorado. Background: Agriculture, rural communities, a thriving cattle industry, and a kaleidoscope of flora and fauna characterize the plains of SE Colorado. Unfortunately, the hot and dry climate of SE Colorado combines with the ever-expanding wildland-urban interface (WUI), presenting an enormous grassland fire risk. A review of the literature revealed a deficit of research that addressed the alerting mechanism between remote WSN-based fire detection and response forces. Methodology: The present research pursued a converged-network solution from two courses of action (COA) to address the wildfire risk. COA-A and COA-B coupled the ZigBee-Pro and ZigBee-IP WSN protocols, respectively, with the 4G-LTE infrastructure prevalent throughout SE Colorado to bolster alert information availability. Following construction of the simulation models, the Ostinato packet generator performed 194 end-to-end transmissions with each COA to ascertain the better-performing solution in terms of network transmission delay. Contribution: The study’s findings offer a starting point for subsequent research that will lead to a proposal for SE Coloradans – and beyond – to help bridge the gap between detective WSNs and the response forces that can subdue remote grassfires. To the extent the authors could surmise, the current research effort was the first to model and simulate a one-way, UDP-based wireless network that comprised a WSN node, two WSN-Cellular gateway designs, and several 4G-LTE infrastructure components. The simulated environment also measured and compared the end-to-end network transmission delay for two unique solutions, including the convergence process within the WSN-Cellular gateway. Findings: COA-B proved the superior solution with a 16.2% delay improvement over COA-A. An independent-samples t-test confirmed the statistical significance between the results’ means. Recommendations for Practitioners: COA-B offered a remote SE Colorado grassfire alerting solution that minimized network transmission delay and maximized alert payload to responding firefighters. However, the generalizability of the current research’s results indicates utility for firefighters providing overwatch to grasslands throughout the world – wherever valuable grasslands intersect with a 4G-LTE on-ramp. Within the United States and outside SE Colorado, 4G-LTE from multiple carriers exists throughout the Great Plains. U.S. industries, communities, and ecosystems that rely on the abundance of Great Plains grasslands abound and feature use cases ripe for benefit from the present research. Recommendations for Researchers: Additional studies could offer more depth and recommend solutions to bolsters the alert mechanism between fire detection and response capabilities. The literature is teeming with research that improves the efficacy of fire-detective WSNs. However, the dearth of practice-oriented research that delivers an alert to firefighters in SE Colorado and elsewhere warrants further work on top of the present study. Impact on Society: The study’s findings need not apply only to grassfires. Much research and residual challenges exist on the topic of forest and wildfire alerts throughout the world. Although the generation mechanisms may differ, propelling an alert over available infrastructure – 4G-LTE or other – offers a workable solution to ensure timely response to unsolicited fires. Future Research: The current research’s incremental construction of delay measurements for COA-A and COA-B encourages the creation of an end-to-end model in network simulators such as NS3 or OMNeT++. A network simulation framework like OMNeT++ would allow a more comprehensive representation of wireless channel effects on overall delay. The creation and testing of a physical COA-B prototype would provide a proof of concept for the current study. Future work could bridge the gap between any varietal of remote sensor network and the audience that consumes sensor data anywhere in the world.

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Section: Review Of the Literaturementioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

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