Smart Mobile Gateway: Technical Challenges for Converged Wireless Sensor Networks and Mobile Cellular Networks

Shan, Lianhai; Fang, Weidong; Qiu, Yunzhou; He, Wei; Sun, Yanzan

doi:10.14257/ijfgcn.2016.9.9.08

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…In WSNs, the smart mobile user equipment (UE) gateway moves into the coverage area of the sensor nodes, broadcasts beacon packets to these nodes, and provides backhaul access to these WSN nodes. MCN can send the detected WSN data directly to a central data center [24]. The main disadvantages of WSNs include less mobility robustness, small coverage, and weak terminals.…”

Section: Proposed Iot/m2m-based Smart Building System Using Heterogen...mentioning

confidence: 99%

Adaptive Control of IoT/M2M Devices in Smart Buildings Using Heterogeneous Wireless Networks

et al. 2023

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With the rapid development of wireless communication technology, the Internet of Things (IoT) and Machine-to-Machine (M2M) are becoming essential for many applications. One of the most emblematic IoT/M2M applications is smart buildings. The current Building Automation Systems (BAS) are limited by many factors, including the lack of integration of IoT and M2M technologies, unfriendly user interfacing, high costs, using no more than one or two wireless communication networks, limited wireless transmission range, and the lack of a convergent solution. Therefore, this paper proposes a better approach of using heterogeneous wireless networks consisting of Wireless Sensor Networks (WSNs) and Mobile Cellular Networks (MCNs) for IoT/M2M smart building systems. The proposed system is an inexpensive embedded system that comprises Arduino and NodeMCU boards with several compatible sensors, actuators, and modules for controlling and collecting data over heterogeneous communication technologies (RFID, Bluetooth, Wi-Fi, GSM, LTE). All collected data is uploaded to the ThingSpeak platform, allowing the building system to be monitored via the ThingSpeak webpage or the Raniso app. One of the most significant outcomes of this research is to provide accurate readings to the server, and very low latency, through which users can easily control and monitor remotely the proposed system that consists of several innovative services, namely smart parking, garden irrigation automation, intrusion alarm, smart door, fire and gas detection, smart lighting, smart medication reminder, and indoor air quality monitoring. All these services are designed and implemented to control and monitor from afar the building via our free mobile application named "Raniso" which is a local server that allows remote control of the building via RFID/Bluetooth/Wi-Fi connectivity and cellular networks. This IoT/M2M smart building system is customizable to meet the needs of users, improving safety and quality of life while reducing energy consumption. Additionally, it helps prevent the loss of resources and human lives by detecting and managing risks.

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Section: Proposed Iot/m2m-based Smart Building System Using Heterogen...mentioning

confidence: 99%

Adaptive Control of IoT/M2M Devices in Smart Buildings Using Heterogeneous Wireless Networks

et al. 2023

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“…For the current study, network convergence via protocol conversion occurred in the WSN-MCN gateway (Swain & Ray, 2020). To minimize network delay (Xu et al, 2018) and processing complexity (Kaur & Manshahia, 2017;Shan et al, 2016), COA-A and COA-B designs relied on one-way, connectionless fire alert transmissions from remote WSNs to firefighters. The WSN-MCN gateway in the study also remained stationary instead of exercising mobility, significantly reducing the computational overhead of both the gateway and the detective WSN (Shan et al, 2016;Swain et al, 2018;Zhang et al, 2012).…”

Section: Data Collection Instrumentation and Proceduresmentioning

confidence: 99%

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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“…In this paper, we have focused on the data flow in the MAC layer of WSN and MCN convergence network for QoS management [20]. We have considered preemptive and non preemptive queuing model by giving emphasis on the priority of data packet to enter in to the queue.…”

Section: Figure 1 Data Flow In a Wsn-mcn Convergence Networkmentioning

confidence: 99%

QoS Management in WSN-MCN Convergence Network Using Priority Based Traffic Models

Swain¹,

Ray²

2018

IJEECS

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Technological advancements and increased developments leads to increase the development of hand-held devices which encourage the growth of Machine-to-Machine (M2M) communication. In this context devices belongs to heterogeneous network require a common platform to meet the challenges of data centric wireless services and applications. To satisfy the needs of M2M communication, Mobile Cellular Network (MCN) and Wireless Sensor Network (WSN) converged to a single platform. In this paper two priority models has been proposed with non preemptive priority and preemptive priority to analyze the data traffic at MAC layer of the converged network. The performance matrices are determined to maintain QoS in terms of reduced dropping and blocking probability, waiting time in the queue etc. Finally, the proposed models are compared in terms of QoS factors.

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Smart Mobile Gateway: Technical Challenges for Converged Wireless Sensor Networks and Mobile Cellular Networks

Abstract: With the continued requirement of internet of things (IoT)

Cited by 5 publications

References 23 publications

Adaptive Control of IoT/M2M Devices in Smart Buildings Using Heterogeneous Wireless Networks

Adaptive Control of IoT/M2M Devices in Smart Buildings Using Heterogeneous Wireless Networks

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

QoS Management in WSN-MCN Convergence Network Using Priority Based Traffic Models

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