Low Power Wide Area Networks Operating in the ISM Band- Overview and Unresolved Challenges

Stoynov, Viktor; Poulkov, Vladimir; Valkova-Jarvis, Zlatka

doi:10.1007/978-3-030-23976-3_10

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…It is suitable for low data rate applications. Streetlights are an example of the Sigfox application [15,16].…”

Section: • Sigfoxmentioning

confidence: 99%

“…It requires low power and provides a 20-kbps data rate. The DASH7 network is similar to the star LoRa network; however, it commonly uses a tree topology [16]. It provides good results in applications that need low latency.…”

Section: • Sigfoxmentioning

confidence: 99%

“…Each cluster has four sensors. The cluster head is the last node in the cluster (sensor IDs 4,8,12,16,20,24,28,32,36,40,44,48). In the second hierarchical scenario, there are five clusters.…”

Section: Hierarchical Topology Calculationsmentioning

confidence: 99%

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Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems

et al. 2022

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In recent years, various applications have emerged requiring linear topologies of wireless sensor networks (WSN). Such topologies are used in pipeline (water/oil/gas) monitoring systems. The linear structure has a significant impact on network performance in terms of delay, throughput, and power consumption. Regarding communication efficiency, routing protocols play a critical role, considering the special requirements of linear topology and energy resources. Therefore, the challenge is to design effective routing protocols that can address the diverse requirements of the monitoring system. In this paper, we present various wireless communication technologies and existing leak detection systems. We review different routing protocols focusing on multi-hop hierarchical protocols, highlighting the limitations and design issues related to packet routing in linear pipeline leak detection networks. Additionally, we present a LoRa multi-hop model for monitoring aboveground oil pipelines. A set of model parameters are identified such as the distance between sensors. In addition, the paper determines some calculations to estimate traffic congestion and energy consumption. Several alternative model designs are investigated. The model is evaluated using different multi-hop communication scenarios, and we compare the data rate and energy to provide an energy-efficient and low-cost leak detection system.

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“…It is suitable for low data rate applications. Streetlights are an example of the Sigfox application [15,16].…”

Section: • Sigfoxmentioning

confidence: 99%

Section: • Sigfoxmentioning

confidence: 99%

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Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems

et al. 2022

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The applications of Internet of Things in the automotive industry: A review of the batteries, fuel cells, and engines

et al. 2022

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Multi-band sub-GHz technology recognition on NVIDIA’s Jetson Nano

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Shahid

Elsas

et al. 2020

2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)

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Low power wide area networks support the success of long range Internet of things applications such as agriculture, security, smart cities and homes. This enormous popularity, however, breeds new challenging problems as the wireless spectrum gets saturated which increases the probability of collisions and performance degradation. To this end, smart spectrum decisions are needed and will be supported by wireless technology recognition to allow the networks to dynamically adapt to the ever changing environment where fair coexistence with other wireless technologies becomes essential. In contrast to existing research that assesses technology recognition using machine learning on powerful graphics processing units, this work aims to propose a deep learning solution using convolutional neural networks, cheap software defined radios and efficient embedded platforms such as NVIDIA's Jetson Nano. More specifically, this paper presents low complexity near-real time multi-band sub-GHz technology recognition and supports a wide variety of technologies using multiple settings. Results show accuracies around 99%, which are comparable with state of the art solutions, while the classification time on a NVIDIA Jetson Nano remains small and offers real-time execution. These results will enable smart spectrum management without the need of expensive and high power consuming hardware. Index Terms-Sub-GHz, deep learning, Software-defined radio, low-cost devices I. INTRODUCTION The Internet of things (IoT) paradigm has grown exponentially in the past decade and continues this trend into the foreseeable future. At the beginning of 2020, IoT Analytics estimated that 9.5 billion devices are connected to the Internet and forecasts a growth of 28 billion devices by 2025 [1]. This is due to the proliferation of various IoT application areas such as security, tracking, agriculture, smart metering, smart cities and smart homes. To accommodate commercial deployment of such large number of devices, recently a number of IoT technologies were developed which are called low power wide area networks (LPWANs). These technologies offer very long communication ranges allowing to connect a large number of devices using limited infrastructure cost (e.g. by installing a small number of gateways). Example technologies include Sigfox, LoRA, IEEE 802.11ah, IEEE 802.11g, Dash7, Weightless, etc. These technologies operate in unlicensed sub-GHz band, typically 868 MHz in Europe and 915 MHz in North America. These radio frequencies offer good object penetration performance and can be used for a long range of communication, i.e., up to 15 km (for LoRa) [2]. However, due to

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Low Power Wide Area Networks Operating in the ISM Band- Overview and Unresolved Challenges

Cited by 4 publications

References 11 publications

Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems

Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems

The applications of Internet of Things in the automotive industry: A review of the batteries, fuel cells, and engines

Multi-band sub-GHz technology recognition on NVIDIA’s Jetson Nano

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