Drone-Enabled Internet-of-Things Relay for Environmental Monitoring in Remote Areas Without Public Networks

Zhang, Minghu; Li, Xiaoqiong

doi:10.1109/jiot.2020.2988249

Cited by 61 publications

(37 citation statements)

References 39 publications

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“…To answer RQ 4, there are several indicators to measure the LoRa performance, such as RSSI, packet loss rate, and SNR. The best RSSI, packet loss rate, and SNR were obtained by Zhang et al [79] (-60 dBm), Yuan et al [89] (0%), and Godot et al [55] (3 dBm), respectively. Most of the real-time deployments of LoRa using UAVs reported in this study are limited by the UAV's payload and flying time.…”

Section: Discussionmentioning

confidence: 81%

“…Prototype basic validation in a relevant environment [47], [54], [67], [70] 6-7 Prototype demonstration in a relevant and operational environment [28], [29], [49], [53], [55]- [60], [68], [72], [73], [76], [77], [79] 8…”

Section: Discussionmentioning

confidence: 99%

“…MQTT [53] IEEE 802.11s [28] ES920GW as an Internet Gateway [47] 4G/LTE [29], [57], [70], [76], [77], [79] Wi-Fi [44], [68], [73], [74] Virtual Ethernet [49]…”

Section: Backhaul Communication Referencesmentioning

confidence: 99%

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A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

2021

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The term Internet of Things (IoT) has emerged in recent decades because this network revolutionizes almost every aspect of our daily life, including products such as smartphones and intelligent vehicles, and crucial tasks such as precision agriculture and environmental monitoring. Myriads of communication technologies have been developed to fulfill the two main features of the IoT: long-range transmissions and low power consumption. Long-range (LoRa) has become one of the vital parts of IoT communication. In this study, the real-time deployments of an unmanned aerial vehicle (UAV)-based LoRa communication network are systematically reviewed, with a focus on the communication setup and its reported performance. Importantly, the UAV-based LoRa communication network has a low bit rate connectivity to ensure the high reliability of connections, especially in applications that require long transmission ranges. This study provides recommendations for researchers on what research perspectives need to be explored when implementing UAVs for IoT-based LoRa communication. This study also describes publication trends related to UAV-based LoRa communication networks. A supplementary Excel file that contains the reported publications on UAV-based LoRa communication networks is included to show this publication trend.

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

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A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

2021

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“…Zhang et al [14] also provide a similar solution. They argue that public ground networks do not provide sufficient coverage, which has resulted in the problematic backhaul of monitoring data from remote areas of interest.…”

Section: Related Workmentioning

confidence: 89%

Improving Response Time for Crowd Management in Hajj

2021

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Flying Adhoc Network (FANET) is a particular type of Mobile Adhoc Network (MANET) that consists of flying drones or unmanned aerial vehicles (UAVs). MANETs are especially useful in rural and remote areas, where the lack of public networks necessitates data delivery through mobile nodes. Additionally, FANETs provide better coverage where there is a lack of roads. Generally, the goal of FANETs is to provide multimedia data to applications such as search and rescue operations, forest fire detection, surveillance and patrol, environmental monitoring, and traffic and urban monitoring. The above applications’ performance and efficiency depend on the quality and timely delivery of these essential data from an area of interest to control centers. This paper presents a Priority-based Routing Framework for Flying Adhoc Networks (PRoFFAN) for the expedited delivery of essential multimedia data to control centers. PRoFFAN reduces the FANET application’s response time by prioritizing the sending and forwarding of critical image data from the UAV to the control center. Our motivation application is crowd management; we believe that having important image features as early as possible will save lives and enhance the crowd’s safety and flow. We integrated PRoFFAN over the RPL routing layer of Contiki-NG’s IPv6 network stack. We used simulations in Cooja to demonstrate the benefit of PRoFFAN over conventional ZigBee.

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“…After years of the constant construction of Earth surface observational systems, massive quantities of observational data have been accumulated into big Earth data at a considerable scale. In particular, with the rapid development of technologies related to the Internet of Things (IoT) [ 11 , 12 ], observational systems on the Earth’s surface are becoming increasingly mature, with the quantity of observational data reaching the petabyte scale and the number of modes of observational data significantly increasing [ 11 , 13 , 14 ]. The rise of the IoT has made it possible to acquire Earth surface observational and monitoring data in real time.…”

Section: Introductionmentioning

confidence: 99%

River Basin Cyberinfrastructure in the Big Data Era: An Integrated Observational Data Control System in the Heihe River Basin

Guo

Zhang

Shang

et al. 2021

Sensors

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River basin cyberinfrastructure with the Internet of Things (IoT) as the core has brought watershed data science into the big data era, greatly improving data acquisition and sharing efficiency. However, challenges in analyzing, processing, and applying very large quantities of observational data remain. Given the observational needs in watershed research, we studied the construction of river basin cyberinfrastructure and developed an integrated observational data control system (IODCS). The IODCS is an important platform for processing large quantities of observational data, including automated collection, storage, analysis, processing, and release. This paper presents various aspects of the IODCS in detail, including the system’s overall design, function realization, big data analysis methods, and integrated models. We took the middle reaches of the Heihe River Basin (HRB) as the application research area to show the performance of the developed system. Since the system began operation, it has automatically received, analyzed, and stored more than 1.4 billion observational data records, with an average of more than 14 million observational data records processed per month and up to 21,011 active users. The demonstrated results show that the IODCS can effectively leverage the processing capability of massive observational data and provide a new perspective for facilitating ecological and hydrological scientific research on the HRB.

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Drone-Enabled Internet-of-Things Relay for Environmental Monitoring in Remote Areas Without Public Networks

Cited by 61 publications

References 39 publications

A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

Improving Response Time for Crowd Management in Hajj

River Basin Cyberinfrastructure in the Big Data Era: An Integrated Observational Data Control System in the Heihe River Basin

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