An Empirical Analysis of Multi-Connectivity between 5G Terrestrial and LEO Satellite Networks

Lopez, Melisa; Damsgaard, Sebastian Bro; Rodríguez, Inés González; Mogensen, Preben

doi:10.1109/gcwkshps56602.2022.10008752

Cited by 13 publications

(12 citation statements)

References 8 publications

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“…To tackle the dynamic environments, our system needs realtime assessment capabilities through the rover connected using IoT to the farmers. Thanks to the 5G connectivity for providing the seamless internet provision in the rural areas [19], [20]. Here, the rover and controller are seamlessly connected through the utilization of IoT capabilities offered by ESP32 modules.…”

Section: Proposed Model Of the Long-range Surveillance Intelligent Ro...mentioning

confidence: 99%

Edge AI in Sustainable Farming: Deep Learning-Driven IoT Framework to Safeguard Crops From Wildlife Threats

Reddy,

Goutham

et al. 2024

IEEE Access

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The relentless global population growth and the ever-increasing food demand pose formidable challenges to the agricultural sector. Farmers grapple with the ongoing challenge of wildlifeinduced crop damage and human loss, which not only impedes food production but also exacerbates supply and demand imbalances. However, the rise of TinyML enables Edge AI as a promising avenue for implementing resource-efficient deep learning techniques on low-end edge devices. In this paper, we introduce an innovative solution that harnesses the power of Edge AI using tinyML-based deep learning algorithms in conjunction with the Internet of Things (IoT) for animal intrusion detection and deterrence system. The proposed system is developed to create remotely managed defense system tailored to safeguard vast agricultural expanses. It integrates a laser detection system and an AI-CAM with light weight deep learning algorithms for animal intrusion detection and classification. This system also ensures efficient animal deterrence and real-time monitoring for farmers, enabling them to assess the situation with the assistance of an intelligent rover build using IoT. This work emphasizes on proposing a light-weight deep learning model named EvoNet for animal classification. Results reveal that the proposed model achieves the highest accuracy at 96.7%, surpassing other models presented in this paper. However, for edge devices where compact file sizes are crucial, the model also offers comparable accuracy with file sizes as low as 1.63MB with the help of pruning and quantization techniques. This conceptualized solution has the potential to revolutionize agricultural wildlife management, ushering in a new era of crop protection and economic resilience.

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Section: Proposed Model Of the Long-range Surveillance Intelligent Ro...mentioning

confidence: 99%

Edge AI in Sustainable Farming: Deep Learning-Driven IoT Framework to Safeguard Crops From Wildlife Threats

Reddy,

Goutham

et al. 2024

IEEE Access

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“…Those technologies will allow 6G networks to provide global coverage, high data rates, and low latency [ 57 ]. Terrestrial technologies can provide coverage in urban areas, while non-terrestrial technologies can provide in rural areas and remote locations [ 58 , 59 , 60 ]. Non-terrestrial technologies could also provide high data rates and low latency, which is essential for applications such as virtual reality (VR) and augmented reality (AR) [ 61 ].…”

Section: 6g Visions and Requirementsmentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

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“…In our previous work [8], we presented a preliminary analysis of the performance of single-and multi-connectivity between a public 5G Non-Standalone (NSA) network and a LEO satellite network. Latency and throughput statistics were provided for a static test in a suburban area, showing the potential of using this multi-connectivity configuration to provide low latency and high throughput.…”

Section: Introductionmentioning

confidence: 99%

Connecting Rural Areas: an Empirical Assessment of 5G Terrestrial-LEO Satellite Multi-Connectivity

López,

Damsgaard,

Rodríguez

et al. 2023

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

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The digital transformation accomplished in recent years in the agricultural, farming, forestry, and transport industries has led to a series of emerging connectivity-based use cases that demand ubiquitous coverage. In such a scenario, Non-Terrestrial Networks (NTN), and their integration with Terrestrial Networks (TN), will play a key role to achieve global and seamless connectivity. This paper evaluates the current service provided by cellular terrestrial networks in rural areas and proposes and investigates a multi-connectivity solution for the use of TN and NTN to improve such service. Experimental data was gathered along a route covering more than 250 km in a rural area, using two multi-band cellular modems connected over public 5G non-standalone (NSA) cellular networks from two different operators and a SpaceX Starlink User Terminal (UT) connected to the Starlink Low Earth Orbit (LEO) satellite network. The multi-connectivity solution that we propose can be easily implemented, and results show that having an additional link to a satellite network avoids service degradation due to a lack of cellular coverage in up to 20 % of the route. The proposed solution also allows achieving round-trip latency targets of 100 ms with 99.99% reliability.

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An Empirical Analysis of Multi-Connectivity between 5G Terrestrial and LEO Satellite Networks

Cited by 13 publications

References 8 publications

Edge AI in Sustainable Farming: Deep Learning-Driven IoT Framework to Safeguard Crops From Wildlife Threats

Edge AI in Sustainable Farming: Deep Learning-Driven IoT Framework to Safeguard Crops From Wildlife Threats

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Connecting Rural Areas: an Empirical Assessment of 5G Terrestrial-LEO Satellite Multi-Connectivity

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