Extending Internet Access Over LoRa for Internet of Things and Critical Applications

• URL: http://arxiv.org/abs/2501.03465v1
• Date: Tue, 07 Jan 2025 01:47:49 GMT
• Title: Extending Internet Access Over LoRa for Internet of Things and Critical Applications
• Authors: Atonu Ghosh, Devadeep Misra, Hirdesh Mewada,
• Abstract summary: ILoRa enables accessing Application Programming Interfaces (APIs) and web pages on the Internet over a LoRa backbone network.<n>This work proposes and implements ILoRa to extend the public Internet to disconnected areas for essential service delivery.
• Score: 0.8192907805418583
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: LoRa bridges the gap between remote locations and mainstream networks, enabling large-scale Internet of Things (IoT) deployments. Despite the recent advancements around LoRa, Internet access over this technology is still largely unexplored. Most existing solutions only handle packets within the local LoRa network and do not interact with web applications. This limits the scalability and the ability to deliver essential web services in disconnected regions. This work proposes and implements ILoRa to extend the public Internet to disconnected areas for essential service delivery. ILoRa enables accessing Application Programming Interfaces (APIs) and web pages on the Internet over a LoRa backbone network. It comprises a ILoRa coordinator code (ICN) and access point nodes (APNs). The ICN interfaces the LoRa network with the public Internet and interprets content. The APN tethers a WiFi hotspot to which devices connect and access the web content. This work further proposes data handling methods for ICNs and APNs. An actual hardware-based implementation validates the proposed system. The implementation achieves a throughput of 1.06 kbps tested for an Internet-based API returning JSON data of 930 B. Furthermore, the APN consumed approximately $0.162$A current, and the resource utilization on the ICN was minimal.

Related papers

• The Kubernetes Network Driver Model: A Composable Architecture for High-Performance Networking [0.0]
  Traditional networking struggles to meet the escalating demands of AI/ML and evolving Telco infrastructure.<n>This paper introduces Network Drivers (KNDs), a transformative, modular, and declarative architecture designed to overcome current imperative provisioning and API limitations.
  arXiv  Detail & Related papers  (2025-06-30T08:45:54Z)
• LoRaConnect: Unlocking HTTP Potential on LoRa Backbones for Remote Areas and Ad-Hoc Networks [26.152275462641168]
  We propose LoRaWeb, which enables web access over LoRa networks.<n>LoRaWeb hardware tethers a WiFi hotspot to which the client devices connect and access the web pages using a web browser.<n>LoRaWeb implements a synchronization procedure to address the challenges for effective message exchange between requesters and responders.
  arXiv  Detail & Related papers  (2025-01-05T07:41:53Z)
• I've Got 99 Problems But FLOPS Ain't One [70.3084616806354]
  We take an unconventional approach to find relevant research directions, starting from public plans to build a $100 billion datacenter for machine learning applications. We discover what workloads such a datacenter might carry and explore the challenges one may encounter in doing so, with a focus on networking research. We conclude that building the datacenter and training such models is technically possible, but this requires novel wide-area transports for inter-DC communication, a multipath transport and novel datacenter topologies.
  arXiv  Detail & Related papers  (2024-07-01T10:33:46Z)
• A Lightweight Security Solution for Mitigation of Hatchetman Attack in RPL-based 6LoWPAN [0.24578723416255752]
  The Internet of Things (IoT) has a significant rise in industries, and we live in the era of Industry 4.0. The conventional routing method is ineffective in networks with limited resource devices, lossy links, and slow data rates. This paper shows significant degradation in terms of network performance when an attacker exploits the Non-Storing feature of the Routing Protocol for Low Power and Lossy Networks (RPL)
  arXiv  Detail & Related papers  (2024-04-02T06:48:33Z)
• Prioritising Interactive Flows in Data Center Networks With Central Control [0.0]
  We deal with two problems relating to central controller assisted prioritization of interactive flow in data center networks. In the first part of the thesis, we deal with the problem of congestion control in a software defined network. We propose a framework, where the controller with its global view of the network actively participates in the congestion control decisions of the end TCP hosts.
  arXiv  Detail & Related papers  (2023-10-27T07:15:15Z)
• Security and Privacy of IP-ICN Coexistence: A Comprehensive Survey [18.841230080121118]
  This article provides the first comprehensive Security and Privacy (SP) analysis of the state-of-the-art IP-ICN coexistence architectures. Our analysis shows that most architectures fail to provide several SP features, including data and traffic flow confidentiality, availability, and anonymity of communication.
  arXiv  Detail & Related papers  (2022-09-06T22:25:17Z)
• Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs [64.26714148634228]
  congestion control (CC) algorithms become extremely difficult to design. It is currently not possible to deploy AI models on network devices due to their limited computational capabilities. We build a computationally-light solution based on a recent reinforcement learning CC algorithm.
  arXiv  Detail & Related papers  (2022-07-05T20:42:24Z)
• No Size Fits All: Automated Radio Configuration for LPWANs [5.7224176761305]
  Low power long-range networks like LoRa have become increasingly mainstream for Internet of Things deployments. We propose an alternative approach -- we allow network devices to transmit at any data rate they choose. Our gateway design, Proteus, runs a neural network architecture and is backward compatible with existing LoRa protocols.
  arXiv  Detail & Related papers  (2021-09-10T20:45:03Z)
• Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective [88.39981851247727]
  We propose a novel framework called training-free neural architecture search (TE-NAS) TE-NAS ranks architectures by analyzing the spectrum of the neural tangent kernel (NTK) and the number of linear regions in the input space. We show that: (1) these two measurements imply the trainability and expressivity of a neural network; (2) they strongly correlate with the network's test accuracy.
  arXiv  Detail & Related papers  (2021-02-23T07:50:44Z)
• Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks [96.24723959137218]
  We study an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network.
  arXiv  Detail & Related papers  (2020-11-25T12:51:59Z)
• Neural Architecture Search for Lightweight Non-Local Networks [66.49621237326959]
  Non-Local (NL) blocks have been widely studied in various vision tasks. We propose a Lightweight Non-Local (LightNL) block by squeezing the transformation operations and incorporating compact features. We also propose an efficient neural architecture search algorithm to learn an optimal configuration of LightNL blocks in an end-to-end manner.
  arXiv  Detail & Related papers  (2020-04-04T15:46:39Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.