The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network. 

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deploying FL in real-world Internet-of-Things (IoT) networks with hierarchies. Although existing works have proposed various approaches to account data heterogeneity, system heterogeneity, unexpected stragglers and scalibility, none of them provides a systematic solution to address all of the challenges in a hierarchical and unreliable IoT network. In this paper, we propose an asynchronous and hierarchical framework (Async-HFL) for performing FL in a common three-tier IoT network architecture. In response to the largely varied networking and system processing delays, Async-HFL employs asynchronous aggregations at both the gateway and cloud levels thus avoids long waiting time. To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level. Device selection module chooses diverse and fast edge devices to trigger local training in real-time while device-gateway association module determines the efficient network topology periodically after several cloud epochs, with both modules satisfying bandwidth limitations. We evaluate Async-HFL’s convergence speedup using large-scale simulations based on ns-3 and a network topology from NYCMesh. Our results show that Async-HFL converges 1.08-1.31x faster in wall-clock time and saves up to 21.6% total communication cost compared to state-of-the-art asynchronous FL algorithms (with client selection). We further validate Async-HFL on a physical deployment and observe its robust convergence under unexpected stragglers.

/pdf/async-hfl-efficient-and-robust-asynchronous-federated-2ef0mvh3.pdf

Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks

Terahertz (THz) is a promising technology for future wireless communication networks, particularly for 6G and beyond. The ultra-wide THz band, ranging from 0.1 to 10 THz, can potentially address the limited capacity and scarcity of spectrum in current wireless systems such as 4G-LTE and 5G. Furthermore, it is expected to support advanced wireless applications requiring high data transmission and quality services, i.e., terabit-per-second backhaul systems, ultra-high-definition streaming, virtual/augmented reality, and high-bandwidth wireless communications. In recent years, artificial intelligence (AI) has been used mainly for resource management, spectrum allocation, modulation and bandwidth classification, interference mitigation, beamforming, and medium access control layer protocols to improve THz performance. This survey paper examines the use of AI in state-of-the-art THz communications, discussing the challenges, potentials, and shortcomings. Additionally, this survey discusses the available platforms, including commercial, testbeds, and publicly available simulators for THz communications. Finally, this survey provides future strategies for improving the existing THz simulators and using AI methods, including deep learning, federated learning, and reinforcement learning, to improve THz communications.

/pdf/terahertz-meets-ai-the-state-of-the-art-2hataana.pdf

Terahertz Meets AI: The State of the Art

Robust Clustering Using Hyperdimensional Computing

The industrial edge generates an abundance of spatially distributed and dynamic data that needs to remain on-site for privacy and security reasons. Collaborative training at the edge can leverage this data to refine pre-trained models locally for specific industrial tasks and environments and have them adapt to local changes for enhanced performance, agility, and resilience. However, communication between the devices during training is a key bottleneck and is not modelled by existing frameworks such as MxNet, PyTorch and TensorFlow. This paper introduces DAI-NET, a co-simulation framework for examining communication and its associated costs, and provides results from an implementation using Python, OMNET++ and INET. To validate it and showcase its utility, the developed platform is applied in the analysis of (i) the performance and cost of collaboratively training a Multilayer Perceptron model, and (ii) the influence of computational heterogeneity. Communication costs generated during the training are captured at the device and system levels. In computationally heterogeneous clusters, the root cause of stragglers is exposed. In addition, the key performance contributors are identified to be a cluster's computation capability and the variation in the relative computation capabilities of its devices. This study is particularly useful for artificial intelligence of things (AIoT) systems, whose bandwidth and energy resources are limited. It lends the way for more practical research on communication-efficient algorithms, network protocols and architectures for the AIoT edge. 

DAI-NET: Toward communication-aware collaborative training for the industrial edge

Federated Learning (FL) is a widely adopted distributed learning paradigm for to its privacy-preserving and collaborative nature. In FL, each client trains and sends a local model to the central cloud for aggregation. However, FL systems using neural network (NN) models are expensive to deploy on constrained edge devices regarding computation and communication. In this demo, we present FedHD, a FL system using Hyperdimensional Computing (HDC). In contrast to NN, HDC is a brain-inspired and lightweight computing paradigm using high-dimensional vectors and associative memory. Our measurements indicate that FedHD is 3.2×, 3.2×, 5× better on performance, energy and communication efficiency respectively compared to NN-based FL systems whilst maintaining similar accuracy to the state of the art. Our code is available on GitHub1.

FedHD: federated learning with hyperdimensional computing

Federated Learning (FL) is a widely adopted distributed learning paradigm for to its privacy-preserving and collaborative nature. In FL, each client trains and sends a local model to the central cloud for aggregation. However, FL systems using neural network (NN) models are expensive to deploy on constrained edge devices regarding computation and communication. In this demo, we present FedHD, a FL system using Hyperdimensional Computing (HDC). In contrast to NN, HDC is a brain-inspired and lightweight computing paradigm using high-dimensional vectors and associative memory. Our measurements indicate that FedHD is 3.2×, 3.2×, 5× better on performance, energy and communication efficiency respectively compared to NN-based FL systems whilst maintaining similar accuracy to the state of the art. Our code is available on GitHub1. 

FedHD

In recent years, there has been a spike in interest in the field of federated learning (FL). As a result, an increasing number of federated learning algorithms have been developed. Large-scale deployments to validate these algorithms are often not feasible, resulting in a need for simulation tools which closely emulate real deployment conditions. Existing federated learning simulators lack complex network settings, and instead focus on data and algorithmic development. ns-3 is a discrete event network simulator, which has a plethora of models to represent network components and can simulate complex networking scenarios. In this paper, we present ns3-fl, which is a tool that connects an existing FL simulator, flsim, with ns-3 to produce a federated learning simulator that considers data, algorithm, and network. We first discuss the learning, network and power models used to develop our tool. We then present an overview of our implementation, including the Client/Server ns-3 applications and interprocess communication protocols. A real Raspberry Pi-based deployment is setup to validate our tool. Finally, we perform a simulation emulating FL training on 40 clients throughout the UCSD campus and analyze the performance of our tool, in terms of real clock execution time for various FL rounds.

/pdf/ns3-fl-simulating-federated-learning-with-ns-3-1nksdm8c.pdf

ns3-fl: Simulating Federated Learning with ns-3

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Kai Lee

Author Tools

Chat about Author

Papers

FedHD: federated learning with hyperdimensional computing

FedHD

ns3-fl: Simulating Federated Learning with ns-3