Enabling Seamless Device Association with DevLoc using Light Bulb Networks for Indoor IoT Environments

• URL: http://arxiv.org/abs/2005.07731v1
• Date: Fri, 15 May 2020 18:33:42 GMT
• Title: Enabling Seamless Device Association with DevLoc using Light Bulb Networks for Indoor IoT Environments
• Abstract summary: System named DevLoc takes advantage of ubiquitous light sources around us to perform continuous and seamless device grouping. We support two modes of device associations between different entities: device-to-device and device-to-area. We analyze patterns of device associations to improve the data privacy by recognizing semantic device groups, such as personal and stranger's devices.
• Score: 0.7519872646378835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To enable serendipitous interaction for indoor IoT environments, spontaneous device associations are of particular interest so that users set up a connection in an ad-hoc manner. Based on the similarity of light signals, our system named DevLoc takes advantage of ubiquitous light sources around us to perform continuous and seamless device grouping. We provide a configuration framework to control the spatial granularity of user's proximity by managing the lighting infrastructure through customized visible light communication. To realize either proximity-based or location-based services, we support two modes of device associations between different entities: device-to-device and device-to-area. Regarding the best performing method for device grouping, machine learning-based signal similarity performs in general best compared to distance and correlation metrics. Furthermore, we analyze patterns of device associations to improve the data privacy by recognizing semantic device groups, such as personal and stranger's devices, allowing automated data sharing policies.

Related papers

• Building a privacy-preserving Federated Recommender system for mobile devices [0.0]
  We present a two-stage federated recommendation system pipeline for mobile devices.<n>The first stage runs a collaborative filtering model on non-sensitive app-context data in the cloud to generate a shortlist of relevant items.<n>The second stage re-ranks these candidates on-device using sensitive mobile signals, with only model updates/gradients ever leaving the device.
  arXiv  Detail & Related papers  (2026-05-21T18:02:26Z)
• Indoor/Outdoor Spectrum Sharing Enabled by GNSS-based Classifiers [1.3682156035049033]
  We leverage the Global Navigation Satellite System (GNSS) signals for I/O classification.<n>Our results demonstrate that threshold-based techniques and machine learning approaches can achieve greater accuracy than approaches on wireless (Wi-Fi) data.
  arXiv  Detail & Related papers  (2025-09-30T16:43:59Z)
• LLM-Driven Auto Configuration for Transient IoT Device Collaboration [1.5479848902142663]
  CollabIoT employs a Large language Model (LLM)-driven approach to convert users' high-level intents to fine-grained access control policies.<n>We implement a prototype of CollabIoT's policy generation and auto configuration pipelines.<n>We show that our LLM-based policy generation pipeline is able to generate functional and correct policies with 100% accuracy.
  arXiv  Detail & Related papers  (2025-07-03T17:12:52Z)
• Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels [14.696896223432507]
  Federated learning (FL) is a popular solution for distributed machine learning (ML) tt CF-CL employs local device cooperation where either explicit (i.e., raw data) or implicit (i.e., embeddings) information is exchanged through device-to-device (D2D) communications.
  arXiv  Detail & Related papers  (2024-04-15T15:17:38Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey [3.219812767529503]
  Federated learning (FL) offers a privacy-preserving trade-off between communication overhead and model accuracy. We outline the challengesFL has to overcome to be widely applicable in real-world applications.
  arXiv  Detail & Related papers  (2023-07-18T12:05:36Z)
• Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange [21.789359767103154]
  Federated learning (FL) has been recognized as one of the most promising solutions for distributed machine learning (ML) We develop a novel methodology, Cooperative Federated unsupervised Contrastive Learning (CF-CL), for FL across edge devices with unlabeled datasets.
  arXiv  Detail & Related papers  (2022-08-04T19:26:59Z)
• Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point [51.47520726446029]
  cooperative edge learning (CE-FL) is a distributed machine learning architecture. We model the processes taken during CE-FL, and conduct analytical training. We show the effectiveness of our framework with the data collected from a real-world testbed.
  arXiv  Detail & Related papers  (2022-03-26T00:41:57Z)
• Federated Learning-based Active Authentication on Mobile Devices [98.23904302910022]
  User active authentication on mobile devices aims to learn a model that can correctly recognize the enrolled user based on device sensor information. We propose a novel user active authentication training, termed as Federated Active Authentication (FAA) We show that existing FL/SL methods are suboptimal for FAA as they rely on the data to be distributed homogeneously.
  arXiv  Detail & Related papers  (2021-04-14T22:59:08Z)
• Federated and continual learning for classification tasks in a society of devices [59.45414406974091]
  Light Federated and Continual Consensus (LFedCon2) is a new federated and continual architecture that uses light, traditional learners. Our method allows powerless devices (such as smartphones or robots) to learn in real time, locally, continuously, autonomously and from users. In order to test our proposal, we have applied it in a heterogeneous community of smartphone users to solve the problem of walking recognition.
  arXiv  Detail & Related papers  (2020-06-12T12:37:03Z)
• Wireless Communications for Collaborative Federated Learning [160.82696473996566]
  Internet of Things (IoT) devices may not be able to transmit their collected data to a central controller for training machine learning models. Google's seminal FL algorithm requires all devices to be directly connected with a central controller. This paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller.
  arXiv  Detail & Related papers  (2020-06-03T20:00:02Z)
• Canopy: A Verifiable Privacy-Preserving Token Ring based Communication Protocol for Smart Homes [10.744387940645712]
  We develop techniques that are based on a cryptographically secure token circulation in a ring network consisting of smart home devices. The solution hides the device activity and corresponding channel activities, and thus, preserve the individual's activities.
  arXiv  Detail & Related papers  (2020-04-08T06:57:01Z)
• Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge [84.55126371346452]
  We study federated learning at the wireless edge, where power-limited devices with local datasets collaboratively train a joint model with the help of a remote parameter server (PS) We design novel scheduling and resource allocation policies that decide on the subset of the devices to transmit at each round. The results of numerical experiments show that the proposed scheduling policy, based on both the channel conditions and the significance of the local model updates, provides a better long-term performance than scheduling policies based only on either of the two metrics individually.
  arXiv  Detail & Related papers  (2020-01-28T15:15:22Z)
• Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks [8.456633924613456]
  Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems. The paper proposes a fully distributed (or server-less) learning approach: the proposed FL algorithms leverage the cooperation of devices that perform data operations inside the network. The approach lays the groundwork for integration of FL within 5G and beyond networks characterized by decentralized connectivity and computing.
  arXiv  Detail & Related papers  (2019-12-27T15:16:04Z)

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.