Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks

• URL: http://arxiv.org/abs/2112.12478v1
• Date: Thu, 23 Dec 2021 11:56:31 GMT
• Title: Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks
• Authors: Abdalla Elmokhtar Ahmed Elesawi and Kyeong Soo Kim
• Abstract summary: We propose hierarchical multi-building and multi-floor indoor localization based on a recurrent neural network (RNN) using Wi-Fi fingerprinting. The proposed scheme estimates building and floor with 100% and 95.24% accuracy, respectively, and provides three-dimensional positioning error of 8.62 m.
• Score: 2.0305676256390934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There has been an increasing tendency to move from outdoor to indoor lifestyle in modern cities. The emergence of big shopping malls, indoor sports complexes, factories, and warehouses is accelerating this tendency. In such an environment, indoor localization becomes one of the essential services, and the indoor localization systems to be deployed should be scalable enough to cover the expected expansion of those indoor facilities. One of the most economical and practical approaches to indoor localization is Wi-Fi fingerprinting, which exploits the widely-deployed Wi-Fi networks using mobile devices (e.g., smartphones) without any modification of the existing infrastructure. Traditional Wi-Fi fingerprinting schemes rely on complicated data pre/post-processing and time-consuming manual parameter tuning. In this paper, we propose hierarchical multi-building and multi-floor indoor localization based on a recurrent neural network (RNN) using Wi-Fi fingerprinting, eliminating the need of complicated data pre/post-processing and with less parameter tuning. The RNN in the proposed scheme estimates locations in a sequential manner from a general to a specific one (e.g., building->floor->location) in order to exploit the hierarchical nature of the localization in multi-building and multi-floor environments. The experimental results with the UJIIndoorLoc dataset demonstrate that the proposed scheme estimates building and floor with 100% and 95.24% accuracy, respectively, and provides three-dimensional positioning error of 8.62 m, which outperforms existing deep neural network-based schemes.

Related papers

• Hierarchical Stage-Wise Training of Linked Deep Neural Networks for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi RSSI Fingerprinting [3.86574270083089]
  We present a new solution to the problem of large-scale multi-building and multi-floor indoor localization based on linked neural networks. The hierarchical stage-wise training framework extends the original stage-wise training framework to the case of multiple linked networks. The proposed training framework can also significantly reduce the three-dimensional localization error from 11.78 m to 8.71 m.
  arXiv  Detail & Related papers  (2024-07-18T08:43:26Z)
• Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads [65.47816359465155]
  Running multiple deep neural networks (DNNs) in parallel has become an emerging workload in both edge devices. We propose Dysta, a novel scheduler that utilizes both static sparsity patterns and dynamic sparsity information for the sparse multi-DNN scheduling. Our proposed approach outperforms the state-of-the-art methods with up to 10% decrease in latency constraint violation rate and nearly 4X reduction in average normalized turnaround time.
  arXiv  Detail & Related papers  (2023-10-17T09:25:17Z)
• A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization [11.222977249913411]
  We provide a scheme to improve the accuracy of indoor fingerprint localization from the frequency domain. We tested our proposed scheme on COST 2100 simulation data and real time frequency division multiplexing (OFDM) WiFi data collected from an office scenario.
  arXiv  Detail & Related papers  (2023-09-19T08:19:34Z)
• Federated Learning based Hierarchical 3D Indoor Localization [1.5469452301122177]
  We present a federated learning (FL) framework for hierarchical 3D indoor localization using a deep neural network. We show that by adopting a hierarchical learning scheme, we can improve the localization accuracy by up to 24.06%. We also obtain a building and floor prediction accuracy of 99.90% and 94.87% respectively.
  arXiv  Detail & Related papers  (2023-03-01T12:21:00Z)
• On the Multidimensional Augmentation of Fingerprint Data for Indoor Localization in A Large-Scale Building Complex Based on Multi-Output Gaussian Process [3.8310036898137296]
  Wi-Fi fingerprinting becomes a dominant solution for large-scale indoor localization. The number and the distribution of Reference Points (RPs) for the measurement of localization fingerprints greatly affects the accuracy. Data augmentation has been proposed as a feasible solution to improve the smaller number and the uneven distribution of RPs.
  arXiv  Detail & Related papers  (2022-11-19T10:07:17Z)
• Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs [74.83613252825754]
  "smart ecosystems" are being formed where sensing happens concurrently rather than standalone. This is shifting the on-device inference paradigm towards deploying neural processing units (NPUs) at the edge. We propose a novel early-exit scheduling that allows preemption at run time to account for the dynamicity introduced by the arrival and exiting processes.
  arXiv  Detail & Related papers  (2022-09-27T15:04:01Z)
• Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization [3.8310036898137296]
  Location fingerprinting based on RSSI becomes a mainstream indoor localization technique due to its advantage of not requiring the installation of new infrastructure. The use of AI/ML technologies like DNNs makes location fingerprinting more accurate and reliable. We investigate three different methods of RSSI data augmentation based on Multi-Output Gaussian Process (MOGP), i.e., by a single floor, by neighboring floors, and by a single building. The feasibility of the MOGP-based RSSI data augmentation is demonstrated through experiments based on the state-of-the-art RNN indoor localization model and the UJI
  arXiv  Detail & Related papers  (2022-02-04T05:15:52Z)
• Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization [67.93423427193055]
  Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in beyond-5G and 6G communication technologies. In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain.
  arXiv  Detail & Related papers  (2021-11-23T22:25:10Z)
• A novel Deep Neural Network architecture for non-linear system identification [78.69776924618505]
  We present a novel Deep Neural Network (DNN) architecture for non-linear system identification. Inspired by fading memory systems, we introduce inductive bias (on the architecture) and regularization (on the loss function) This architecture allows for automatic complexity selection based solely on available data.
  arXiv  Detail & Related papers  (2021-06-06T10:06:07Z)
• Zero-Shot Multi-View Indoor Localization via Graph Location Networks [66.05980368549928]
  indoor localization is a fundamental problem in location-based applications. We propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization. GLN makes location predictions based on robust location representations extracted from images through message-passing networks. We introduce a novel zero-shot indoor localization setting and tackle it by extending the proposed GLN to a dedicated zero-shot version.
  arXiv  Detail & Related papers  (2020-08-06T07:36:55Z)
• Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment [84.57874289554839]
  Training deep neural networks on large-scale datasets requires significant hardware resources. Backpropagation, the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. We propose a neuro-biologically-plausible alternative to backprop that can be used to train deep networks.
  arXiv  Detail & Related papers  (2020-02-10T16:20:02Z)

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.