1,032 research outputs found
Self-sustaining Ultra-wideband Positioning System for Event-driven Indoor Localization
Smart and unobtrusive mobile sensor nodes that accurately track their own
position have the potential to augment data collection with location-based
functions. To attain this vision of unobtrusiveness, the sensor nodes must have
a compact form factor and operate over long periods without battery recharging
or replacement. This paper presents a self-sustaining and accurate
ultra-wideband-based indoor location system with conservative infrastructure
overhead. An event-driven sensing approach allows for balancing the limited
energy harvested in indoor conditions with the power consumption of
ultra-wideband transceivers. The presented tag-centralized concept, which
combines heterogeneous system design with embedded processing, minimizes idle
consumption without sacrificing functionality. Despite modest infrastructure
requirements, high localization accuracy is achieved with error-correcting
double-sided two-way ranging and embedded optimal multilateration. Experimental
results demonstrate the benefits of the proposed system: the node achieves a
quiescent current of and operates at while performing
energy harvesting and motion detection. The energy consumption for position
updates, with an accuracy of (2D) in realistic non-line-of-sight
conditions, is . In an asset tracking case study within a
multi-room office space, the achieved accuracy level allows for identifying 36
different desk and storage locations with an accuracy of over . The
system`s long-time self-sustainability has been analyzed over in
multiple indoor lighting situations
Technology Implications of UWB on Wireless Sensor Network-A detailed Survey
In today’s high tech “SMART” world sensor based networks are widely used. The main challenge with wireless-based sensor networks is the underneath physical layer. In this survey, we have identified core obstacles of wireless sensor network when UWB is used at PHY layer. This research was done using a systematic approach to assess UWB’s effectiveness (for WSN) based on information taken from various research papers, books, technical surveys and articles. Our aim is to measure the UWB’s effectiveness for WSN and analyze the different obstacles allied with its implementation. Starting from existing solutions to proposed theories. Here we have focused only on the core concerns, e.g. spectrum, interference, synchronization etc.Our research concludes that despite all the bottlenecks and challenges, UWB’s efficient capabilities makes it an attractive PHY layer scheme for the WSN, provided we can control interference and energy problems. This survey gives a fresh start to the researchers and prototype designers to understand the technological concerns associated with UWB’s implementatio
Edge inference for UWB ranging error correction using autoencoders
Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Enhancing Near-Field Sensing and Communications with Sparse Arrays: Potentials, Challenges, and Emerging Trends
As a promising technique, extremely large-scale (XL)-arrays offer potential
solutions for overcoming the severe path loss in millimeter-wave (mmWave) and
TeraHertz (THz) channels, crucial for enabling 6G. Nevertheless, XL-arrays
introduce deviations in electromagnetic propagation compared to traditional
arrays, fundamentally challenging the assumption with the planar-wave model.
Instead, it ushers in the spherical-wave (SW) model to accurately represent the
near-field propagation characteristics, significantly increasing signal
processing complexity. Fortunately, the SW model shows remarkable benefits on
sensing and communications (S\&C), e.g., improving communication multiplexing
capability, spatial resolution, and degrees of freedom. In this context, this
article first overviews hardware/algorithm challenges, fundamental potentials,
promising applications of near-field S\&C enabled by XL-arrays. To overcome the
limitations of existing XL-arrays with dense uniform array layouts and improve
S\&C applications, we introduce sparse arrays (SAs). Exploring their potential,
we propose XL-SAs for mmWave/THz systems using multi-subarray designs. Finally,
several applications, challenges and resarch directions are identified
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