1,530 research outputs found
Optimization Based Self-localization for IoT Wireless Sensor Networks
In this paper we propose an embedded optimization framework for the simultaneous self-localization of all sensors in wireless sensor networks making use of range measurements from ultra-wideband (UWB) signals. Low-power UWB radios, which provide time-of-arrival measurements with decimeter accuracy over large distances, have been increasingly envisioned for realtime localization of IoT devices in GPS-denied environments and large sensor networks. In this work, we therefore explore different non-linear least-squares optimization problems to formulate the localization task based on UWB range measurements. We solve the resulting optimization problems directly using non-linear-programming algorithms that guarantee convergence to locally optimal solutions. This optimization framework allows the consistent comparison of different optimization methods for sensor localization. We propose and demonstrate the best optimization approach for the self-localization of sensors equipped with off-the-shelf microcontrollers using state-of-the-art code generation techniques for the plug-and-play deployment of the optimal localization algorithm. Numerical results indicate that the proposed approach improves localization accuracy and decreases computation times relative to existing iterative methods
A survey on wireless indoor localization from the device perspective
With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while the wireless infrastructure deployed in the environment determines the targetâs location by analyzing its impact on wireless signals.
This article is intended to offer a comprehensive state-of-the-art survey on wireless indoor localization from the device perspective. In this survey, we review the recent advances in both modes by elaborating on the underlying wireless modalities, basic localization principles, and data fusion techniques, with special emphasis on emerging trends in (1) leveraging smartphones to integrate wireless and sensor capabilities and extend to the social context for device-based localization, and (2) extracting specific wireless features to trigger novel human-centric device-free localization. We comprehensively compare each scheme in terms of accuracy, cost, scalability, and energy efficiency. Furthermore, we take a first look at intrinsic technical challenges in both categories and identify several open research issues associated with these new challenges.</jats:p
Device-Free Human State Estimation using UWB Multi-Static Radios
We present a human state estimation framework that allows us to estimate the
location, and even the activities, of people in an indoor environment without
the requirement that they carry a specific devices with them. To achieve this
"device free" localization we use a small number of low-cost Ultra-Wide Band
(UWB) sensors distributed across the environment of interest. To achieve high
quality estimation from the UWB signals merely reflected of people in the
environment, we exploit a deep network that can learn to make inferences. The
hardware setup consists of commercial off-the-shelf (COTS) single antenna UWB
modules for sensing, paired with Raspberry PI units for computational
processing and data transfer. We make use of the channel impulse response (CIR)
measurements from the UWB sensors to estimate the human state - comprised of
location and activity - in a given area. Additionally, we can also estimate the
number of humans that occupy this region of interest. In our approach, first,
we pre-process the CIR data which involves meticulous aggregation of
measurements and extraction of key statistics. Afterwards, we leverage a
convolutional deep neural network to map the CIRs into precise location
estimates with sub-30 cm accuracy. Similarly, we achieve accurate human
activity recognition and occupancy counting results. We show that we can
quickly fine-tune our model for new out-of-distribution users, a process that
requires only a few minutes of data and a few epochs of training. Our results
show that UWB is a promising solution for adaptable smart-home localization and
activity recognition problems
Non-contact Multimodal Indoor Human Monitoring Systems: A Survey
Indoor human monitoring systems leverage a wide range of sensors, including
cameras, radio devices, and inertial measurement units, to collect extensive
data from users and the environment. These sensors contribute diverse data
modalities, such as video feeds from cameras, received signal strength
indicators and channel state information from WiFi devices, and three-axis
acceleration data from inertial measurement units. In this context, we present
a comprehensive survey of multimodal approaches for indoor human monitoring
systems, with a specific focus on their relevance in elderly care. Our survey
primarily highlights non-contact technologies, particularly cameras and radio
devices, as key components in the development of indoor human monitoring
systems. Throughout this article, we explore well-established techniques for
extracting features from multimodal data sources. Our exploration extends to
methodologies for fusing these features and harnessing multiple modalities to
improve the accuracy and robustness of machine learning models. Furthermore, we
conduct comparative analysis across different data modalities in diverse human
monitoring tasks and undertake a comprehensive examination of existing
multimodal datasets. This extensive survey not only highlights the significance
of indoor human monitoring systems but also affirms their versatile
applications. In particular, we emphasize their critical role in enhancing the
quality of elderly care, offering valuable insights into the development of
non-contact monitoring solutions applicable to the needs of aging populations.Comment: 19 pages, 5 figure
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
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