172 research outputs found
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives
Efficient localization plays a vital role in many modern applications of
Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would
contribute to improved control, safety, power economy, etc. The ubiquitous 5G
NR (New Radio) cellular network will provide new opportunities for enhancing
localization of UAVs and UGVs. In this paper, we review the radio frequency
(RF) based approaches for localization. We review the RF features that can be
utilized for localization and investigate the current methods suitable for
Unmanned vehicles under two general categories: range-based and fingerprinting.
The existing state-of-the-art literature on RF-based localization for both UAVs
and UGVs is examined, and the envisioned 5G NR for localization enhancement,
and the future research direction are explored
Soft information for localization-of-things
Location awareness is vital for emerging Internetof-
Things applications and opens a new era for Localizationof-
Things. This paper first reviews the classical localization
techniques based on single-value metrics, such as range and
angle estimates, and on fixed measurement models, such as
Gaussian distributions with mean equal to the true value of the
metric. Then, it presents a new localization approach based
on soft information (SI) extracted from intra- and inter-node
measurements, as well as from contextual data. In particular,
efficient techniques for learning and fusing different kinds of SI
are described. Case studies are presented for two scenarios in
which sensing measurements are based on: 1) noisy features
and non-line-of-sight detector outputs and 2) IEEE 802.15.4a
standard. The results show that SI-based localization is highly
efficient, can significantly outperform classical techniques, and
provides robustness to harsh propagation conditions.RYC-2016-1938
DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection
Recent advancements in Internet of Things (IoTs) have brought about a surge
of interest in indoor positioning for the purpose of providing reliable,
accurate, and energy-efficient indoor navigation/localization systems. Ultra
Wide Band (UWB) technology has been emerged as a potential candidate to satisfy
the aforementioned requirements. Although UWB technology can enhance the
accuracy of indoor positioning due to the use of a wide-frequency spectrum,
there are key challenges ahead for its efficient implementation. On the one
hand, achieving high precision in positioning relies on the
identification/mitigation Non Line of Sight (NLoS) links, leading to a
significant increase in the complexity of the localization framework. On the
other hand, UWB beacons have a limited battery life, which is especially
problematic in practical circumstances with certain beacons located in
strategic positions. To address these challenges, we introduce an efficient
node selection framework to enhance the location accuracy without using complex
NLoS mitigation methods, while maintaining a balance between the remaining
battery life of UWB beacons. Referred to as the Deep Q-Learning
Energy-optimized LoS/NLoS (DQLEL) UWB node selection framework, the mobile user
is autonomously trained to determine the optimal set of UWB beacons to be
localized based on the 2-D Time Difference of Arrival (TDoA) framework. The
effectiveness of the proposed DQLEL framework is evaluated in terms of the link
condition, the deviation of the remaining battery life of UWB beacons, location
error, and cumulative rewards. Based on the simulation results, the proposed
DQLEL framework significantly outperformed its counterparts across the
aforementioned aspects
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