1,305 research outputs found
RFID Localisation For Internet Of Things Smart Homes: A Survey
The Internet of Things (IoT) enables numerous business opportunities in
fields as diverse as e-health, smart cities, smart homes, among many others.
The IoT incorporates multiple long-range, short-range, and personal area
wireless networks and technologies into the designs of IoT applications.
Localisation in indoor positioning systems plays an important role in the IoT.
Location Based IoT applications range from tracking objects and people in
real-time, assets management, agriculture, assisted monitoring technologies for
healthcare, and smart homes, to name a few. Radio Frequency based systems for
indoor positioning such as Radio Frequency Identification (RFID) is a key
enabler technology for the IoT due to its costeffective, high readability
rates, automatic identification and, importantly, its energy efficiency
characteristic. This paper reviews the state-of-the-art RFID technologies in
IoT Smart Homes applications. It presents several comparable studies of RFID
based projects in smart homes and discusses the applications, techniques,
algorithms, and challenges of adopting RFID technologies in IoT smart home
systems.Comment: 18 pages, 2 figures, 3 table
Smart Indoor Positioning/Location and Navigation: A Lightweight Approach
In this paper a new location indoor system is presented, which shows the position and orientation of the user in closed environments, as well as the optimal route to his destination through location tags. This system is called Labelee, and it makes easier the interaction between users and devices through QR code scanning or by NFC tag reading, because this technology is increasingly common in the latest smartphones. With this system, users could locate themselves into an enclosure with less interaction
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
Exploiting Redundancy for UWB Anomaly Detection in Infrastructure-Free Multi-Robot Relative Localization
Ultra-wideband (UWB) localization methods have emerged as a cost-effective
and accurate solution for GNSS-denied environments. There is a significant
amount of previous research in terms of resilience of UWB ranging, with
non-line-of-sight and multipath detection methods. However, little attention
has been paid to resilience against disturbances in relative localization
systems involving multiple nodes. This paper presents an approach to detecting
range anomalies in UWB ranging measurements from the perspective of multi-robot
cooperative localization. We introduce an approach to exploiting redundancy for
relative localization in multi-robot systems, where the position of each node
is calculated using different subsets of available data. This enables us to
effectively identify nodes that present ranging anomalies and eliminate their
effect within the cooperative localization scheme. We analyze anomalies created
by timing errors in the ranging process, e.g., owing to malfunctioning
hardware. However, our method is generic and can be extended to other types of
ranging anomalies. Our approach results in a more resilient cooperative
localization framework with a negligible impact in terms of the computational
workload
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