686 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
Accurate Range-based Indoor Localization Using PSO-Kalman Filter Fusion
Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion (PKFF) is proposed that minimizes the effects of NLOS on localization error without using additional sensors or profiling. Location estimates from a windowed Particle Swarm Optimization (PSO) and a dynamically adjusted KF are fused based on a weighted variance factor. PKFF achieved a 40% lower 90-percentile root-mean-square localization error (RMSE) over the standard least squares trilateration algorithm at 61 cm compared to 102 cm
Advancements In Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-The-Art
Growing apprehensions surrounding public safety have captured the attention
of numerous governments and security agencies across the globe. These entities
are increasingly acknowledging the imperative need for reliable and secure
crowd-monitoring systems to address these concerns. Effectively managing human
gatherings necessitates proactive measures to prevent unforeseen events or
complications, ensuring a safe and well-coordinated environment. The scarcity
of research focusing on crowd monitoring systems and their security
implications has given rise to a burgeoning area of investigation, exploring
potential approaches to safeguard human congregations effectively. Crowd
monitoring systems depend on a bifurcated approach, encompassing vision-based
and non-vision-based technologies. An in-depth analysis of these two
methodologies will be conducted in this research. The efficacy of these
approaches is contingent upon the specific environment and temporal context in
which they are deployed, as they each offer distinct advantages. This paper
endeavors to present an in-depth analysis of the recent incorporation of
artificial intelligence (AI) algorithms and models into automated systems,
emphasizing their contemporary applications and effectiveness in various
contexts
GNSS-free outdoor localization techniques for resource-constrained IoT architectures : a literature review
Large-scale deployments of the Internet of Things (IoT) are adopted for performance
improvement and cost reduction in several application domains. The four main IoT application
domains covered throughout this article are smart cities, smart transportation, smart healthcare, and
smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be
time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor
localization and received significant attention from the research community due to low-power,
low-cost, and long-range communication. In addition, its signals can be used for communication
and localization simultaneously. There are different proposed localization methods to obtain the
IoT relative location. Each category of these proposed methods has pros and cons that make them
useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization
methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated
this work and provided the following contributions: (1) definition of the main requirements and
limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most
relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey
the most relevant methods used within the IoT ecosystem for improving GNSS-free localization
accuracy, and (4) discussion covering the open challenges and future directions within the field.
Some of the important open issues that have different requirements in different IoT systems include
energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview
of research works that have been published between 2018 to July 2021 and made available through
the Google Scholar database.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
Indoor Positioning and Navigation
In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot
Intelligent strategies for mobile robotics in laboratory automation
In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots
Indoor occupant positioning system using active RFID deployment and particle filters
Abstract-This article describes a method for indoor positioning of human-carried active Radio Frequency Identification (RFID) tags based on the Sampling Importance Resampling (SIR) particle filtering algorithm. To use particle filtering methods, it is necessary to furnish statistical state transition and observation distributions. The state transition distribution is obstacle-aware and sampled from a precomputed accessibility map. The observation distribution is empirically determined by ground truth RSS measurements while moving the RFID tags along a known trajectory. From this data, we generate estimates of the sensor measurement distributions, grouped by distance, between the tag and sensor. A grid of 24 sensors is deployed in an office environment, measuring Received Signal Strength (RSS) from the tags, and a multithreaded program is written to implement the method. We discuss the accuracy of the method using a verification data set collected during a field-operational test
- …