5,129 research outputs found
A Survey of Positioning Systems Using Visible LED Lights
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe
Technologies and solutions for location-based services in smart cities: past, present, and future
Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
Echo State Learning for Wireless Virtual Reality Resource Allocation in UAV-enabled LTE-U Networks
In this paper, the problem of resource management is studied for a network of
wireless virtual reality (VR) users communicating using an unmanned aerial
vehicle (UAV)-enabled LTE-U network. In the studied model, the UAVs act as VR
control centers that collect tracking information from the VR users over the
wireless uplink and, then, send the constructed VR images to the VR users over
an LTE-U downlink. Therefore, resource allocation in such a UAV-enabled LTE-U
network must jointly consider the uplink and downlink links over both licensed
and unlicensed bands. In such a VR setting, the UAVs can dynamically adjust the
image quality and format of each VR image to change the data size of each VR
image, then meet the delay requirement. Therefore, resource allocation must
also take into account the image quality and format. This VR-centric resource
allocation problem is formulated as a noncooperative game that enables a joint
allocation of licensed and unlicensed spectrum bands, as well as a dynamic
adaptation of VR image quality and format. To solve this game, a learning
algorithm based on the machine learning tools of echo state networks (ESNs)
with leaky integrator neurons is proposed. Unlike conventional ESN based
learning algorithms that are suitable for discrete-time systems, the proposed
algorithm can dynamically adjust the update speed of the ESN's state and,
hence, it can enable the UAVs to learn the continuous dynamics of their
associated VR users. Simulation results show that the proposed algorithm
achieves up to 14% and 27.1% gains in terms of total VR QoE for all users
compared to Q-learning using LTE-U and Q-learning using LTE
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