Precise Tracking of Things via Hybrid 3-D Fingerprint Database and Kernel Method Particle Filter

Bargshady, Nader; Garza, Gabe; Pahlavan, Kaveh

doi:10.1109/jsen.2016.2616758

Cited by 19 publications

(10 citation statements)

References 84 publications

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“…-based on the angle of signal arrival (AoA -Angle of Arrival) [8,9]; -based on the time of arrival of the signal (ToA -Time of Arrival and TD-A Time Difference of Arrival) [9][10][11]; -based on the power level of the received signal (RSSI -Received Signal Strength Indicator) [12,13].…”

Section: Materials and Methods For Determining The Location Of Wirelementioning

confidence: 99%

Improving the model of decision making about abnormal network state using a positioning system

Антипов¹,

Vasilenko²

2019

EEJET

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deteriorate the quality of the wireless network. First, the determination of a clear boundary between the normal and abnormal behavior of the system leads to a large number of false signals. Second, they are not adaptive to changes in the network operation conditions. Third, these systems do not take into consideration the location of wireless users [4]. The problem of a clear boundary is solved by using fuzzy logic, and the problem of network adaptabilityby taking into consideration the parameters affecting the network state (time of day, day of the week, season (vacation / not vacation), interferences). But this model does not take into consideration the location of users either. This means that part of the attacks will be skipped or detected already after the attacker has reached his goal.In [5], the author explores the adaptability of positioning methods (recent developments provide positioning accuracy up to 1 meter [6]) in issues related to security. Using simulation modeling, the effectiveness of indoor positioning methods is proved. However, it should be noted that the studied methods are used only to provide access to networks with different security requirements. This means that the identification of the attacks themselves is carried out using classic detection systems that do not have a positioning service.

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Section: Materials and Methods For Determining The Location Of Wirelementioning

confidence: 99%

Improving the model of decision making about abnormal network state using a positioning system

Антипов¹,

Vasilenko²

2019

EEJET

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“…The future directions in Wi-Fi positioning is in the integration of RSS signals with other sensor readings on smart devices (accelerometers for instance) to enhance the precision and flexibility of positioning. There are research works on integration of mechanical sensors such as accelerometer and gyroscope on robotic platforms with Wi-Fi positioning [64], there are works in integrating more precise UWB positioning with limited coverage with wider coverage Wi-Fi positioning [65], and frameworks for generalizing fingerprinting in multi-sensor environment [66,67]. Other researchers investigate submeter Wi-Fi positioning using Wi-Fi channel state information (CSI) [68].…”

Section: Emergence Of Location Intelligence From Wpsmentioning

confidence: 99%

Evolution and Impact of Wi-Fi Technology and Applications: A Historical Perspective

Pahlavan

Krishnamurthy

2020

Int J Wireless Inf Networks

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104

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The IEEE 802.11 standard for wireless local area networking (WLAN), commercially known as Wi-Fi, has become a necessity in our day-today life. Over a billion Wi-Fi access points connect close to hundred billion of IoT devices, smart phones, tablets, laptops, desktops, smart TVs, video cameras, monitors, printers, and other consumer devices to the Internet to enable millions of applications to reach everyone, everywhere. The evolution of Wi-Fi technology also resulted in the first commercial piloting of spread spectrum, high speed optical communications, OFDM, MIMO and mmWave pulse transmission technologies, which then became more broadly adopted by cellular phone and wireless sensor networking industries. The popularity and widespread Wi-Fi deployment in indoor areas further motivated innovation in opportunistic cyberspace applications that exploit the ubiquitous Wi-Fi signals. The RF signal radiated from Wi-Fi access points creates an "RF cloud" accessible to any Wi-Fi equipped device hosting or supporting these opportunistic applications. Wi-Fi positioning and location intelligence were the first popular opportunistic applications of Wi-Fi's RF cloud. Today, researchers are investigating opportunistic applications of Wi-Fi signals for gesture and motion detection as well as authentication and security. This paper provides a holistic overview of the evolution of Wi-Fi technology and its applications as the authors experienced it in the last few decades.

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“…These devices provide information on speed and direction of movements of the device. Using hybrid AI algorithms, we can integrate these motions related information with the absolute position estimate from the WPS to enhance the positioning and to refine the tracking in indoor areas [10], [18]- [20].…”

Section: ) Future Directions Of Wpsmentioning

confidence: 99%

RF Cloud for Cyberspace Intelligence

Pahlavan

Ying

et al. 2020

IEEE Access

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Wireless information networks have become a necessity of our day-today life. Over a billion Wi-Fi access points, hundreds of thousands of cell towers, and billions of IoT devices, using a variety of wireless technologies, create the infrastructure that enables this technology to access everyone, everywhere. The radio signal carrying the wireless information, propagates from antennas through the air and creates a radio frequency (RF) cloud carrying a huge amount of data that is commonly accessible by anyone. The big data of the RF cloud includes information about the transmitter type and addresses, embedded in the information packets; as well as features of the RF signal carrying the message, such as received signal strength (RSS), time of arrival (TOA), direction of arrival (DOA), channel impulse response (CIR), and channel state information (CSI). We can benefit from the big data contents of the messages as well as the temporal and spatial variations of their RF propagation characteristics to engineer intelligent cyberspace applications. This paper provides a holistic vision of emerging cyberspace applications and explains how they benefit from the RF cloud to operate. We begin by introducing the big data contents of the RF cloud. Then, we explain how innovative cyberspace applications are emerging that benefit from this big data. We classify these applications into three categories: wireless positioning systems, gesture and motion detection technologies, and authentication and security techniques. We explain how Wi-Fi, cell-tower, and IoT wireless positioning systems benefit from big data of the RF cloud. We discuss how researchers are studying applications of RF cloud features for motion, activity and gesture detection for human-computer interaction, and we show how authentication and security applications benefit from RF cloud characteristics.

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Precise Tracking of Things via Hybrid 3-D Fingerprint Database and Kernel Method Particle Filter

Cited by 19 publications

References 84 publications

Improving the model of decision making about abnormal network state using a positioning system

Improving the model of decision making about abnormal network state using a positioning system

Evolution and Impact of Wi-Fi Technology and Applications: A Historical Perspective

RF Cloud for Cyberspace Intelligence

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