Timing over a 4G (LTE) mobile network

Miškinis, Rimantas; Jokubauskis, Domas; Smirnov, Dmitrij; Urba, Emilis; Malysko, Bogdan; Dzindzeleta, Bronislovas; Svirskas, Kestutis

doi:10.1109/eftf.2014.7331543

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Keep-Alive messages are needed to generate enough traffic for the connection to avoid idle states [46]. The achievable accuracy is within tens of ms ( [46], [47]) with strong constraints on the power consumption and operational modes of the cellular radio. In scenarios where power consumption is a critical limitation, this solution is hardly applicable.…”

Section: Related Technologiesmentioning

confidence: 99%

Protecting GNSS-based Services using Time Offset Validation

Zhang

Spanghero

Papadimitratos

2020

2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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Global navigation satellite systems (GNSS) provide pervasive accurate positioning and timing services for a large gamut of applications, from Time based One-Time Passwords (TOPT), to power grid and cellular systems. However, there can be security concerns for the applications due to the vulnerability of GNSS. It is important to observe that GNSS receivers are components of platforms, in principle having rich connectivity to different network infrastructures. Of particular interest is the access to a variety of timing sources, as those can be used to validate GNSS-provided location and time. Therefore, we consider off-the-shelf platforms and how to detect if the GNSS receiver is attacked or not, by cross-checking the GNSS time and time from other available sources. First, we survey different technologies to analyze their availability, accuracy and trustworthiness for time synchronization. Then, we propose a validation approach for absolute and relative time. Moreover, we design a framework and experimental setup for the evaluation of the results. Attacks can be detected based on WiFi supplied time when the adversary shifts the GNSS provided time, more than 23.942 µs; with Network Time Protocol (NTP) supplied time when the adversary-induced shift is more than 2.046 ms. Consequently, the proposal significantly limits the capability of an adversary to manipulate the victim GNSS receiver.

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Section: Related Technologiesmentioning

confidence: 99%

Protecting GNSS-based Services using Time Offset Validation

Zhang

Spanghero

Papadimitratos

2020

2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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“…As shown in [33], the system meets four essential requirements. First, time synchronization is accurate enough to make sensors use identical peppers at identical time instants (at least when operating on networks with low latency, such as LTE networks [34]). Second, from the SA identifiers, it is realistically impossible to recover the original MAC addresses.…”

Section: B the Sensing Subsystemmentioning

confidence: 99%

Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

Determe

Azzagnuni

Singh

et al. 2022

IEEE Systems Journal

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This paper presents a crowd monitoring system based on the passive detection of probe requests. The system meets strict privacy requirements and is suited to monitoring events or buildings with a least a few hundreds of attendees. We present our counting process and an associated mathematical model. From this model, we derive a concentration inequality that highlights the accuracy of our crowd count estimator. Then, we describe our system. We present and discuss our sensor hardware, our computing system architecture, and an efficient implementation of our counting algorithm-as well as its space and time complexity. We also show how our system ensures the privacy of people in the monitored area. Finally, we validate our system using nine weeks of data from a public library endowed with a camera-based counting system, which generates counts against which we compare those of our counting system. This comparison empirically quantifies the accuracy of our counting system, thereby showing it to be suitable for monitoring public areas. Similarly, the concentration inequality provides a theoretical validation of the system.

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“…This requirement depends on the accuracy of time synchronization. We propose to use network time protocol (NTP), which implies accurate time synchronization on lowlatency networks (e.g., 4G networks, with timing errors lower than 10 ms [20]). There could be synchronization-related mismatches at the frontiers of consecutive one-minute time frames but only for 20 ms/60,000 ms = 0.033% of their duration.…”

Section: Requirement 3: Peppers Are Identical Across All Sensors At A...mentioning

confidence: 99%

MAC Address Anonymization for Crowd Counting

et al. 2022

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Research has shown that counting WiFi packets called probe requests (PRs) implicitly provides a proxy for the number of people in an area. In this paper, we discuss a crowd counting system involving WiFi sensors detecting PRs over the air, then extracting and anonymizing their media access control (MAC) addresses using a hash-based approach. This paper discusses an anonymization procedure and shows time-synchronization inaccuracies among sensors and hashing collision rates to be low enough to prevent anonymization from interfering with counting algorithms. In particular, we derive an approximation of the collision rate of uniformly distributed identifiers, with analytical error bounds.

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Timing over a 4G (LTE) mobile network

Cited by 6 publications

References 2 publications

Protecting GNSS-based Services using Time Offset Validation

Protecting GNSS-based Services using Time Offset Validation

Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

MAC Address Anonymization for Crowd Counting

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