Performance of IEEE 802.11 based WLAN devices under various jamming signals

In VANETs we trust?

“…Previous work [12,14] have carried out similar studies on 802.11bg devices, and similar trends and observations can be expected, despite the differences in the PHY design of 802.11g and 802.11p. Nevertheless, this first step was of paramount importance because: 1) it provided an indicator of the performance of 802.11p communications under the presence of jamming that can be expected in the field experiments with the devices at hand; 2) it helped decide which jamming patterns to select for outdoor measurements; 3) it allowed us (as discussed later in Section 6.3) to discover and understand relevant features of the used 802.11p hardware that might not be clear; 4) it is the first attempt, to the best of our knowledge, to study the robustness of an 802.11p device to RF jamming.…”

“…On the other hand, there is some related work on jamming at both MAC [6,21] and PHY [14] for generic 802.11 WLANs. [12] studied the vulnerabilities of 802.11b/g hardware to RF interference, which are associated to timing recovery and dynamic range selection issues.…”

“…Their results show that wide-band jamming damages OFDM-based 802.11g communications more severely (up to 7 dB) than they affect 802.11b (spread spectrum). The authors in [14] show that a constant wideband noise signal is more efficient (3-4 dB) than a constant wideband digitally modulated signal at disrupting 802.11g communications (16-QAM). The authors further show that the effectiveness of pulsed wide-band noise jamming (equivalent to random jamming with fixed on/off behaviour) is closely related to the pulse period and pulse duration.…”

Machine learning-based jamming detection for IEEE 802.11: Design and experimental evaluation

“…In this context, we face two issues: First, collecting training data requires effort and the existence of the jammer is mandatory. Second, there is an indeterminably large number of jamming attacks that can be obtained by changing the interference signal pattern [11], Detection accuracy achieved exclusively with training data from a particular attack. Interestingly, using only reactive jammer training data for detecting a constant jammer achieves a successful detection.…”

“…With the proliferation of (time-critical) machine-to-machine applications in general, and safety-critical applications in vehicular ad-hoc networks (VANETs) in particular, the importance of jamming-aware communications is expected to increase in the future. In general, the impact of jamming can be alleviated by either increasing the robustness of the legitimate signal [11], [15] or by migrating the communication to a different frequency band [14]. However, many of the proposed countermeasures cannot always be applied on already existing systems and, in most cases, the only alternative is to try to detect the jammer.…”