An Efficient Metric for Physical-layer Jammer Detection in Internet of Things Networks

An active jammer could severely degrade the communication quality for wireless networks. Since all wireless nodes openly access the shared media, the harsh effects are exaggerated by retransmission attempts of affected devices. Fast and precise detection of the jammer is of vital importance for heterogeneous wireless environments such as the Internet of things (IoT). It could activate a series of corrective countermeasures to ensure the robust operation of the network. In this paper, we propose a local, straightforward, and numerical metric called the number of jammed slots (NJS), by which we can quickly detect the presence of a jammer and identify the jammed nodes at the software level in broadcast networks. NJS calculation is carried out by a central node which collects the MAC-layer statuses of all wireless nodes in a periodical fashion. Our simulation results indicate that NJS outperforms current detection methods in terms of accuracy and precision

A mechanism for MPLS broadcast and its extension for multicast dense-mode support

Abstract. Due to the increasing multicast applications and high desire for its deployment, it seems that any new technology should support multicast. However; MPLS technology which enhances IP packet forwarding capability by using layer 2 switching still does not offer any special solution for multicast support. The main difficulty in supporting dense-mode multicast protocols like DVMRP and PIM-DM is lack of a broadcast mechanism in MPLS. We propose a genuine broadcast mechanism for MPLS in this paper. First, the proposed mechanism is based on a central broadcast label assigner called BLAC (Broadcast Label Assignment Center). Then, we propose a new dense-mode multicast protocol for MPLS as an extension to our broadcast mechanism. Our method consumes fewer labels compared to the existing proposals supporting dense-mode multicast groups and has smaller forwarding tables.

Physical-layer Jammer Detection in Multi-hop IoT Networks

The presence of a jammer in an IoT network severely degrades all communication efforts between adjacent wireless devices. The situation is getting worse due to retransmission attempts made by affected devices. Therefore, jammers must be detected or localized quickly to activate a series of corrective countermeasures so as to ensure the robust operation of the IoT network. This paper proposes a novel metric called the number of jammed slots (NJS). It can detect and localize both reactive and proactive jammers that follow arbitrary jamming attack patterns. NJS is applicable to all communication paradigms such as unicast, broadcast, and multicast. In NJS, the wireless medium status is monitored by IoT devices and summarized reports are sent to a central node. Then, the central node determines the jamming duration, the affected nodes, and the approximate location of the jammer(s). Also, the specificity, precision, and accuracy of NJS are at least 48%, 19%, and 20% better than the other state-of-the-art statistical methods, respectively. In addition, in terms of the detection time, NJS is four times faster when detecting an active jammer in the network. It can also localize the jammer with less jammer localization errors

An Efficient Metric for Physical-layer Jammer Detection in Internet of Things Networks

An active jammer could severely degrade the communication quality for wireless networks. Since all wireless nodes openly access the shared media, the harsh effects are exaggerated by retransmission attempts of affected devices. Fast and precise detection of the jammer is of vital importance for heterogeneous wireless environments such as the Internet of things (IoT). It could activate a series of corrective countermeasures to ensure the robust operation of the network. In this paper, we propose a local, straightforward, and numerical metric called the number of jammed slots (NJS), by which we can quickly detect the presence of a jammer and identify the jammed nodes at the software level in broadcast networks. NJS calculation is carried out by a central node which collects the MAC-layer statuses of all wireless nodes in a periodical fashion. Our simulation results indicate that NJS outperforms current detection methods in terms of accuracy and precision