Future wireless networks must incorporate awareness, adaptability, and intelligence as fundamental building elements in order to meet the wide range of requirements of the next-generation communication systems. Wireless sensing techniques can be used to gather awareness from the radio signals present in the surroundings. However, threats from hostile attackers, such as jamming, eavesdropping, and manipulation, are also present along with this. This paper describes in detail an RF-jamming detection testbed and provides experimentally measured data. The RF jamming detection testbed, in particular, makes use of the spectral scan capability of wireless network interfaces and JamRF, a jamming toolkit. In order to facilitate future progress in the experimentation of jamming detection and avoidance systems, we explain the methodology used for the development of our testbed and discuss the reasoning behind the choices made throughout its evolution. Furthermore, we provide various types of measurement data obtained with the testbed. The data set is expected to facilitate and promote the experimental evaluation of jamming detection, jamming avoidance, and anti-jamming schemes developed by the wireless security community. As an illustration, we trained five different machine learning algorithms and got results with an overall jamming detection accuracy of more than 90% for all algorithms.

The wireless networks of the future are evolving to enable reliable communication for resource-constrained, miniaturized internet of things (IoT) devices, which will place stringent demands on future sixth-generation (6G) mobile networks. These demands include low cost, very low latency, improved spectrum, and power efficiency, higher reliability, and significantly improved data rates. Emphasizing that these devices have limited functionality and may be placed in inaccessible locations, replacing or recharging batteries can be a daunting task, so energy-efficient solutions should be developed to ensure uninterrupted, seamless wireless communication for the power-constrained IoT devices. In this paper, we consider the integration of long-range (LoRa) modulation into backscatter communications (BackCom), and we develop a mathematical framework in order to investigate the error rate performance of the considered system model. In particular, we derive novel exact and approximated closed-form expressions for the symbol error rate (SER), under the assumption of canceled radio-frequency (RF) interference. The obtained analytical results, corroborated by numerical results, confirm the advantages of integrating LoRa into BackCom system as a low-complex technique in order to extend the transmission distance in power-limited backscatter devices.

Jamming attacks significantly degrade the performance of wireless communication systems and can lead to significant overhead in terms of re-transmissions and increased power consumption. Although different jamming techniques are discussed in the literature, numerous open-source implementations have used expensive equipment in the range of thousands of dollars with the exception of a few. These implementations have also tended to be partial band, and do not cover the whole available bandwidth of the system under attack. In this work, we demonstrate that flexible, reliable, and low priced software-defined radio (SDR) jamming is feasible by designing and implementing different types of jammers against IEEE 802.11n networks. First, to demonstrate the optimal jamming waveform, we present an analytical bit error rate expression of the system under attack by employing two common jamming waveforms: Gaussian noise and digitally modulated. Then, we validate this analysis through simulations using the MATLAB WLAN toolbox. Afterwards, we implement JamRF, a toolkit that employs a low-cost SDR to implement numerous types of jammers to validate the analysis. Obtained results showed that, to jam the whole 2.4GHz spectrum, a stateful-reactive jammer employing random channel hopping jamming strategy, achieves a packet loss ratio above 90%.