This article investigates the feasibility of 5G ultra-reliable low-latency communication (URLLC) for wide-area protection in distribution systems with distributed energy resources (DER). Simulations conducted using ns-3 demonstrate that URLLC can meet the stringent latency requirements of wide-area protection, even under mixed traffic conditions. Based on these findings, experiments are conducted on the platform for open wireless data-driven experimental research (POWDER) platform, using commercial off-the-shelf (COTS) hardware and open-source software stacks. Long-term wireless experiments on POWDER evaluate the impact of network slicing and 5G quality of service identifiers on the network performance. The behavior of best-effort users when sharing the same infrastructure as URLLC UEs is studied. Based on these experiments, significant discrepancies between simulation and hardware implementation performance are identified and analyzed. Finally, critical measures are discussed to align 5G network performance with the requirements of wide-area protection.

Unencrypted broadcast data in cellular networks is vulnerable to signal injection attacks that are capable of revealing sensitive information and disrupting critical services. Existing detection methods struggle with such attacks, especially for attacks with low transmission power. This paper introduces SigDetect, a collaborative anomaly detection system that leverages complex channel frequency response (CFR) measurements and machine learning to detect signal injection attacks reliably. Extensive evaluations demonstrate that individual SigDetect detectors outperform a Received Signal Strength (RSS)-based method by 32.8% in outdoor experiments with stationary radios and 26.7% in indoor experiments where one of the radios is in motion. SigDetect also outperforms a CFR approach while eliminating the need to adjust thresholds to adapt to different wireless environments. Finally, SigDetect’s collaborative approach, in which neighboring network endpoints aid in detection, improves detection accuracy from 90.2% to 97.2% while reducing the false alarm from rate 11.2% to 0.7% and the missed detection rate from 8.3% to 4.9% in an indoor environment without mobile endpoints. These results suggest that SigDetect offers a promising solution for protecting cellular networks against low-power signal injection attacks.

This demonstration will showcase our efforts to develop a radio access network (RAN) slicing mechanism that is controllable via management software in an Open RAN framework. To our knowledge, our work represents the first effort that combines an open source Open RAN framework with an open source mobility stack, provides a top-to-bottom RAN application via the RAN intelligent control (RIC) provided by that framework and illustrates its functionality in a realistic wireless environment. Our software is publicly available and we provide a profile in the POWDER platform to enable others to replicate and build on our work.

POWDER is a highly flexible, deeply programmable, and city-scale scientific instrument that enables cutting-edge research in wireless technologies. Researchers interact with the POWDER platform via the Internet to conduct their experiments, with zero penalty for remote access. In this two-part demonstration, the POWDER implementers show how to use the platform. First, they present the workflow that researchers follow to conduct experiments. Second, they highlight some of the hardware and software building blocks available through POWDER, including components related to over-the-air wireless and mobile networking, 5G, and massive MIMO.

We investigate the ability of an attacker to passively use an otherwise secure wireless network to detect moving people through walls. We call this attack on privacy of people a "monitoring radio windows" (MRW) attack. We design and implement the MRW attack methodology to reliably detect when a person crosses the link lines between the legitimate transmitters and the attack receivers, by using physical layer measurements. We also develop a method to estimate the direction of movement of a person from the sequence of link lines crossed during a short time interval. Additionally, we describe how an attacker may estimate any artificial changes in transmit power (used as a countermeasure), compensate for these power changes using measurements from sufficient number of links, and still detect line crossings. We implement our methodology on WiFi and ZigBee nodes and experimentally evaluate the MRW attack by passively monitoring human movements through external walls in two real-world settings. We find that %our methods an attacker may achieve close to 100% accuracy in detecting line crossings and determining direction of motion, even through reinforced concrete walls.

New techniques in cross-layer wireless networks are building demand for ubiquitous channel sounding, that is, the capability to measure channel impulse response (CIR) with any standard wireless network and node. Towards that goal, we present a software-defined IEEE 802.11b receiver and CIR measurement system with little additional computational complexity compared to 802.11b reception alone. The system implementation, using the universal software radio peripheral (USRP) and GNU Radio, is described and compared to previous work. We validate the CIR measurement system and present the results of a measurement campaign which measures millions of CIRs between WiFi access points and a mobile receiver in urban and suburban areas.