Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.

For many applications, drones are required to operate entirely or partially autonomously. In order to fly completely or partially on their own, drones need to access location services for navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments. The ranging method using beacons is one of the most popular methods for localization, especially for indoor environments. In general, the localization error in this class is due to two factors: the ranging error, and the error induced by the relative geometry between the beacons and the target object to be localized. This paper proposes OPTILOD (Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones), an optimization algorithm for the optimal placement of beacons deployed in three-dimensional indoor environments. OPTILOD leverages advances in evolutionary algorithms to compute the minimum number of beacons and their optimal placement, thereby minimizing the localization error. These problems belong to the Mixed Integer Programming (MIP) class and are both considered NP-hard. Despite this, OPTILOD can provide multiple optimal beacon configurations that minimize the localization error and the number of deployed beacons concurrently and efficiently.

In many scenarios, unmanned aerial vehicles (UAVs), aka drones, need to have the capability of autonomous flight to carry out their missions successfully. In order to fly autonomously, drones need to know their location constantly. Then, based on their current position and the final destination, navigation commands will be generated and drones will be guided to their destination. Localization can be easily carried out in outdoor environments using GPS signals and drone inertial measurement units (IMUs). However, such an approach is not feasible in indoor environments or GPS-denied areas. In this paper, we propose a localization scheme for drones called PILOT (High-Precision Indoor Localization for Autonomous Drones), which is specifically designed for indoor environments. PILOT relies on ultrasonic acoustic signals to estimate the target drone's location. To obtain a precise final estimation of the drone's location, PILOT deploys a three-stage localization scheme. The first two stages provide robustness against the multi-path fading effect of indoor environments and mitigate the ranging error. Then, in the third stage, PILOT deploys a simple yet effective technique to reduce the localization error induced by the relative geometry between transmitters and receivers, which significantly reduces the height estimation error. The performance of PILOT was assessed in different scenarios, and the results indicate that it achieves centimeter-level accuracy for three-dimensional drone localization.

Geofencing technologies enable the creation of virtual boundaries around specific locations to regulate actions within that area. These boundaries provide a flexible, yet secure way to control access, monitor activity, and enforce rules. A prime example is the use of geofencing to establish no-fly zones for drones, ensuring aviation safety. Geofencing can also be used in virtual environments, such as metaverse platforms, to restrict user access to specific rooms, enhancing security and management. In this article, we present EGO-6, an optimization framework for a geofencing system tracking users in indoor environments. EGO6 optimizes 6G transmission/reception point (TRP) deployment in three-dimensional spaces with its innovative technique. Using Evolutionary Algorithm (EA) advancements, EGO-6 calculates minimum 6G anchor requirements and their optimal placement to lower deployment costs and tracking errors. Solving this complex NP-Hard challenge which belongs to Mixed Integer Programming (MIP) problems, EGO-6 offers cost-efficient anchor configurations for improved indoor geofencing and user tracking with fewer deployed 6G TRPs.

The pursuit of high-accuracy localization without relying on the global positioning system (GPS) has gained significant interest in recent years. The deployment of autonomous vehicles (AVs) in diverse indoor applications exemplifies a prominent domain where the demand for a robust positioning system is evident. With the advancements in 5G and beyond radio access networks (RAN), the availability of new positioning signals presents an opportunity to deliver accurate location estimates for these applications. Nevertheless, these signals encounter substantial path losses in indoor environments. Additionally, the precise localization within existing frameworks requires stringent synchronization, which is challenging to meet. In this paper, we propose OFDRA: Optimal Femtocell Deployment for Accurate Indoor Positioning of RIS-Mounted AVs, a novel positioning framework that is robust against multipath and does not require strict synchronization between anchor-anchor or anchor-target entities. Specifically, OFDRA is designed to operate in scenarios where the line of sight (LOS) exists. The first design objective of OFDRA is the mitigation of ranging errors by leveraging a compact reconfigurable intelligent surface (RIS) mounted on top of AVs acting as a programmable mirror in a 5G network. The second design objective is to achieve optimal anchor placement in three-dimensional indoor spaces, thereby reducing the geometric dilution of precision (GDOP) and mitigating geometric-induced errors in the final position estimation. Our experimental verification reveals that the localization error is influenced by GDOP, encompassing both the $X-Y$ plane and $Z$ -axis estimations. Through optimized anchor placement, OFDRA demonstrates a seven-fold enhancement in $Z$ -axis accuracy compared to the state-of-the-art, achieving a sub-1 m three-dimensional accuracy for more than 95% of cases.

Drones in many applications need the ability to fly fully or partially autonomously to accomplish their mission. To allow these fully/partially autonomous flights, first, the drone needs to be able to locate itself constantly. Then, the navigation command signal would be generated and passed on to the controller unit of the drone. In this article, we propose a localization scheme for drones called robust localization for indoor navigation of drones with optimized beacon placement (iDROP) that is specifically devised for GPS-denied environments (e.g., indoor spaces). Instead of GPS signals, iDROP relies on speaker-generated ultrasonic acoustic signals to enable a drone to estimate its location. In general, localization error is caused by two factors: the ranging error and the error induced by relative geometry between the transmitters and the receiver. iDROP mitigates these two types of errors and provides a high-precision 3-D localization scheme for drones. iDROP employs a waveform that is robust against multipath fading. Moreover, placing beacons in optimal locations reduces the localization error induced by the relative geometry between the transmitters and the receiver.

Navigating in environments where the GPS signal is unavailable, weak, purposefully blocked, or spoofed has become crucial for a wide range of applications. A prime example is autonomous navigation for drones in indoor environments: to fly fully or partially autonomously, drones demand accurate and frequent updates of their locations. This paper proposes a Robust Acoustic Indoor Localization (RAIL) scheme for drones designed explicitly for GPS-denied environments. Instead of depending on GPS, RAIL leverages ultrasonic acoustic signals to achieve precise localization using a novel hybrid Frequency Hopping Code Division Multiple Access (FH-CDMA) technique. Contrary to previous approaches, RAIL is able to both overcome the multipath fading effect and provide precise signal separation in the receiver. Comprehensive simulations and experiments using a prototype implementation demonstrate that RAIL provides high-accuracy three-dimensional localization with an average error of less than 1.5 cm.

Drones must operate permanently or temporarily autonomously for many applications. They rely on access to location services upon obtaining navigation commands and continually thereafter to enable completely or partially autonomous flight. Global Positioning System (GPS) is not always available, can be spoofed or jammed, and is particularly error-prone in indoor and underground settings. In this article, we present SPIN (Sensor Placement for Indoor Navigation of Drones), a sensor-assisted ranging system for drones that performs in GPS-deficient situations. SPIN employs a novel optimization technique for the deployment of indoor sensors in three-dimensional spaces. SPIN utilizes advancements in Evolutionary Algorithms to compute the smallest number of sensors and their ideal placement in order to minimize deployment costs and localization errors. This challenge is classified as NP-Hard and belongs to the class of Mixed Integer Programming (MIP) problems. SPIN can provide numerous optimal sensor configurations that decrease the number of deployed sensors, enabling autonomous navigation of drones in inside environments at a low cost.

Due to the great achievements in artificial intelligence, it is predicted that autonomous vehicles with little or even no human involvement will come to market in the near future. Autonomous vehicles are equipped with multiple types of sensors. An autonomous vehicle relies on its sensors to perceive its environment, and this sensory information plays a key role in the vehicle's driving decisions. Hence, ensuring the trustworthiness of the sensor data is crucial for drivers' safety. In this article, we discuss the impact of perception error attacks (PEAs) on autonomous vehicles, and propose a countermeasure called LIFE (LIDAR and Image data Fusion for detecting perception Errors). LIFE detects PEAs by analyzing the consistency between camera image data and LIDAR data using novel machine learning and computer vision algorithms. The performance of LIFE has been evaluated extensively using the KITTI dataset.

Multi Link Aggregation (MLA) is a feature likely to be introduced in Wi-Fi 7, the next-generation of Wi-Fi, which will be based on the IEEE 802.11be specifications. MLA will allow Wi-Fi devices that support multiple bands (such as the 2.4 GHz, 5 GHz, and 6 GHz bands) to operate on them simultaneously. The resulting throughput and latency gains are likely to bring Wi-Fi one step closer to supporting emerging real-time applications like augmented and virtual reality. While throughput gains resulting from the use of MLA are mostly linear, the latency gains exhibit interesting characteristics and are the subject of this paper. We use our in-house simulator to study the latency enhancements resulting from MLA and seek to answer whether Wi-Fi 7 devices can meet the challenging latency requirements demanded by most real-time applications. In this pursuit, we observe that allowing Wi-Fi devices to contend on even a single additional link without changing any physical layer parameters can lead to an order of magnitude improvement in the worst-case latency in many scenarios. In addition, we highlight that even in dense conditions, MLA can help Wi-Fi devices meet the challenging latency requirements of most real-time applications.

Regulators in the US and Europe have stepped up their efforts to open the 6 GHz bands for unlicensed access. The two unlicensed technologies likely to operate and coexist in these bands are Wi-Fi 6E and 5G New Radio Unlicensed (NR-U). The greenfield 6 GHz bands allow us to take a fresh look at the coexistence between Wi-Fi and 3GPP-based unlicensed technologies. In this paper, using tools from stochastic geometry, we study the impact of Multi User Orthogonal Frequency Division Multiple Access, i.e., MU OFDMA—a feature introduced in 802.11ax—on this coexistence issue. Our results reveal that by disabling the use of the legacy contention mechanism (and allowing only MU OFDMA) for uplink access in Wi-Fi 6E, the performance of both NR-U networks and uplink Wi-Fi 6E can be improved. This is indeed feasible in the 6 GHz bands, where there are no operational Wi-Fi or NR-U users. In so doing, we also highlight the importance of accurate channel sensing at the entity that schedules uplink transmissions in Wi-Fi 6E and NR-U. If the channel is incorrectly detected as idle, factors that improve the uplink performance of one technology contribute negatively to the performance of the other technology.

In many countries, sharing has become a significant approach to problems of spectrum allocation and assignment. As this approach moves from concept to reality, it is reasonable to expect an increase in interference or usage conflict events between sharing parties.Scholars such as Coase, Demsetz, Stigler, and others have argued that appropriate enforcement is critical to successful contracts (such as spectrum sharing agreements) and Polinsky, Shavell, and others have analyzed enforcement mechanisms in general. While many ex-ante measures may be used, reducing the social costs of ex-ante enforcement means shifting the balance more toward ex-post measures. Ex post enforcement requires detection, data collection, and adjudication methods. At present, these methods are ad hoc (operating in a decentralized way between parties) or fairly costly (e.g., relying on the FCC Enforcement Bureau). The research presented in this paper is the culmination of an NSF-funded inquiry into how and what enforcement functions can be automated.

The 3rd Generation Partnership Project (3GPP) is actively designing New Radio Vehicle-to-Everything (NR V2X)—a 5G NR-based technology for V2X communications. NR V2X, along with its predecessor Cellular V2X (C-V2X), is set to enable low-latency and high-reliability communications in high-speed and dense vehicular environments. A key reliability-enhancing mechanism that is available in C-V2X and is likely to be re-used in NR V2X is packet re-transmissions. In this paper, using a systematic and extensive simulation study, we investigate the impact of this feature on the system performance of C-V2X. We show that statically configuring vehicles to always disable or enable packet re-transmissions either fails to extract the full potential of this feature or leads to performance degradation due to increased channel congestion. Motivated by this, we propose and evaluate Channel Congestion-based Re-transmission Control (C 2 RC), which, based on the observed channel congestion, allows vehicles to autonomously decide whether or not to use packet re-transmissions without any role of the cellular infrastructure. Using our proposed mechanism, C-V2X-capable vehicles can boost their performance in lightly-loaded environments, while not compromising on performance in denser conditions.

In many drone applications, drones need the ability to fly fully or partially autonomously to carry out their mission. To enable such fully/partially autonomous flights, the ground control station that is supporting the drone’s operation needs to constantly localize and track the drone, and send this information to the drone’s navigation controller to enable autonomous/semiautonomous navigation. In outdoor environments, localization and tracking can be readily carried out using GPS and the drone’s Inertial Measurement Units (IMUs). However, in indoor areas or GPS-denied environments, such an approach is not feasible. In this paper, we propose a localization and tracking scheme for drones called ROLATIN (Robust Localization and Tracking for Indoor Navigation of drones) that was specifically devised for GPS-denied environments. Instead of GPS signals, ROLATIN relies on speakergenerated ultrasonic acoustic signals to estimate the target drone’s location and track its movement. Compared to vision and RF signal-based methods, our scheme offers a number of advantages in terms of performance and cost.

When different stakeholders share a common resource, such as the case in spectrum sharing, security and enforcement become critical considerations that affect the welfare of all stakeholders. Recent advances in radio spectrum access technologies, such as cognitive radios, have made spectrum sharing a viable option for significantly improving spectrum utilization efficiency. However, those technologies have also contributed to exacerbating the difficult problems of security and enforcement. In this paper, we review some of the critical security and privacy threats that impact spectrum sharing. We propose a taxonomy for classifying the various threats, and describe representative examples for each threat category. We also discuss threat countermeasures and enforcement techniques, which are discussed in the context of two different approaches: ex ante (preventive) and ex post (punitive) enforcement.

The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability.

In spectrum sharing, a spatial separation region is defined around a primary user (PU) where co-channel and/or adjacent channel secondary users (SUs) are not allowed to operate. This region is often called an Exclusion Zone (EZ), and it protects the PU from harmful interference caused by SUs. Unfortunately, existing methods for defining an EZ prescribe a static and an overly conservative boundary, which often leads to poor spectrum utilization efficiency. In this paper, we propose a novel framework-namely, Multi-tiered dynamic Incumbent Protection Zones (MIPZ)-for prescribing interference protection for PUs. MIPZ can be used to dynamically adjust the PU's protection boundary based on the changing radio interference environment. MIPZ can also serve as an analytical tool for quantitatively analyzing a given protection region to gain insights on and determine the trade-off between interference protection and spectrum utilization efficiency. Using results from extensive simulations and a real-world case study, we demonstrate the effectiveness of MIPZ in protecting PUs from harmful interference and in improving the overall spectrum utilization efficiency.

One of the notable features of the upcoming Wireless Fidelity (Wi-Fi) standard-namely, IEEE 802.11ax-is the use of Multi-User Orthogonal Frequency Division Multiple Access (MU-OFDMA). MU-OFDMA facilitates multiple users to transmit simultaneously in smaller sub-channels (a.k.a. resource units (RUs)), thereby improving the 802.11ax MAC efficiency. The 802.11ax MAC enables MU-OFDMA transmissions in the uplink (UL) by using two types of RUs: i) Random Access (RA) RUs, and ii) Scheduled Access (SA) RUs. In this paper, we investigate the impact of different distributions of RA RU and SA RU on the MAC layer performance. We leverage our analysis in devising a practical UL RU allocation scheme that maximizes the overall 802.11ax network throughput. We implement the 802.11ax MAC in network simulator-3 (NS-3) and perform extensive simulations to validate the efficacy of our proposed scheme.

The 5.9 GHz band has been earmarked in many countries for Intelligent Transportation Systems (ITS) applications. Cellular V2X (C-V2X)-a recently developed access technology for vehicle-to-everything (V2X) communications is a candidate technology to provision ITS applications using the 5.9 GHz band. Due to the ever-increasing popularity of Wi-Fi and search for additional unlicensed bands, in the US and Europe, regional regulators have previously considered allowing co-channel Wi-Fi operations in the ITS band on a secondary basis to the incumbent ITS technology. Additionally, there are several Wi-Fi channel configurations, some already in operation while others still under consideration, which place Wi-Fi and C-V2X devices in adjacent bands. It is, therefore, likely that C-V2X users may be susceptible to interference from Wi-Fi devices operating both in co-channel scenarios and in adjacent bands. To make an informed decision on future Wi-Fi channelization in and around the ITS band, a detailed study on the impact of these Wi-Fi transmissions on the system performance of C-V2X is extremely essential. In this paper, through a comprehensive and systematic simulation study, we investigate the impact of Wi-Fi transmissions on the performance of C-V2X, both in co-channel and adjacent channel scenarios. Our simulations reveal that if Wi-Fi devices are to coexist with C-V2X in the same spectrum, existing mechanisms either fall short of sufficiently protecting C-V2X performance or render the spectrum unusable for Wi-Fi operations. On the other hand, all Wi-Fi channels that are adjacent to the ITS band can significantly degrade the system-wide C-V2X performance. In such scenarios, to adequately protect the C-V2X network performance, either certain restrictions need to be put in place on Wi-Fi operations, or the operations of Wi-Fi in such channels must be prohibited.

The harmful interference caused by rogue radios poses a serious threat to spectrum sharing ecosystems. One approach for mitigating this problem is to adopt an enforcement scheme that can be used by an enforcement entity (e.g., Federal Communications Commission’s Enforcement Bureau) to uniquely identify transmitters by authenticating their waveforms. In this approach, the enforcement entity that is authenticating the waveform is not the intended receiver, and hence it has to decode the authentication signal “blindly” with little or no knowledge of the transmission parameters. In real-world scenarios, an enforcement entity may need to cope with additional challenges, including poor signal strength of the received signals and simultaneous co-channel transmissions from multiple transmitters. In this paper, we propose a novel concept that effectively addresses some of these challenges, which we refer to as crowd-sourced blind authentication of co-channel transmitters (CBAT). We also present a concrete instantiation of this concept called frequency offset embedding for CBAT (FREE). Our results show that FREE enables the enforcement entity to blindly authenticate multiple co-channel transmitters with good accuracy by harnessing the power of crowd-sourcing.