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Radio maps can be used for source localization, link performance prediction, and wireless relay planning. This paper studies an air-to-ground radio map learning problem to predict the channel gain between a ground terminal and a low altitude unmanned aerial vehicle (UAV). The challenge is the insufficient measurement samples under the high dimensionality of the radio map, where both the transmitter and the receiver have full spatial degrees of freedom. Classical methods, such as k-nearest neighbor (KNN) and Kriging, may fail due to insufficient data. In this paper, a multi-degree channel model is proposed to fit low altitude air-to-ground propagation scenario. To address the high dimensionality issue, a hidden multi-class virtual obstacle model is developed, and a corresponding obstacle map is estimated from a small set of training data. Our numerical results confirm that the proposed model and the learning algorithm can significantly increase the prediction accuracy. When the constructed radio map applied to received signal strength (RSS) based source localization in a cellular communication scenario, a sub-20-meter accuracy is achieved, substantially better than the baseline model-based localization method.

In recent years, computation offloading has been considered as a promising technology to support computation- intensive vehicular applications. In this paper, we mainly focus on computation offloading in a vehicular cloud network (VCN), in which both vehicles and infrastructures with resource availability are defined as resource providers. However, due to the dynamically changing on-board resource distribution and the uncoordinated offloading strategies among vehicles, the computation offloading problem in a VCN is very challenging. In this paper we first model the problem as a multi-agent multi- armed bandit problem. We then propose a reshaped upper confidence bound (UCB) algorithm to estimate the on-board resource distribution with the reward estimation. We further utilize a novel multi-agent reinforcement learning algorithm to manage the computation offloading in a VCN. Simulation results demonstrate the performance gains by using the proposed algorithm.

This paper considers the problem of upgrading a legacy network into a Software Defined Network (SDN) over multiple stages and maximizing energy saving (ES) in the resulting upgraded network or hybrid SDN. In each stage, an operator needs to select and replace legacy switches with SDN switches and seek to switch off as many cables as possible over each link. This paper addresses the said problem where it considers (i) the available budget at each stage, (ii) maximum path delays, (iii) maximum link utilization, (iv) per-stage increase (decrease) in traffic size (upgrade cost), and (v) each non SDN switch must comply with the Open Shortest Path First (OSPF)-Equal Cost Multi-Path (ECMP) protocol. It outlines a Mixed Integer Program (MIP) and a heuristic algorithm called M-GMSU. The results show that (i) MIP and M-GMSU achieve ES of up to 71.93%, (ii) using a larger budget and/or number of stages increases ES, and (iii) the ES of M-GMSU is within 3.55% away from the optimal ES computed by MIP.

Unauthorized voice recording via smartphones can leak the talking content stealthily. This would be a serious security threat to those individuals, enterprises and the government who need to keep the conversation confidential. Furthermore, due to the size miniaturization of smartphones, it is hard to find the covert recording from malicious attendees. Existing solutions usually jam the recording with audible noise or electromagnetic emitting. However, the audible noise will seriously interfere with conversation and the effect of electromagnetic emitting will be limited by the distance. In this paper, we propose UltraArray, a pioneering silent ultrasonic anti-recording jammer, which can covertly block recording for a long distance. The principle of covert blocking is inspired by acoustic parametric array theory, which suggests that the audible frequency wave can be spread through the air silently while it is modulated to an inaudible ultrasonic frequency. The modulation used in this paper is double sideband (DSB) modulation. The microphone on the phone will record the audible frequency and filtering out the ultrasonic frequency. The jammer we developed uses an acoustics array to form a beam to spread the signal further. The evaluation shows that the device has a good jamming effect on more than 5 meters for most Android smartphones. It will also work well with more than 2.5 meters effective distance on iPhone XR, which has the active noise control (ANC) function. Those results achieve ten times the interference ability of existing solutions.

With the growing momentum around Open RAN (O-RAN) initiatives, performing dynamic Function Splitting (FS) in disaggregated and virtualized Radio Access Networks (vRANs), in an efficient way, is becoming highly important. An equally important efficiency demand is emerging from the energy consumption dimension of the RAN hardware and software. Supplying the RAN with Renewable Energy Sources (RESs) promises to boost the energy-efficiency. Yet, FS in such a dynamic setting, calls for intelligent mechanisms that can adapt to the varying conditions of the RES supply and the traffic load on the mobile network. In this paper, we propose a reinforcement learning (RL)-based dynamic function splitting (RLDFS) technique that decides on the function splits in an O-RAN to make the best use of RES supply and minimize operator costs. We also formulate an operational expenditure minimization problem. We evaluate the performance of the proposed approach on a real data set of solar irradiation and traffic rate variations. Our results show that the proposed RLDFS method makes effective use of RES and reduces the cost of an MNO. We also investigate the impact of the size of solar panels and batteries which may guide MNOs to decide on proper RES and battery sizing for their networks., Comment: Accepted Paper. 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

With the rapid development of the Internet of Things technology, smart speakers have become increasingly popular. However, smart speaker security is an ensuing threat. At present, smart speakers are activated by voice, and they monitor users’ voices 24 hours per day. Consequently, there may be problems with user privacy leakage. In this paper, we propose a non-intrusive digital forensic method for smart speakers. The main contribution of the paper is an effective method of combining network traffic analysis with the extraction of user intent and alarms about abnormal network traffic to support the investigation of security. We use Xiaomi smart speakers as an example in an experiment to verify our forensic method. The evaluation results show that our method works well for detecting security risks.

A Cyber-Physical System (CPS) refers to the interconnection of control (actuation), computational nodes and sensors, in order to manage physical processes. In recent years, the CPS design methodology has been adopted in several large-scale infrastructures such as smart power grids. Given the application criticality, sustaining the security of these systems is of utmost importance. One of the major security goals is to protect CPS against impersonation, where an adversary intends to manipulate the system state by sending erroneous data that appears to be reported by one of the system nodes, e.g. PMUs of a power grid. This paper proposes a novel hardware-assisted authentication scheme to counter such a threat, by exploiting imperfections that occur in the manufacturing process of integrated circuits. In essence, the proposed scheme associates a fingerprint for each system node so that the authenticity of the data source could be verified. In addition, the paper tackles the threat of message replay where the adversary re-transmits a legitimate message so that the system factors in outdated rather than fresh sensor measurements. This paper thwarts such a replay attack by leveraging the synchronized clocks across the CPS nodes, e.g., based on GPS; the idea is to employ a combination of time-stamp signatures and hardware fingerprints. Our proposed schemes can also detect and prevent data forgery, and Sybil attacks. The viability and performance of the proposed schemes are validated through analysis and prototype implementation.

The beyond fifth-generation (B5G) and future wireless networks face the challenges of spectral, energy and cost efficiency for large scale machine-type communications. Recently, emerging ambient backscatter communication (AmBC) technology provides a promising paradigm for the development of green Internet of Things (IoT) networks in beyond B5G era. In this paper, we consider a multi-nodes scenario where a backscatter network is symbiotic with primary network consisting of multiple ambient radio frequency (RF) sources, thereby allowing the system to use the appropriate RF to support high throughput and wide coverage for IoT devices. Unlike existing work on AmBC, which focuses on physical layer with relatively ideal model, i.e., classic three-nodes model composed of RF, backscatter device (BD) and IoT device, this paper studies the access control strategy, including coefficient design and device association of BDs and IoT devices, for multi-RF backscatter network from the perspective of maximizing device transmission rate. Under the guarantee of quality of service (QoS), we develop an access control strategy with aim of maximizing the weighted sum of primary and backscatter transmission rates, and design a distributed access control strategy called DCA-S, by using the difference of two convex functions approximation (DCA) and dual decomposition to transform the non-concave optimization problem into the solvable concave subproblems. Numerical results show that the proposed DCA-S can achieve significantly performance improvement of the system compared with benchmark schemes.

Terahertz frequency bands will likely be used for the next-generation wireless communication systems to provide data rates of hundreds of Gbps or even Tbps because of the wide swaths of unused and unexplored spectrum. This paper presents two outdoor wideband measurement campaigns in downtown Brooklyn (urban microcell environment) in the sub-THz band of 140 GHz with TX-RX separation distance up to 117.4 m: i) terrestrial urban microcell measurement campaign, and ii) rooftop surrogate satellite and backhaul measurement campaign. Outdoor omnidirectional and directional path loss models for both line-of-sight and non-line-of-sight scenarios, as well as foliage loss (signal attenuation through foliage), are provided at 140 GHz for urban microcell environments. These measurements and models provide an understanding of both the outdoor terrestrial (e.g., 6G cellular and backhaul) and non-terrestrial (e.g., satellite and unmanned aerial vehicle communications) wireless channels, and prove the feasibility of using THz frequency bands for outdoor fixed and mobile cellular communications. This paper can be used for future outdoor wireless system design at frequencies above 100 GHz.

In this paper we explore solutions to robust group communication in disadvantaged wireless networks that exhibit low bandwidth, high packet losses, and frequent or even permanent network partitions. More specifically, we propose a group communication protocol based on Named Data Networking (NDN). By design, NDN’s in-network caching and stateful forwarding plane can help improve data delivery robustness in disadvantaged networks, but when content is generated with dynamic rates, an efficient, low-overhead synchronization protocol is needed to inform group members to fetch newly generated content promptly. In this paper, we describe the Prefetch Loss-Insensitive Sync (PLI-Sync) protocol for group communication in highly disadvantaged networks. PLI-Sync combines optimistic content pre-fetching with new content notification via sync, and addresses the challenges of how to distinguish wireless packet losses from mobility-induced disconnections, and between data availability and retrievability. Our evaluations show that leveraging the interplay between optimistic pre-fetching and a low rate sync protocol can significantly reduce communication overhead compared to relying on sync protocols alone, while maintaining low data fetching latency and robust delivery in a variety of wireless conditions and traffic load settings.