As an essential technology for constructing green passive Internet of Things (IoT) network, backscatter communication (BackCom) enables battery-free IoT devices to deliver information by modulating and reflecting incident carriers. Nevertheless, the energy harvesting at an IoT device is constrained by its distance from the radio-frequency (RF) emitter, and the double-fading phenomenon significantly restricts the achievable performance of BackCom-based IoT network. Existing research has revealed that intelligent reflecting surface (IRS) and dynamic unmanned aerial vehicle (UAV) are promising solutions to overcome the current bottleneck, and their combined application in IoT network for BackCom remains in the preliminary exploration phase. Most studies focus more on traditional fixed RF emitters, which lack the flexibility needed for efficient energy transmission and are not suitable for infrastructure blank areas or hard-to-maintain regions. Motivated by this, an IRS-aided UAV-powered green IoT network is proposed in this paper, where the UAV acts as a mobile power beacon to energize multiple IoT nodes on the ground in a time-division multiple address manner, and an IRS is deployed in the scenario to enhance the BackCom performance. To guarantee reliable data transfer under energy harvesting constraint, we maximize the minimum average throughput among all IoT nodes for BackCom during the UAV flight duration by optimizing the node communication scheduling, power splitting coefficient, UAV trajectory and IRS phase shift. Specifically, we employ a four-stage alternating optimization method to decouple the optimization variables and solve for each variable independently. Finally, extensive experimental results verify the superior performance of our proposal in improving throughput over other benchmark schemes.

In this paper, we introduce two double-IRS strategies to enhance proactive eavesdropping, namely Double-IRS Enhancing Strategy (DES) and Double-IRS Enhancing-Jamming Strategy (DEJS). In the former strategy, both cooperative IRSs reflect the incident signal passively. In the latter strategy, one IRS performs passive reflection while the other IRS acts as a passive jammer to modulate the incident signal into the jamming signal to deteriorate the suspicious link. Based on these two strategies, the semidefinite relaxation (SDR) technique and the bisection search method are applied to optimize the reflection coefficients at two IRSs, aiming at maximizing the effective eavesdropping rate. Finally, numerical evaluations are conducted to verify the effectiveness of our proposed schemes.

This article proposes a novel intelligent reflecting surface (IRS) backscatter enhancing strategy to secure multi-input multioutput (MIMO) transmission in the presence of an eavesdropper and a malicious jammer. To be specific, the IRS is employed to backscatter the jamming signal into the desired signal to enhance the reception of the user. Utilizing this strategy, we maximize the system secrecy rate by jointly designing the reflection coefficients of IRS and active beamforming at the base station (BS). To efficiently handle this nonconvex optimization problem, we adopt an iterative block coordinate descent (BCD)-based algorithm, where the active beamforming is optimized through the Lagrange multiplier method and the backscatter coefficient matrix of IRS is optimized via the majorization-minimization (MM) method. Then, we examine the robustness of the proposed scheme when considering channel estimation errors. Extensive simulations confirm the secrecy performance gains achieved by our proposed strategy and verify its superiority compared to conventional IRS-based physical layer security (PLS) strategy, IRS backscatter-aided anti-eavesdropping strategy, and other baselines.

Seizure clusters, i.e., seizures that occur within a short duration of each other, occur in several epilepsy patients and are associated with increased disease severity. Understanding the characteristics of seizure clusters and predicting whether a given seizure will cluster or not is valuable both from a patient’s and clinician’s perspective. We propose a novel methodology for studying seizure clusters based on bivariate intracranial EEG (iEEG) features and develop one of the first individualized seizure cluster prediction models by combining machine learning with relative entropy (a bivariate feature). Relative entropy was used to quantify interactions between brain regions and capture potential differences in interactions underlying isolated and cluster seizures. We evaluated our methodology using one of the largest ambulatory iEEG datasets, consisting of data from 15 patients with up to 2 years of recordings each. This provided us a sufficient number of seizures in each patient to enable individualized analyses and prediction. On data of 3710 seizures consisting of 3341 cluster seizures (from 427 clusters) and 369 isolated seizures, machine learning models based on relative entropy predicted seizure clusters with up to 73.6% F1-score and outperformed baseline predictors. Our results are beneficial in addressing the clinical burden of clusters.

This article pioneers an unprecedented strategy of secure transmission, in which intelligent reflecting surface (IRS) is used as a backscatter device to form and scatter jamming signal while the transmitter (Alice) is regarded as a radio-frequency (RF) source. Specifically, Alice transmits confidential signal to a single-antenna legitimate user (Bob) while the transmission is overheard by multiple single-antenna illegitimate users (Eves). The beamformer at Alice is designed to align with the estimated channel vector from Alice to Bob, in order that the proposed strategy is completely compatible with the common communication system without respect to wiretap. To achieve secure transmission, IRS is deployed to modulate the received confidential signal to jamming signal and reflect it so as to deteriorate the reception at Eves. Based on this model, the reflection coefficient vector of IRS is optimized to minimize the eavesdropped information amount while guaranteeing the reliable communication at Bob. By comparing with the familiar IRS-based beamforming scheme and the cooperative jamming scheme in extensive simulations, the feasibility and secrecy performance gain are confirmed for the proposed strategy of IRS-based backscatter jamming.

We model Alzheimer’s disease (AD) progression by combining differential equations (DEs) and reinforcement learning (RL) with domain knowledge. DEs provide relationships between some, but not all, factors relevant to AD. We assume that the missing relationships must satisfy general criteria about the working of the brain, for e.g., maximizing cognition while minimizing the cost of supporting cognition. This allows us to extract the missing relationships by using RL to optimize an objective (reward) function that captures the above criteria. We use our model consisting of DEs (as a simulator) and the trained RL agent to predict individualized 10-year AD progression using baseline (year 0) features on synthetic and real data. The model was comparable or better at predicting 10-year cognition trajectories than state-of-the-art learning-based models. Our interpretable model demonstrated, and provided insights into, "recovery/compensatory" processes that mitigate the effect of AD, even though those processes were not explicitly encoded in the model. Our framework combines DEs with RL for modelling AD progression and has broad applicability for understanding other neurological disorders.

Reconfigurable intelligent surface (RIS), as a revolutionary technique, appears to ameliorate undesirable propagation environment in a controllable manner, which has the potential to substantially boost security of private information in the future smart radio environment. Much recent attention has been directed to the RIS-enhanced secure communication, among which the terrestrial network scenarios are generally concerned, while the exploration of aerial-ground communication integrated with RIS remains an open issue. Furthermore, in the available research, the ideal assumption of line-of-sight channel for aerial-ground link cannot be exploited in complex urban environment. Inspired by this, we provide in this paper the secrecy rate maximization problem under a complex urban scenario, where the drone's high mobility and RIS's tunable capability are utilized to against the potential eavesdropper. Although the established problem is intractable to tackle, we devise an iterative algorithm combining alternating optimization and successive convex approximation method as the solution, which jointly optimizes the phase shifts, the drone's trajectory along with transmit power for the single-user scenario. The proposed designs are also extended to the multi-user multi-eavesdropper system. Eventually, extensive simulation experiments indicate the remarkable benefits brought by our proposed scheme on secrecy performance and the necessity of optimizing phase shifts.

With the development of Internet and communication technologies, various information can easily spread to children via applications (Apps) on Internet-of-Things (IoT) devices (e.g., emerging smart toys, watches, and phones), especially, the Apps on smart phones based on Android. While greatly bringing up convenience for children's lives and studies, these Apps also make illegal and inappropriate contents (such as violence, pornography, gambling, and drug) more accessible to kids, which is harmful to minors' growth. To keep children away from inappropriate contents in applications, previous researches mainly focused on detecting unsuitable videos and advertisements in children applications or designing App maturity rating methods and parental control software. There are few literature that specially investigate the inspection of inappropriate contents in children Android Apps. Toward this end, we propose a novel automatic content inspection and the forensics framework to identify children Android Apps which are not proper for kids under 12. In addition, this framework offers evidence to make users understand why the inspected App is judged as unsuitable. In experiments, we apply this framework on some specially chosen Android Apps which distinctly include inappropriate contents to verify its performance. The results show that it can successfully identify those applications with high precision that reaches 85.7%. Besides, by analyzing the collected children's Android Apps through our framework, we find that 40% of them are identified to be improper, which illustrates the serious issue of unsuitable children Android Apps.

Nowadays, almost all fields extremely rely on Global Positioning System (GPS) which provides accurate and reliable position as well as time information free of charge. While bringing up convenience, GPS also has a number of flaws which bring people nonnegligible security threats. Existing works primarily focused on GPS signal attacks at the physical layer in GPS systems without considering the road navigation scenario. In this paper, we present a novel attack scheme targeting the road navigation systems by injecting the malicious codes into a map application and falsifying the GPS location remotely. Successful attacks are launched to induce victims into arriving at the wrong destination or passing a malicious place.

User Interface (UI) testing is a popular approach to ensure the quality of mobile apps. Numerous test generation tools have been developed to support UI testing on mobile apps, especially for Android apps. Previous work evaluates and compares different test generation tools using only relatively simple open-source apps, while real-world industrial apps tend to have more complex functionalities and implementations. There is no direct comparison among test generation tools with regard to effectiveness and ease-of-use on these industrial apps. To address such limitation, we study existing state-of-the-art or state-of-the-practice test generation tools on 68 widely-used industrial apps. We directly compare the tools with regard to code coverage and fault-detection ability. According to our results, Monkey, a state-of-the-practice tool from Google, achieves the highest method coverage on 22 of 41 apps whose method coverage data can be obtained. Of all 68 apps under study, Monkey also achieves the highest activity coverage on 35 apps, while Stoat, a state-of-the-art tool, is able to trigger the highest number of unique crashes on 23 apps. By analyzing the experimental results, we provide suggestions for combining different test generation tools to achieve better performance. We also report our experience in applying these tools to industrial apps under study. Our study results give insights on how Android UI test generation tools could be improved to better handle complex industrial apps.

Leveraging the concept of software-defined network (SDN), the integration of terrestrial 5G and satellite networks brings us lots of benefits. The placement problem of controllers and satellite gateways is of fundamental importance for design of such SDN-enabled integrated network, especially, for the network reliability and latency, since different placement schemes would produce various network performances. To the best of our knowledge, it is an entirely new problem. Toward this end, in this paper, we first explore the satellite gateway placement problem to obtain the minimum average latency. A simulated annealing based approximate solution (SAA), is developed for this problem, which is able to achieve a near-optimal latency. Based on the analysis of latency, we further investigate a more challenging problem, i.e., the joint placement of controllers and gateways, for the maximum network reliability while satisfying the latency constraint. A simulated annealing and clustering hybrid algorithm (SACA) is proposed to solve this problem. Extensive experiments based on real world online network topologies have been conducted and as validated by our numerical results, enumeration algorithms are able to produce optimal results but having extremely long running time, while SAA and SACA can achieve approximate optimal performances with much lower computational complexity.