This paper proposes CSS, a built-in channel state scrambling scheme to provide always-on protection for channel state information. CSS uses randomly generated scrambling vectors to emulate human activities, preventing eavesdroppers from recovering human physical activity from the channel state. By carefully designing the scrambling scheme based on physical channel models, we ensure that legacy receivers can successfully decode the frames and all frames transmitted over the air are scrambled. Furthermore, legitimate Wi-Fi sensors can still recover the true activity with a pre-shared secret key. Implementation on a real communication system shows that CSS can retain the same frame-error rate for commercial wireless receivers while misleading eavesdroppers with a success rate of over 95%.

Acoustic ranging has been viewed as a promising Human-Computer Interaction (HCI) technology in many scenarios, such as Augmented Reality (AR)/Virtual Reality (VR) and smart appliances. Most ranging systems with distributed devices undergo an extra calibration process to remove the timing errors. However, the calibration process needs user intervention. Furthermore, it should assume that the clock drifts are linear and stable, which is disabled within tens of minutes. In this paper, we introduce a self-calibrated acoustic ranging system that achieves sub-millimeter accuracy on distributed asynchronous devices. Based on our theoretical timing model, we precisely cancel both the system delay and the nonlinear clock drift with carefully designed Orthogonal Frequency-Division Multiplexing (OFDM) ranging signals. Our synchronization scheme achieves a timing accuracy of 1.9 microseconds, which allows us to build large-scale virtual acoustic arrays. Based on such a calibration scheme, our localization system achieves a ranging error of$\rm{0.39}~mm$within three meters in real-world experiments.

We propose UltraCLR, a new contrastive learning framework that fuses dual modulation ultrasonic sensing signals to enhance gesture representation. Most existing ultrasound-based gesture recognition tasks rely on a large amount of manually labeled samples to learn task-specific representations via end-to-end training. However, they cannot exploit unlabeled continuous gesture signals that are easy to collect. Inspired by recent self-supervised learning techniques, UltraCLR aims to autonomously learn a ubiquitous gesture signal representation that can benefit all tasks from low-cost unlabeled signals. We use the STFT heatmap as a secondary input and leverage the contrastive learning framework to improve the high-quality Channel Impulsive Response heatmap input representations. The learned representations can better represent the spatial-position information and intermediate states of gesture movement. With the representation learned by UltraCLR, we can greatly reduce the complexity of downstream gesture recognition tasks so that they can be completed using a simple classifier trained with a small training set and a lower computational cost. Our experimental results show that UltraCLR outperforms state-of-the-art gesture recognition systems with only a few labeled samples and achieves more than 85% reduction in computational complexity and over 9× improvement in inference speed.

Once challenged by the SARS-CoV-2 virus, the human host immune system triggers a dynamic process against infection. We constructed a mathematical model to describe host innate and adaptive immune response to viral challenge. Based on the dynamic properties of viral load and immune response, we classified the resulting dynamics into four modes, reflecting increasing severity of COVID-19 disease. We found the numerical product of immune system's ability to clear the virus and to kill the infected cells, namely immune efficacy, to be predictive of disease severity. We also investigated vaccine-induced protection against SARS-CoV-2 infection. Results suggested that immune efficacy based on memory T cells and neutralizing antibody titers could be used to predict population vaccine protection rates. Finally, we analyzed infection dynamics of SARS-CoV-2 variants within the construct of our mathematical model. Overall, our results provide a systematic framework for understanding the dynamics of host response upon challenge by SARS-CoV-2 infection, and this framework can be used to predict vaccine protection and perform clinical diagnosis.

Several recent works have studied mobile charging under the “one-to-many” charging pattern where a single charger can charge multiple devices simultaneously. However, most of them focus on path planning and charging time allocation, but overlook the underlying dependence of the charging efficiency on initial deployment positions of chargers. This paper studies the problem ofPlacing directional wIreless chargers withLimited mObiliTy (PILOT), that is, maximize the overall charging utility for a set of static rechargeable devices on a 2D plane by determining deployment positions, stop positions and orientations, and portions of time for all deployed chargers that can move in a limited area after their deployment. To the best of our knowledge, we are the first to study placement of mobile directional chargers under the “one-to-many” pattern. To address PILOT, we propose a$(\frac{1}{2}-\epsilon)$-approximation algorithm. First, we present a method to approximate nonlinear charging power of chargers, and further propose an approach to construct Maximal Covered Set uniform subareas to reduce the infinite continuous search space for stop positions and orientations to a finite discrete one. Second, we present geometrical techniques to further reduce the infinite solution space for candidate deployment positions to a finite one without performance loss, and transform PILOT to a mixed integer nonlinear programming problem. Finally, we propose a linear programming based greedy algorithm to address it. Simulation and experimental results show that our algorithm outperforms six comparison algorithms by$19.74 \% \sim 500.01 \%$.

Continuous domestic respiration monitoring provides vital information for diagnosing assorted diseases. In this article, we introduce RespTracker , the first continuous, multiple-person respiration tracking system in domestic settings using acoustic-based COTS devices. RespTracker uses a multi-stage algorithm to separate and recombine respiration signals from multiple paths so that it can track the respiration rate of multiple moving subjects. And it leverages features from multiple dimensions to separate different users in the same area. Our experimental results show that our two-stage algorithm can distinguish the respiration of at least four subjects and cover a distance of three meters.

The limitation on the flight range motivates a hybrid monitoring system, wherein trucks carrying drones drive to pre-planned positions and then free drones for task execution. While the flight range limitation is mitigated, it is challenging to determine the destination of trucks and drones and set airborne cameras. This paper optimizes the joint Deployment of trUcks and dronEs for objecT monitoring (DUET), that is, deploy a set of trucks where each truck carries drones, and each drone is equipped with a varifocal camera such that the overall monitoring utility for target objects is maximized. To tackle the DUET problem, we first model the hybrid system and monitoring utility; then, discretize the solution space of DUET with performance bound. In this way, the problem is transformed into a two-level combinatorial optimization problem satisfying submodularity. To address it, a two-level greedy algorithm with $\frac{{{{(e - 1)}^2}}}{{e(2e - 1)}} \cdot (1 - \varepsilon )$ approximation ratio is proposed to select deployment strategies. After the strategy selection, an optimal method is devised to carefully adjust the strategy for energy saving and communication improvement without loss of monitoring utility. Both simulations and field experiments are conducted to evaluate the proposed framework, which outperforms baseline algorithms on monitoring utility by at least 28.4% and 40%, respectively.

Unmanned aerial vehicle (UAV) has attracted much attention due to its excellent ability to collect visual information of surroundings. In this paper, we investigate a new monitoring model to focus on sizes and shapes of objects, and occlusion between objects, and then study the placement of a set of UAVs to monitor polygonal sizable objects. Our aim is to maximize the overall monitoring utility of all objects by determining the positions and orientations of UAVs, given a set of polygonal sizable objects with fixed coordinates and shapes on a$2\mathbf{D}$plane. We study two typical scenarios of the problem: the former stipulates that a line segment is effectively monitored only when it is completely monitored by a single UAV, and the latter allows multiple UAVs to cooperatively monitor a line segment and then integrate their image information. The problem is proved to be NP-hard with infinite continuous solution space. For the first scenario, we propose a$(1-1/e)$-approximation algorithm. For the second one, we first propose a 1/2-approximation algorithm to address its simple version, and then propose a heuristic solution. Numerical evaluations validate the effectiveness of our proposed algorithms.

Continuous domestic respiration monitoring provides vital information for diagnosing assorted diseases. In this paper, we introduce RESPTRACKER, the first continuous, multiple-person respiration tracking system in domestic settings using acoustic-based COTS devices. RESPTRACKER uses a two-stage algorithm to separate and recombine respiration signals from multiple paths in a short period so that it can track the respiration rate of multiple moving subjects. Our experimental results show that our two-stage algorithm can distinguish the respiration of at least four subjects at a distance of three meters.

An increasing amount of users' sensitive information is now being collected for analytics purposes. Differential privacy has been widely studied in the literature to protect the privacy of users' information. The privacy parameter bounds the information about the data set leaked by the noisy output. Oftentimes, a data set needs to be used for answering multiple queries, so the level of privacy protection may degrade as more queries are answered. Thus, it is crucial to keep track of privacy budget spending, which should not exceed the given limit of privacy budget. Moreover, if a query has been answered before and is asked again on the same data set, we may reuse the previous noisy response for the current query to save the privacy cost. In view of the above, we design an algorithm to reuse previous noisy responses if the same query is asked repeatedly. In particular, considering that different requests of the same query may have different privacy requirements, our algorithm can set the optimal reuse fraction of the old noisy response and add new noise to minimize the accumulated privacy cost. Furthermore, we design and implement a blockchain-based system for tracking and saving differential-privacy cost. As a result, the owner of the data set will have full knowledge about how the data set has been used and be confident that no new privacy cost will be incurred for answering queries once the specified privacy budget is exhausted.

With the advent of the era of big data, deep learning has become a prevalent building block in a variety of machine learning or data mining tasks, such as signal processing, network modeling and traffic analysis, to name a few. The massive user data crowdsourced plays a crucial role in the success of deep learning models. However, it has been shown that user data may be inferred from trained neural models and thereby exposed to potential adversaries, which raises information security and privacy concerns. To address this issue, recent studies leverage the technique of differential privacy to design private-preserving deep learning algorithms. Albeit successful at privacy protection, differential privacy degrades the performance of neural models. In this paper, we develop ADADP, an adaptive and fast convergent learning algorithm with a provable privacy guarantee. ADADP significantly reduces the privacy cost by improving the convergence speed with an adaptive learning rate and mitigates the negative effect of differential privacy upon the model accuracy by introducing adaptive noise. The performance of ADADP is evaluated on real-world datasets. Experiment results show that it outperforms state-of-the-art differentially private approaches in terms of both privacy cost and model accuracy.

With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dilemmas in accident scenarios, such as staying the course resulting in hurting pedestrians or swerving leading to hurting passengers. To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision. Although a useful methodology for building ethical AI systems, such an approach can potentially violate the privacy of voters since moral preferences are sensitive information and their disclosure can be exploited by malicious parties resulting in negative consequences. In this paper, we report a first-of-its-kind privacy-preserving crowd-guided AI decision-making approach in ethical dilemmas. We adopt the formal and popular notion of differential privacy to quantify privacy, and consider four granularities of privacy protection by taking voter-/record-level privacy protection and centralized/distributed perturbation into account, resulting in four approaches VLCP, RLCP, VLDP, and RLDP, respectively. Moreover, we propose different algorithms to achieve these privacy protection granularities, while retaining the accuracy of the learned moral preference model. Specifically, VLCP and RLCP are implemented with the data aggregator setting a universal privacy parameter and perturbing the averaged moral preference to protect the privacy of voters' data. VLDP and RLDP are implemented in such a way that each voter perturbs her/his local moral preference with a personalized privacy parameter. Extensive experiments based on both synthetic data and real-world data of voters' moral decisions demonstrate that the proposed approaches achieve high accuracy of preference aggregation while protecting individual voter's privacy.

Respiration rate monitoring is beneficial for the diagnosis of a variety of diseases, such as heart failure and sleep disorders. Radio Frequency (RF) based respiration rate monitoring systems, namely ultra-wideband radar and COTS device, have been proposed without requiring any direct contact with the detected person. However, existing RF based systems either require expensive UWB radio (radar based) or work only in stationary environments (COTS device based). To address the limitations of both radar based and COTS device based systems, in this paper, we propose RespiRadio, a system that can detect a person's respiration rate in dynamic ambient environments via a single TX-RX pair of WiFi cards. The key novelty of RespiRadio is that it overcomes the limit of existing COTS device based respiration rate systems by synthesizing a wider-bandwidth WiFi radio. With the synthesized WiFi radio, we can identify the path reflected by the breathing person and then analyze the periodicity of the signal power measurements only from this path to infer the respiration rate. We experimentally evaluate the performance of RespiRadio in non-static indoor environments and the results demonstrate that the overall estimation error is 0.152 breaths per minute (bpm).

The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to secondary (or unlicensed) receivers/users on more than one broadcast channels via channel hopping (CH), because a single broadcast channel can be reclaimed by the primary (or licensed) user, leading to broadcast failures. Meanwhile, a secondary receiver needs to synchronize its clock with the BS's clock to avoid broadcast failures caused by the possible clock drift between the CH sequences of the secondary receiver and the BS. In this paper, we propose a CH-based broadcast protocol called SASS, which enables a BS to successfully broadcast to secondary receivers over multiple broadcast channels via channel hopping. Specifically, the CH sequences are constructed on basis of a mathematical construct- the Self-Adaptive Skolem Sequence (SASS). Moreover, each secondary receiver under SASS is able to adaptively synchronize its clock with that of the BS without any information exchanges, regardless of any amount of clock drift.

Given the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface, researchers have shown great interest in analyzing signal fluctuation on this interface for environmental perception. A popular example is the enabling of indoor localization with RF signals. As an alternative to active device-based positioning, device-free passive (DfP) indoor localization has the advantage that the sensed individuals do not require to carry RF sensors. We propose a probabilistic fingerprinting-based technique for DfP indoor localization. Our system adopts CSI readings derived from off-the-shelf WiFi 802.11n wireless cards which can provide fine-grained subchannel measurements in the context of MIMO-OFDM PHY layer parameters. This complex channel information enables accurate localization of non-equipped individuals. Our scheme further boosts the localization efficiency by using principal component analysis (PCA) to identify the most relevant feature vectors. The experimental results demonstrate that our system can achieve an accuracy of over 92% and an error distance smaller than 0.5m. We also investigate the effect of other parameters on the performance of our system, including packet transmission rate, the number of links as well as the number of principle components.

Given the penetration of mobile devices equipped with cameras, there has been increasing interest in enabling user interaction via visual codes. Simple examples like QR Codes abound. Since many codes like QR Codes are visually intrusive, various mechanisms have been explored to design visual codes that can be hidden inside regular images or videos, though the capacity of these codes remains low to ensure invisibility. We argue, however, that high capacity while maintaining invisibility would enable a vast range of applications that embed rich contextual information in video screens.

In cognitive radio (CR) networks, “TTR”, a.k.a. time-to-rendezvous, is one of the most important metrics for evaluating the performance of a channel hopping (CH) rendezvous protocol, and it characterizes the rendezvous delay when two CRs perform channel hopping. There exists a trade-off of optimizing the average or maximum TTR in the CH rendezvous protocol design. On one hand, the random CH protocol leads to the best “average” TTR without ensuring a finite “maximum” TTR (two CRs may never rendezvous in the worst case), or a high rendezvous diversity (multiple rendezvous channels). On the other hand, many sequence-based CH protocols ensure a finite maximum TTR (upper bound of TTR) and a high rendezvous diversity, while they inevitably yield a larger average TTR. In this paper, we strike a balance in the average-maximum TTR trade-off for CR rendezvous by leveraging the advantages of both random and sequence-based CH protocols. Inspired by the neighbor discovery problem, we establish a design framework of creating a wake-up schedule whereby every CR follows the sequence-based (or random) CH protocol in the awake (or asleep) mode. Analytical and simulation results show that the hybrid CH protocols under this framework are able to achieve a greatly improved average TTR as well as a low upper-bound of TTR, without sacrificing the rendezvous diversity.

We consider the detection of activities from non-cooperating individuals with features obtained on the radio frequency channel. Since environmental changes impact the transmission channel between devices, the detection of this alteration can be used to classify environmental situations. We identify relevant features to detect activities of non-actively transmitting subjects. In particular, we distinguish with high accuracy an empty environment or a walking, lying, crawling or standing person, in case-studies of an active, device-free activity recognition system with software defined radios. We distinguish between two cases in which the transmitter is either under the control of the system or ambient. For activity detection the application of one-stage and two-stage classifiers is considered. Apart from the discrimination of the above activities, we can show that a detected activity can also be localized simultaneously within an area of less than 1 meter radius.

We consider the recognition of activities from passive entities by analysing radio-frequency (RF)-channel fluctuation. In particular, we focus on the recognition of activities by active Software-defined-radio (SDR)-based Device-free Activity Recognition (DFAR) systems and investigate the localisation of activities performed, the generalisation of features for alternative environments and the distinction between walking speeds. Furthermore, we conduct case studies for Received Signal Strength (RSS)-based active and continuous signal-based passive systems to exploit the accuracy decrease in these related cases. All systems are compared to an accelerometer-based recognition system.

We introduce a passive system to recognise environmental situations. Differing from other RF-based approaches, our system has the advantage of neither installing a transmitter generating the signal nor equipping the monitored entities with any active component. When activities are performed, it consecutively samples ambient RF-signals, derived from a non-cooperating FM-radio source. Since changes in an environment impact the propagation of radio waves, this data implicitly contains information to distinguish environmental situations. We experimentally demonstrate the distinction of the situations 'empty room', 'opened door' and 'walking person' with an average accuracy of over 90%.

We introduce a novel activity recognition method based on the RF-signal originated from ambient FM radio source. For the purpose of classifying activities, we utilise a two stage approach which can initially distinguish between coarse-grained activities, then make further fine-grained recognition. Additionally, a study on features is conducted to investigate the most suitable combination to achieve the highest accuracy on the detection of activities. By comparing to a one stage classification process, the experimental results demonstrate the advantage of our designed approach.