Power line communication (PLC) is emerging as the preferred connectivity solution to enlarge WiFi’s communication range for mobile clients. However, we have to consider the mobility in power-line assisted WiFi for a seamless mobile experience and wide connectivity. To this end, in this paper, we exploit the power line backbone to achieve seamless mobility and wide connectivity for mobile clients in a power line-assisted wireless transit network. To do so, we introduce TurboGrid that characterizes the power-line backbone and allows the network infrastructure to make performance-impacting decisions on roaming between PLC adapters. We first characterize the power line channel over time and space in the powerline infrastructure. Then, we show that the existing WiFi handover approaches cannot be directly applied when integrating power lines to assist WiFi communication. Therefore, we propose to integrate the link quality on the power line backbone with link quality over the air for seamless mobile client switching. The systematic evaluation and implementation of commodity WiFi AP and PLC adapters show the efficacy of our TurboGrid during the mobile client’s mobility.

This paper addresses the critical challenge of resource saturation in fog computing paradigms by proposing novel solicitation-based migration methods that alleviate near-saturated nodes. Unlike traditional reactive approaches that trigger migration post-saturation, the proposed methods employ continuous monitoring wherein adjacent nodes transmit beacon messages to advertise their instantaneous load levels. First, a dual least load and delay (LD) solicitation approach that balances resource utilization with link delay. Second, an affinity-based solicitation that migrates virtual network functions (VNFs) to nodes already hosting related elements from the same service function chain (SFC), thus preserving locality and reducing fragmentation. Simulations demonstrate that both methods outperform baseline proactive load balancing and reactive greedy search approaches at high traffic volumes in terms of saturation levels, migration iterations, service downtime, and SFC fragmentation. The affinity method particularly excels in minimizing SFC fragmentation and service disruption, while the LD approach provides saturation resistance across varying traffic volumes.

Internet-of-things (IoT) devices (e.g., micro camera and microphone) are usually small form factor, low-cost, and low-power, which makes them easy to conceal and deploy in the indoor environment to spy on people for human private information such as location and indoor activities. As a result, these IoT devices introduce a great privacy and ethical threat. Therefore, it is important to reveal these concealed IoT devices in the indoor environment for human privacy protection. This paper presents RFScan, a system that can passively detect, fingerprint, and localize diverse concealed IoT devices in the indoor environment by sensing their unintentional electromagnetic emanations. However, sensing these emanations is challenging due to the weak emanation strength and the interference from the ambient wireless communication signals. To this end, we boost the emanation strength through the non-coherent averaging based on the emanation signal's characteristics and design a novel suppression algorithm to mitigate interference from the wireless communication signals. We further profile emanations across frequency and time that act as the emanation source's unique signature and customize a deep neural network architecture to fingerprint the emanation sources. Furthermore, we can localize the emanation source with an angle-of-arrival (AoA) based triangulation approach. Our experimental results demonstrate the efficiency of the IoT devices' detection, fingerprinting, and localization across different indoor environments.

The proliferation of transmissions in the RF spectrum demands robust and responsive signal analysis techniques to detect and label malicious activity. Traditional methods struggle to balance sensitivity and accuracy in real-time. This paper introduces Nomad, a modular system that combines global spectral pattern recognition with localized energy detection and classic signal processing methods for efficient and accurate RF analysis. Nomad's innovative architecture facilitates rapid development and deployment, while its intuitive visualizations provide actionable insights into even weak signals within complex RF environments.

Human-centered wireless sensing (HCWS) aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around him/her. While the sensed information about a human can be used for many good purposes such as enhancing life quality, an adversary can also abuse it to steal private information about the human (e.g., location and person's identity). However, the literature lacks a systematic understanding of the privacy vulnerabilities of wireless sensing and the defenses against them, resulting in the privacy-compromising HCWS design. In this work, we aim to bridge this gap to achieve the vision of secure human-centered wireless sensing. First, we propose a signal processing pipeline to identify private information leakage and further understand the benefits and tradeoffs of wireless sensing-based inference attacks and defenses. Based on this framework, we present the taxonomy of existing inference attacks and defenses. As a result, we can identify the open challenges and gaps in achieving privacy-preserving human-centered wireless sensing in the era of machine learning and further propose directions for future research in this field.

In this paper, we focus on the tasks scheduling problem in UAV swarm network with power constraint on each UAV. To do so, we propose a distributed optimal scheduling algorithm to achieve optimal task allocation for UAVs with limited power constraint. Our algorithm does not require any prior knowledge of each UAV's state. Hence, each UAV can make its own decision on performing the tasks. The design of our algorithm is enabled by the stochastic network optimization and distributed correlated scheduling. We demonstrate the optimum of our algorithm through theoretical analysis and simulations.

In this paper, we present RFDiaper, a commodity passive RFID based healthy diapering system, which can sense the diaper wetness (i.e., wet/dry) and identify pH value of urine absorbed by the diaper. To do so, we leverage the coupling effect between the urine absorbed by the diaper and RFID tag, thereby the phase and amplitude variation can indicate urine pH and diaper wetness. However, rich scattering and dynamic environment exhibit a great challenge for accurate diaper wetness sensing and urine pH identification. Therefore, we propose a twin-tag based dynamic environment mitigation approach for robust and healthy diapering. Specifically, by extracting the differential amplitude and phase from the co-located sensing tag and reference tag (i.e., twin-tag) attached on the diaper, the multipath effect and the other dynamic factors (e.g., diaper wearer's body, tag's orientation and temperature, etc.) can be mitigated. Then, we detect the diaper wetness and estimate the urine pH based on differential amplitude and phase. We have implemented RFDiaper's design and evaluated its effectiveness with the experiments using commercial off-the-shelf (COTS) RFID tags attached on the diaper worn by the doll and the human subjects. RFDiaper can achieve the median accuracy of around 96% for diaper wetness sensing and urine pH estimation error of around 0.23 in dynamic environment.

The recent advancement of web-scale digital advertising saw a paradigm shift from the conventional focus of digital advertisement distribution towards integrating digital processes and methodologies and forming a seamless workflow of advertisement design, production, distribution, and effectiveness monitoring. In this work, we implemented a computational framework for the predictive analysis of the content-based features extracted from advertisement video files and various effectiveness metrics to aid the design and production processes of commercial advertisements. Our proposed predictive analysis framework extracts multi-dimensional temporal patterns from the content of advertisement videos using multimedia signal processing and natural language processing tools. The pattern analysis part employs an architecture of cross modality feature learning where data streams from different feature dimensions are employed to train separate neural network models and then these models are fused together to learn a shared representation. Subsequently, a neural network model trained on this joint representation is utilized as a classifier for predicting advertisement effectiveness. Based on the predictive patterns identified between the content features and the effectiveness metrics of advertisements, we have elicited a useful set of auditory, visual and textual patterns that is strongly correlated with the proposed effectiveness metrics while can be readily implemented in the design and production processes of commercial advertisements. We validate our approach using subjective ratings from a dedicated user study, the text sentiment strength of online viewer comments, and a viewer opinion metric of the likes/views ratio of each advertisement from YouTube video-sharing website.

Crowd sourced sensing over smartphones presents a new paradigm for collecting sensing data over a vast area for real-time monitoring applications. A monitoring application may require different types of sensing data, while under a budget constraint. This paper explores the crucial problem of maximizing the aggregate data utility of heterogeneous sensing tasks while maintaining utility-centric fairness across different tasks under a budget constraint. In particular, we take the redundancy of sensing data into account. This problem is highly challenging given its unique characteristics including the intrinsic trade off between aggregate data utility and fairness, and the large number of smartphones. We propose a fairness-aware distributed approach to solving this problem. To overcome the intractability of the problem, we decompose it to two sub problems of recruiting smartphones under a budget constraint and allocating workloads of sensing tasks. For the first sub problem, we propose an efficient greedy algorithm which has a constant approximation ratio of two. For the second problem, we apply dual based decomposition based on which we design a distributed algorithm for determining the workloads of different tasks on each recruited smartphone. We have implemented our distributed algorithm on a windows-based server and Android-based smartphones. With extensive simulations we demonstrate that our approach achieves high aggregate data utility while maintaining good utility-centric fairness across sensing tasks.

Spectrum sharing between the licensed primary user (PU) and secondary users (SUs) has been increasingly important. We consider a utility maximization framework for spectrum sharing among cognitive SUs and the PU in multi-hop cognitive radio networks. We propose an efficient distributed algorithm for solving the spectrum allocation problem. Extensive simulations have been performed and the results show that our algorithm achieves much better network utility than other compared algorithms.

In this paper we study a social welfare maximization problem for spectrum sharing in cognitive radio networks. To fully use the spectrum resource, the spectrum owned by the licensed primary user (PU) can be leased to secondary users (SUs) for transmitting data. We first formulate the social welfare of a cognitive radio network, considering the cost for the primary user sharing spectrum and the utility gained for secondary users transmitting data. The social welfare maximization is a convex optimization, which can be solved by standard methods in a centralized manner. However, the utility function of each secondary user always contains the private information, which leads to the centralized methods disabled. To overcome this challenge, we propose an iterative distributed algorithm based on a pricing-based decomposition framework. It is theoretically proved that our proposed algorithm converges to the optimal solution. Numerical simulation results are presented to show that our proposed algorithm achieves optimal social welfare and fast convergence speed.