Securing Large-Scale D2D Networks Using Covert Communication and Friendly Jamming

We exploit both covert communication and friendly jamming to propose a friendly jamming-assisted covert communication and use it to doubly secure a large-scale device-to-device (D2D) network against eavesdroppers (i.e., wardens). The D2D transmitters defend against the wardens by: 1) hiding their transmissions with enhanced covert communication, and 2) leveraging friendly jamming to ensure information secrecy even if the D2D transmissions are detected. We model the combat between the wardens and the D2D network (the transmitters and the friendly jammers) as a two-stage Stackelberg game. Therein, the wardens are the followers at the lower stage aiming to minimize their detection errors, and the D2D network is the leader at the upper stage aiming to maximize its utility (in terms of link reliability and communication security) subject to the constraint on communication covertness. We apply stochastic geometry to model the network spatial configuration so as to conduct a system-level study. We develop a bi-level optimization algorithm to search for the equilibrium of the proposed Stackelberg game based on the successive convex approximation (SCA) method and Rosenbrock method. Numerical results reveal interesting insights. We observe that without the assistance from the jammers, it is difficult to achieve covert communication on D2D transmission. Moreover, we illustrate the advantages of the proposed friendly jamming-assisted covert communication by comparing it with the information-theoretical secrecy approach in terms of the secure communication probability and network utility

Achieving Covert Communication in Large-Scale SWIPT-Enabled D2D Networks

We aim to secure a large-scale device-to-device (D2D) network against adversaries. The D2D network underlays a downlink cellular network to reuse the cellular spectrum and is enabled for simultaneous wireless information and power transfer (SWIPT). In the D2D network, the transmitters communicate with the receivers, and the receivers extract information and energy from their received radio-frequency (RF) signals. In the meantime, the adversaries aim to detect the D2D transmission. The D2D network applies power control and leverages the cellular signal to achieve covert communication (i.e., hide the presence of transmissions) so as to defend against the adversaries. We model the interaction between the D2D network and adversaries by using a two-stage Stackelberg game. Therein, the adversaries are the followers minimizing their detection errors at the lower stage and the D2D network is the leader maximizing its network utility constrained by the communication covertness and power outage at the upper stage. Both power splitting (PS)-based and time switch (TS)-based SWIPT schemes are explored. We characterize the spatial configuration of the large-scale D2D network, adversaries, and cellular network by stochastic geometry. We analyze the adversary's detection error minimization problem and adopt the Rosenbrock method to solve it, where the obtained solution is the best response from the lower stage. Taking into account the best response from the lower stage, we develop a bi-level algorithm to solve the D2D network's constrained network utility maximization problem and obtain the Stackelberg equilibrium. We present numerical results to reveal interesting insights

Road Grade Estimation Using Crowd-Sourced Smartphone Data

Estimates of road grade/slope can add another dimension of information to existing 2D digital road maps. Integration of road grade information will widen the scope of digital map's applications, which is primarily used for navigation, by enabling driving safety and efficiency applications such as Advanced Driver Assistance Systems (ADAS), eco-driving, etc. The huge scale and dynamic nature of road networks make sensing road grade a challenging task. Traditional methods oftentimes suffer from limited scalability and update frequency, as well as poor sensing accuracy. To overcome these problems, we propose a cost-effective and scalable road grade estimation framework using sensor data from smartphones. Based on our understanding of the error characteristics of smartphone sensors, we intelligently combine data from accelerometer, gyroscope and vehicle speed data from OBD-II/smartphone's GPS to estimate road grade. To improve accuracy and robustness of the system, the estimations of road grade from multiple sources/vehicles are crowd-sourced to compensate for the effects of varying quality of sensor data from different sources. Extensive experimental evaluation on a test route of ~9km demonstrates the superior performance of our proposed method, achieving $5\times$ improvement on road grade estimation accuracy over baselines, with 90\% of errors below 0.3$^\circ$.Comment: Proceedings of 19th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN'20

VehSense: Slippery Road Detection Using Smartphones

This paper investigates a new application of vehicular sensing: detecting and reporting the slippery road conditions. We describe a system and associated algorithm to monitor vehicle skidding events using smartphones and OBD-II (On board Diagnostics) adaptors. This system, which we call the VehSense, gathers data from smartphone inertial sensors and vehicle wheel speed sensors, and processes the data to monitor slippery road conditions in real-time. Specifically, two speed readings are collected: 1) ground speed, which is estimated by vehicle acceleration and rotation, and 2) wheel speed, which is retrieved from the OBD-II interface. The mismatch between these two speeds is used to infer a skidding event. Without tapping into vehicle manufactures' proprietary data (e.g., antilock braking system), VehSense is compatible with most of the passenger vehicles, and thus can be easily deployed. We evaluate our system on snow-covered roads at Buffalo, and show that it can detect vehicle skidding effectively.Comment: 2017 IEEE 85th Vehicular Technology Conference (VTC2017-Spring

A Generalized Packing Server for Scheduling Task Graphs on Multiple Resources

This paper presents the generalized packing server. It reduces the problem of scheduling tasks with precedence constraints on multiple processing units to the problem of scheduling independent tasks. The work generalizes our previous contribution made in the specific context of scheduling Map/Reduce workflows. The results apply to the generalized parallel task model, introduced in recent literature to denote tasks described by workflow graphs, where some subtasks may be executed in parallel subject to precedence constraints. Recent literature developed schedulability bounds for the generalized parallel tasks on multiprocessors. The generalized packing server, described in this paper, is a run-time mechanism that packs tasks into server budgets (in a manner that respects precedence constraints) allowing the budgets to be viewed as independent tasks by the underlying scheduler. Consequently, any schedulability results derived for the independent task model on multiprocessors become applicable to generalized parallel tasks. The catch is that the sum of capacities of server budgets exceeds by a certain ratio the sum of execution times of the original generalized parallel tasks. Hence, a scaling factor is derived that converts bounds for independent tasks into corresponding bounds for generalized parallel tasks. The factor applies to any work-conserving scheduling policy in both the global and partitioned multiprocessor scheduling models. We show that the new schedulability bounds obtained for the generalized parallel task model, using the aforementioned conversion, improve in several cases upon the best known bounds in current literature. Hence, the packing server is shown to improve the schedulability of generalized parallel tasks. Evaluation results confirm this observation.Ope

Exercise-Induced Th17 Lymphocyte Response and Their Relationship to Cardiovascular Disease Risk Factors in Obese, Post-Menopausal Women

Obesity-induced inflammation promotes type 2 diabetes and cardiovascular disease (CVD). A causative link between adaptive immunity and pathogenesis of obesity-associated diseases has been established. PURPOSE: To examine the effects of exercise on circulating T-helper (Th) 17 lymphocytes in overweight/obese post-menopausal women. METHODS: Twenty-seven overweight/obese women (BMI 32.7 ± 5.1 kg×m-2, 55-75 yr) were randomly assigned to the exercise (EX, n=14) or education (ED, n=13) groups. EX performed a 25-min walk (75-80% HRR) and 2 sets of 8 resistance exercises (70-80% 1RM) with blood samples obtained at: pre-exercise, post-exercise, one-hour and two-hour post-exercise. Blood samples were obtained at the same time points in resting ED. Whole blood was stained using the extracellular markers CD4, CD196, CD194, CD26, and CD161 to identify Th17 lymphocytes via flow cytometry. RESULTS: Acute exercise increased lymphocyte number (p = 0.0001), but decreased percent of CD4+ cells (p = 0.019) at PO. We observed a diurnal response (main effect) where CD26 expression was significantly lower by 2H compared to PRE (PR: 10631 ± 208; 2H: 9961 ± 271 MFI). There was a main effect (p=0.024) of group for CD26 expression (EX: 10745 ± 251; ED 9880 ± 260 MFI). The difference may have been driven by the apparent exercise-induced plateau of CD26 expression at 2H, which minimized the diurnal reduction observed in ED (p \u3e 0.05). There was a tendency (p = 0.09) for a group x time interaction in Th17 cell number at 1HR (EX = 25.3 ± 4.8; ED =37.2 ± 5.2 x 103 cells×ml-1). BMI was significantly correlated with Th17% (r = 0.5, p = 0.008). HbA1c was positively correlated with Th17 number and percentage (r = 0.598, p = 0.003; r = 0.614, p = 0.001, respectively), as well as CCR4+ Th17 cells (r = 0.421, p = 0.036). Multiple regression analysis revealed that BMI, fat percentage, and HbA1c were significant predictors (69%, r2 = 0.685) of Th17 cell %. CONCLUSION: Exercise reduced CD26 expression, the receptor responsible for Th17 cell migration, but did not significantly alter Th17 concentration (p = 0.09). CD26 upregulation may indicate that Th17 cells, via chemokine release, promote the stress-dependent migratory response of T-helper cells (CD4+). Obese individuals may experience a preferential differentiation of Th17 cells, based on their association with adiposity (BMI and %fat) and HbA1c

Acute Exercise-Induced Response of Platelet-Monocyte Complexes in Obese, Postmenopausal Women

Inactivity-related diseases such as cardiovascular disease (CVD) are linked to chronic low-grade, systemic inflammation. Platelet-monocyte complexes (PMCs) are markers of in vivo platelet activation and atherosclerosis, and may be early indicators of subclinical inflammation. PURPOSE: To examine the effects of an exercise bout on PMCs in those at risk for CVD. METHODS: Twenty-five overweight-obese (BMI 32.7 ± 5.2 kg×m-2, 55-75 yr) women were randomly assigned to either the exercise (EX, n=13) or non-exercise control (CON, n=12) group. EX performed 2 sets of 8 resistance exercises and a 25-min treadmill walk at 70-80% HRR. Blood was obtained pre-exercise (PR), post- (PO), 1-hour and 2 hours post-exercise (1HR and 2HR). Blood was obtained at the same time points in CON. PMCs were identified via flow cytometry and analyzed in each monocyte phenotype. Monocyte phenotypes were defined as: Mon1 (CD14+CD16−CCR2+), Mon2 (CD14+CD16+CCR2+), and Mon3 (CD14+CD16+CCR2−). All events positive for both CD14 and CD42a (marker for platelets) were considered PMCs. RESULTS: A main effect for time revealed an increase in PMC number at PO (p=0.036) which appears to have been driven by EX (EX = 61.5%; CON = 33.8% increase). PMCs formed with Mon1 and Mon2 followed a similar response. A significant group x time interaction for Mon3 PMC number (p=0.002) indicated an increase from PR to PO (PR = 5218±1170, PO = 8195±1152 cells·ml-1), and a decrease from PO to 1HR and 2HR (1HR = 3767±820 cells·ml-1 2HR = 3818±814 cells·ml-1) in EX. PMC number remained constant for CON at all timepoints. Estimated VO2max was negatively correlated with CD42a MFI (a marker of platelet density per monocyte) (r = -0.583, p = 0.003). Systolic blood pressure (SBP) positively correlated with percent PMC (% CD42a positive monocytes; r = 0.458, p = 0.042). CONCLUSION: Aerobic fitness appears to reduce platelet activation indicated by the negative relationship between VO2max and CD42a MFI. Chronic elevations in resting SBP are linked to PMC percentage, possibly due to sheer stress-induced platelet activation. It is possible that PMC elevation at PO is at least partially driven by exercise-induced increases in BP. These results support previous literature, indicating that PMCs are a CVD risk marker and may elucidate one mechanism by which physical fitness reduces risk for CVD

STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks

Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain. This observation leads to a fundamental question: For IoT applications, can one develop a new brand of neural network structures that synthesize features inspired not only by the biology of human perception but also by the fundamental nature of physics? Hence, in this paper, instead of using conventional building blocks (e.g., convolutional and recurrent layers), we propose a new foundational neural network building block, the Short-Time Fourier Neural Network (STFNet). It integrates a widely-used time-frequency analysis method, the Short-Time Fourier Transform, into data processing to learn features directly in the frequency domain, where the physics of underlying phenomena leave better foot-prints. STFNets bring additional flexibility to time-frequency analysis by offering novel nonlinear learnable operations that are spectral-compatible. Moreover, STFNets show that transforming signals to a domain that is more connected to the underlying physics greatly simplifies the learning process. We demonstrate the effectiveness of STFNets with extensive experiments. STFNets significantly outperform the state-of-the-art deep learning models in all experiments. A STFNet, therefore, demonstrates superior capability as the fundamental building block of deep neural networks for IoT applications for various sensor inputs