With the popularity of Mobility-on-Demand (MOD) vehicles, a new market called MOD-Vehicular-Crowdsensing (MOVE-CS) was introduced for drivers to earn more by collecting road data. Unfortunately, MOVE-CS failed after two years of operation. To identify the root cause, we survey 581 drivers and reveal its simple operation model based on blindly competitive rewards. This model brings most drivers few yields, resulting in their withdrawals. In contrast, a similar market termed MOD-Human-Crowdsensing (MOMAN-CS) remains successful thanks to a complex model based on
exclusively customized rewards. Hence, we wonder whether MOVE-CS can be resurrected by learning from MOMAN-CS. Despite considerable similarity, we can hardly apply the operation model of MOMAN-CS to MOVE-CS, since drivers are also concerned with passenger missions that dominate their earnings. To this end, we analyze a large-scale dataset of 12,493 MOD vehicles, finding that drivers have explicit preference for short-term, immediate gains as well as implicit rationality in pursuit of long-term, stable profits. Therefore, we design a
novel operation model for MOVE-CS, at the heart of which lies a spatial-temporal differentiation-aware task recommendation scheme empowered by submodular optimization. Applied to the dataset, our design would essentially benefit both the drivers and platform, thus possessing the potential to resurrect MOVE-CS.

IFTTT is a programming framework for Applets (i.e., user customized policies with a "trigger-action" syntax), and is the most popular Home Internet-of-Things (H-IoT) platform. The execution of an Applet prompted by a device operation suffers from a long delay, since IFTTT has to periodically reads the states of the device to determine whether the trigger is satisfied, with an interval of up to 5 min for professionals and 60 min for normal users. Although IFTTT sets up a flexible polling interval based on the past several times an Applet has run, the delay is still around 2 min even for frequently executed Applets. This paper proposes a novel trigger notification mechanism "RTX-IFTTT" to implement real-time execution of Applets. The mechanism does not require any changes to the current IFTTT framework or the H-IoT devices, but only requires an H-IoT edge node (e.g., router) to identify the device events (e.g., turning on/off) and notify IFTTT to perform the action of an Applet when an identified event is the trigger of that Applet. The experimental results show that the averaged Applet execution delay for RTX-IFTTT is only about 2 sec.

While Mobile CrowdSensing (MCS) has become a popular paradigm that recruits mobile users to carry out various sensing tasks collaboratively, the performance of MCS is frequently degraded due to the limited spatiotemporal coverage in data collection. A possible way here is to incorporate sparse MCS with data inference, where unsensed data could be completed through prediction. However, the spatiotemporal data inference is usually "fractured" with poor performance, because of following challenges: 1) the sparsity of the sensed data, 2) the unpredictability of a spatiotemporal fracture and 3) the complex spatiotemporal relations. To resolve such fracture data issues, we elaborate a data generative model for achieving spatiotemporal fracture data inference in sparse MCS. Specifically, an algorithm named Generative High-Fidelity Matrix Completion (GHFMC) is proposed through combining traditional Deep Matrix Factorization (DMF) and Generative Adversarial Networks (GAN) for generating spatiotemporal fracture data. Along this line, GHFMC learns to extract the features of spatiotemporal data and further efficiently complete and predict the unsensed data by using Binary Cross Entropy (BCE) loss. Finally, we conduct experiments on three popular datasets. The experimental results show that our approach performs higher than the state-of-the-art (SOTA) baselines in both data inference accuracy and fidelity.

As a cost-effective paradigm, Sparse Crowdsensing (SC) aims to recruit workers to perform a part of sensing tasks and infer the rest. In most cases, workers participate in real time, and thus their sensing data are coming dynamically. Taking full advantage of the online coming data to complete the full map is an important problem. However, for data completion, the importance of data collected from spatio-temporal areas is different and time-varying. Moreover, the area importance also influences the subsequent worker selection, i.e., selecting suitable workers to actively sense important areas (instead of passively waiting for a given set of data) for improving completion accuracy. To this end, we propose a framework for ONline Sparse Crowdsensing, called ON-SC, which consists of three parts: data completion, importance estimation, and worker selection. We start from the dynamically coming data and propose an online matrix completion algorithm with spatio-temporal constraints. Based on that, we estimate the spatio-temporal area importance by conducting a reinforcement learning-based up-to-date model. Finally, we utilize the prophet secretary problem to select suitable workers to sense important areas for accurate completion in an online manner. Extensive experiments on real-world data sets show the effectiveness of our proposals.

Contact-free vital-signs monitoring enabled by radio frequency (RF) sensing is gaining increasing attention, thanks to its non-intrusiveness, noise-resistance, and low cost. Whereas most of these systems only perform respiration monitoring or retrieve heart rate, few can recover fine-grained heartbeat waveform. The major reason is that, though both respiration and heartbeat cause detectable micro-motions on human bodies, the former is so strong that it overwhelms the latter. In this paper, we aim to answer the question in the paper title, by demystifying how heartbeat waveform can be extracted from RF-sensing signal. Applying several mainstream methods to recover heartbeat waveform from raw RF signal, our results reveal that these methods fail to achieve what they have claimed, mainly because they assume linear signal mixing whereas the composition between respiration and heartbeat can be highly nonlinear. To overcome the difficulty of decomposing nonlinear signal mixing, we leverage the power of a novel deep generative model termed variational encoder-decoder (VED). Exploiting the universal approximation ability of deep neural networks and the generative potential of variational inference, VED demonstrates a promising capability in recovering fine-grained heartbeat waveform from RF-sensing signal; this is firmly validated by our experiments with 12 subjects and 48-hour data.

Energy and cost are two primary concerns when leveraging drones for urban sensing. With the advances of wireless charging technologies and the inspiration from the sparse crowdsensing paradigm, this paper proposes a novel drone-based collaborative sparse-sensing framework DroneSense, demonstrating its feasibility for sustainable urban-scale sensing. We focus on a typical use case, i.e., leveraging DroneSense to sense open parking spaces. DroneSense selects a minimum number of Points of Interest (POIs) to schedule drones for physical data sensing and then infers the parking occupancy of the remaining POIs to meet the overall quality requirement. However, drone-based sensing is different from human-centric crowdsensing, resulting in a series of new problems, including which POIs are visited first, when and where to charge drones, which drones to charge first, how much to charge, and when to stop the scheduling. To this end, we design a holistic solution, including context-aware matrix factorization for parking occupancy data inference, progressive determination of task quantity, deep reinforcement learning (DRL) based task selection, energy-aware DRL-based task scheduling, and adaptive charger scheduling. Extensive experiments with a real-world on-street parking dataset from Shenzhen, China demonstrate the obvious advantages of DroneSense.

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measure from each query, e.g., phase, RSS, etc. It is sufficient to fulfill the designs bounded to tag's own movement, e.g., tag's localization, while it imposes inevitable uncertainty to a larger spectrum of the sensing tasks that rely on the features extracted from RFID signals, which limits the fidelity of RFID sensing fundamentally and prevents its broader usage in more sophisticated scenarios. This paper presents RF-Wise to push the limit of RFID sensing, which is motivated by an insightful observation to customize RFID signals. RF-Wise can enrich the prior single-dimensional measure to a channel state information (CSI)-like measure with up to 150 dimensional samples across frequencies concurrently. More importantly, RF-Wise is a software solution atop standard RFID without using any extra hardware, requires only one tag for sensing, works within ISM band and is compatible to EPC Gen2 protocol. RF-Wise so far as we know is the first system of such a kind. We develop a RF-Wise prototype. Extensive experiments show that RF-Wise does not impact underlying RFID communications, while by using RF-Wise's features, applications' sensing performance can be improved remarkably.

With the rapid development of mobile devices and the fast increase of sensitive data, secure and convenient mobile authentication technologies are desired. Except for traditional passwords, many mobile devices have biometric-based authentication methods (e.g., fingerprint, voiceprint, and face recognition), but they are vulnerable to spoofing attacks. To solve this problem, we study new biometric features which are based on the dental occlusion and find that the bone-conducted sound of dental occlusion collected in binaural canals contains unique features of individual bones and teeth. Motivated by this, we propose a novel authentication system, TeethPass, which uses earbuds to collect occlusal sounds in binaural canals to achieve authentication. We design an event detection method based on spectrum variance and double thresholds to detect bone-conducted sounds. Then, we analyze the time-frequency domain of the sounds to filter out motion noises and extract unique features of users from three aspects: bone structure, occlusal location, and occlusal sound. Finally, we design an incremental learning-based Siamese network to construct the classifier. Through extensive experiments including 22 participants, the performance of TeethPass in different environments is verified. TeethPass achieves an accuracy of 96.8% and resists nearly 99% of spoofing attacks.

Programmable switches have been proposed in today's network to enable flexible reconfiguration of devices and reduce time-to-deployment. Buffer sizing, an important factor for network performance, however, has not received enough attention in programmable network. The state-of-the-art buffer sizing solutions usually employ either fixed buffer size or adjust the buffer size heuristically. Without programmability, they suffer from either massive packet drops or large queueing delay in dynamic environment. In this paper, we propose Adaptive Buffer Sizing (ABS), a low-cost and deploy-friendly framework compatible with programmable network. By decoupling the data plane and control plane, ABS-capable switches only need to react to the actions from controller, optimizing network performance in run-time under dynamic traffic. Meanwhile, actions can be programmed by particular Machine Learning (ML) models in the controller to meet different network requirements. In this paper, we address two specific ML models, a reinforcement learning model for relatively stable network with user specific quality requirements, and a supervised learning model for highly dynamic network scenarios. We implement the ABS framework by integrating the prevalent network simulator NS-2 with ML module. The experiment shows ABS outperforms state-of-the-art buffer sizing solutions by up to 38.23x under various network environments.

Several key internet routing protocols like OSPF and ISIS use shortest path routing to transport traffic from the ingress node to the egress node. These shortest paths are computed with respect to the weights on the links in the underlying connectivity graph. Since the routed paths depend on the link weights, a fundamental problem in network routing is to determine the set of weights that minimize congestion in the network. This is an NP-hard combinatorial optimization problem and several heuristics have been developed to determine the set of link weights to minimize congestion. In this paper, we develop a smoothed version of the weight setting problem and use gradient descent in the PyTorch framework to derive approximate solutions to this problem. We show the improvement in performance compared to traditional approaches on several benchmark networks.

Despite the evolution of distributed machine learning (ML) systems in recent years, the communication overhead induced by their data transfers remains a major issue that hampers the efficiency of such systems, especially in edge networks with poor wireless link conditions. In this paper, we propose to explore a new paradigm of error-tolerant distribute ML to mitigate the communication overhead. Unlike generic network traffic, ML data exhibits an intrinsic error-tolerant capability which helps the model yield fair performance even with errors in the data transfers. We first characterize the error tolerance capability of state-of-art distributed ML frameworks. Based on the observations, we propose NeuroMessenger, a lightweight mechanism that can be built into the cellular network stack, which can enhance and utilize the error tolerance in ML data to reduce communication overhead. NeuroMessenger does not require per-model profiling and is transparent to application layer, which simplifies the development and deployment. Our experiments on a 5G simulation framework demonstrate that NeuroMessenger reduces the end-to-end latency by up to 99% while maintaining less than low accuracy loss under various link conditions.

Recently, there have been renewed interests in applying machine learning (ML) techniques to wireless systems. However, ML-based approaches often require large amount of data in training and prior ML-based symbol detectors usually adopt off-line learning approaches which are not applicable to real-time processing. In this paper, echo state network (ESN), a prominent type of reservoir computing (RC), is applied to the symbol detection task in MIMO-OFDM systems. Our RC-based approach avoids the model-mismatch problem in traditional model-based receivers and consistently achieves better performance. Furthermore, two new ESN training methods, namely recursive-least-square (RLS) and generalized adaptive weighted recursive-least-square (GAW-RLS) are introduced, together with a decision feedback mechanism to further improve the training procedure. Simulation study shows that the proposed methods can achieve better performance than previous conventional and ML-based symbol detectors. Finally, the effectiveness of our RC-based approach is validated with a software-defined radio (SDR) transceiver and extensive field tests in various real-world scenarios. To the best of our knowledge, this is the first report of a real-time SDR implementation for ML-based MIMO-OFDM symbol detector. Our work provide a strong indication that ML-based signal processing could be a promising and key approach for future wireless networks.