Chaocan Xiang

PassFit: Participatory Sensing and Filtering for Identifying Truthful Urban Pollution Sources

With the increasing ubiquitous usage of the smart phone, participatory sensing networks become applicable for urban sensing applications. Notably, in pollution monitoring applications, untrained participants and heterogeneously unreliable sensors bring much noise into the reported data. Status quo researches fail to identify truthful pollution sources when the parameters are not known. In this paper, we cluster the reported data and make parameter estimations of the pollution sources. Inspired by the expectation maximization method, we estimate the existence of the pollution sources and leverage these results for noise estimations iteratively. The key insight is that, we can identify the truthful pollution sources with the noisy data mitigation iteratively. Further, theoretical analysis also shows that, we can achieve optimal estimation in the sense of maximum likelihood (ML). Simulation results show that, compared with basic ML algorithm, we improve the false positive and false negative of identification by 99% and 82%, respectively, at the same time, the mean error and the deviation error of noise estimation by 49% and 70%, respectively.

QoS-based service selection with lightweight description for large-scale service-oriented internet of things

Quality of Service (QoS)-based service selection is the key to large-scale service-oriented Internet of Things (IOT), due to the increasing emergence of massive services with various QoS. Current methods either have low selection accuracy or are highly time-consuming (e.g., exponential time complexity), neither of which are desirable in large-scale IOT applications. We investigate a QoS-based service selection method to solve this problem. The main challenges are that we need to not only improve the selection accuracy but also decrease the time complexity to make them suitable for large-scale IOT applications. We address these challenges with the following three basic ideas. First, we present a lightweight description method to describe the QoS, dramatically decreasing the time complexity of service selection. Further more, based on this QoS description, we decompose the complex problem of QoS-based service selection into a simple and basic sub-problem. Finally, based on this problem decomposition, we present a QoS-based service matching algorithm, which greatly improves selection accuracy by considering the whole meaning of the predicates. The traces-driven simulations show that our method can increase the matching precision by 69% and the recall rate by 20% in comparison with current methods. Moreover, theoretical analysis illustrates that our method has polynomial time complexity, i.e., O(m 2 × n), where m and n denote the number of predicates and services, respectively.

Calibrate without Calibrating: An Iterative Approach in Participatory Sensing Network

With widespread usages of smart phones, participatory sensing becomes mainstream, especially for applications requiring pervasive deployments with massive sensors. However, the sensors on smart phones are prone to the unknown measurement errors, requiring automatic calibration among uncooperative participants. Current methods need either collaboration or explicit calibration process. However, due to the uncooperative and uncontrollable nature of the participants, these methods fail to calibrate sensor nodes effectively. We investigate sensor calibration in monitoring pollution sources, without explicit calibration process in uncooperative environment. We leverage the opportunity in sensing diversity, where a participant will sense multiple pollution sources when roaming in the area. Further, inspired by expectation maximization (EM) method, we propose a two-level iterative algorithm to estimate the source presences, source parameters and sensor noise iteratively. The key insight is that, only based on the participatory observations, we can “calibrate sensors without explicit or cooperative calibrating process”. Theoretical analysis proves that, our method can converge to the optimal estimation of sensor noise, where the likelihood of observations is maximized. Also, extensive simulations show that, ours improves the estimation accuracy of sensor bias up to 20 percent and that of sensor noise deviation up to 30 percent, compared with three baseline methods.

CARM: Crowd-Sensing Accurate Outdoor RSS Maps with Error-Prone Smartphone Measurements

Received Signal Strength (RSS) maps provide fundamental information for mobile users, aiding the development of conflict graph and improving communication quality to cope with the complex and unstable wireless channels. In this paper, we present CARM: a scheme that exploits crowd-sensing to construct outdoor RSS maps using smartphone measurements. An alternative yet impractical approach in literature is to appeal to professionals with customized devices. Our work distinguishes itself from previous studies by supporting off-the-shelf smartphone devices, and more importantly, by mitigating the error-prone nature and inaccuracies of these devices to build RSS maps through crowd-sensing. The main challenges are that, we need to calibrate error-prone smartphone measurements with “inaccurate” and “incomplete” data. To address these challenges, we build the measurement error model of smartphone based on the experimental observations and analyses. Moreover, we propose an iterative method based on Davidon-Fletcher-Powell (DFP) algorithm, to estimate the parameters for the error models of each smartphone and the signal propagation models of each AP simultaneously. The key intuition is that, the calibrated measurements based on the error model are constrained by the physics of the signal propagation model. Finally, a model-driven RSS map construction scheme is built upon these two models with these estimated parameters. The theoretical analyses prove the optimality and convergence of this iterative method. Also, the crowd-sensing experiments show that, CARM can achieve an accurate RSS map, decreasing the average error from 19.8 to 8.5 dBm.

Walk globally, act locally: efficient influential user identification in mobile social networks

Being a fundamental and challenging research topic, influential user identification has attracted much attention with the rapid growth of mobile social networks. Previous studies either focus on relatively stable network structure, or need fairly large information overhead in achieving global maxima. In tackling the dynamic topologies, we propose an influential user identification scheme fully exploiting the active mobile users, where the stable state property is leveraged under information potential construction scheme. We present an efficient routing scheme in reaching the global maxima without relying on specific routing protocols. We validate our scheme with both synthetic and real-world mobility traces. The experimental results show that, the proposed scheme achieves considerable performance on influential user identification and route construction, while bringing forth less overhead.

Taming the big to small: efficient selfish task allocation in mobile crowdsourcing systems

This paper investigates the selfish load balancing problem in mobile distributed crowdsourcing networks. Conventional methods heavily relied on cooperation among users to achieve balanced resource utilization in a platform‐centric view. In achieving fairly low communication and computational overhead, this work leverages the d‐choice method based on Ball and Bin theory for effective balancing under limited information and the Proportional Allocation scheme for selfish load balancing, maintaining good load balancing property among selfish users. Even with limited information, the balancing performance could be improved significantly. Moreover, theoretical analysis has been presented in convergence property. Extensive evaluations have been made to show that Chance‐Choice outperforms several existing algorithms. Typically, comparing with Proportional Allocation scheme, it could decrease the load gap between the maximum and the minimal in system by 50% to 80% and reduce the overhead complexity from O(n) to O(1) comparing with the Max‐weight Best Response algorithm, where n denotes the number of mobile users in a crowdsourcing system. Copyright

Near Optimal Mobile Advertisement User Selection with Interested Area Coverage

Mobile advertisement distribution effects are vitally important for advertisers as well as users. Status quo studies are lacking of efficient distribution especially when user traces and budgets are involved. In achieving efficient and effective mobile advertisement applications, this work advocates the concept of location-centric mobile crowdsourcing network instead of conventional user-centric and platform, where locations are vitally important for advertisement distribution. To this end, this work focuses on the mobile advertisement user selection problem when interested area coverage (IAC) is considered. Unfortunately, developing location-centric needs to deal with the spatio-temporal features in each user, and IAC coverage needs to be effectively counted. Even worse, budget constraint makes this problem intractable. In tackling aforementioned challenges, this work makes following efforts: First, a budget-constrained user selection problem is formulated when location sensitive mobile advertisement applications are considered, which is proved to be NP-hard. Second, the submodularity feature is explored, and a simple but efficient heuristic algorithm is presented with guaranteed approximation ratio (1–1/e). Finally, extensive simulation results show that, our scheme could effectively improve the propagation effects for mobile advertisement with 125%.

iDial: Enabling a Virtual Dial Plate on the Hand Back for Around-Device Interaction

Smart wearable devices have become pervasive and are playing a more important role in our everyday lives. However, the small screen size and very few buttons make the interaction and control cumbersome and inconvenient. Previous solutions to mitigate this problem either require extra dedicated hardware, or instrument the users fingers with special purpose sensors, limiting their real-life applications. We present iDial, a novel real time approach that enables a virtual dial plate on the hand back, extending the interaction beyond the small screen of wearable devices. iDial only employs the already built-in microphone and motion sensors of the commercial-off-the-shelf (COTS) device to facilitate interactions between user and wearable, without any extra hardware involved. The key idea is to exploit the acoustic signatures extracted from passive subtle acoustic signals to accurately recognize the virtual keys input on the skin of the hand back. We innovatively locate the virtual keys on the 4 pieces of metacarpal bones to significantly reduce the possibility of casual inputs. iDial also takes advantages of the motion sensor fusion already available inside the wearable to achieve robustness against the ambient noise and human voices efficiently. We design and implement iDial on the Samsung Gear S2 smartwatch. Our extensive experiments show that iDial is able to achieve an average recognition accuracy of 96.7%, and maintain high accuracies across varying user behaviors and different environments. iDial achieves a below 0.5s end-to-end latency with all the computations and processes happening at the cheap commodity wearable.

Cloud is safe when compressive: Efficient image privacy protection via shuffling enabled compressive sensing

Abstract Cloud-assisted image services are widely used for various applications. Due to the high computational complexity of existing image encryption techniques, privacy protection becomes extremely challenging for resource-constrained smart devices. We propose eCIS, a cloud-assisted image service where compression and encryption are jointly used for image processing efficiency. eCIS could shift the high computational cost from encoder and decoder to the cloud while providing efficient and adaptive image privacy protection for end users. The key idea is to leverage different measurement matrices for sampling device and cloud. Thus encryption is realized with shuffling enabled compressive sensing. We conduct in-depth theoretical analysis and demonstrate the effectiveness with evaluations. To this end, extensive experimental results show that eCIS can effectively protect image privacy and meet user’s adaptive security requirements. Meanwhile, our experimental results show that eCIS could significantly save the system running time compared with existing cloud-assisted schemes.

Counter-strike: accurate and robust identification of low-level radiation sources with crowd-sensing networks

The use of crowd-sensing networks is a promising and low-cost way for identifying low-level radiation sources, which is greatly important for the security protection of modern cities. However, it is challenging to identify radiation sources based on inaccurate crowd-sensing measurements with unknown sensor efficiency, due to uncontrollable nature of users. However, existing methods assume the sensor efficiency is available, while their identification accuracy tightly depends on identification threshold. To address these problems, we present Counter-Strike, an accurate and robust identification method. Specifically, we use truthful probability of sources for robust identification. And then, we propose an iterative truthful-source identification algorithm, alternately iterating between sensor efficiency estimation and truthful probability estimation, gradually improving the identification accuracy. The extensive simulations and theoretical analysis show that our method can converge into the maximum likelihood of crowd-sensing measurements, achieving much higher identification accuracy than the existing methods. Further, the identification threshold makes slight influence on the identification accuracy in our method, facilitating its practical use.