Chang Tian

Quality-Aware Sensing Coverage in Budget-Constrained Mobile Crowdsensing Networks

Mobile crowdsensing has shown elegant capacity in data collection and has given rise to numerous applications. In the sense of coverage quality, marginal works have considered the efficient (less cost) and effective (considerable coverage) design for mobile crowdsensing networks. We investigate the optimal quality-aware coverage in mobile crowdsensing networks. The difference between ours and the conventional coverage problem is that we only select a subset of mobile users so that the coverage quality is maximized with constrained budget. To address this new problem, which is proved to be NP-hard, we first prove that the set function of coverage quality is nondecreasing submodular. By leveraging the favorable property in submodular optimization, we then propose an (1 - (1/e)) approximation algorithm with O(nk+2) time complexity, where k is an integer that is greater than or equal to 3. Finally, we conduct extensive simulations for the proposed scheme, and the results demonstrate that ours outperforms the random selection scheme and one of the state of the art in terms of total coverage quality by, at most, 2.4× and 1.5× and by, on average, 1.4× and 1.3×, respectively. Additionally, ours achieves a near-optimal solution, compared with the brute-force search results.

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.

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.

Network coding-based multicast in multi-hop CRNs under uncertain spectrum availability

The benefits of network coding on multicast in traditional multi-hop wireless networks have already been demonstrated in previous works. However, most existing approaches cannot be directly applied to multi-hop cognitive radio networks (CRNs), given the unpredictable primary user occupancy on licensed channels. Specifically, due to the unpredictable occupancy, the channels bandwidth is uncertain and thus the capacity of the link using this channel is also uncertain, which may result in severe throughput loss. In this paper, we study the problem of network coding-based multicast in multi-hop CRNs considering the uncertain spectrum availability. To capture the uncertainty of spectrum availability, we first formulate our problem as a chance-constrained program. Given the computationally intractability of the above program, we transform the original problem into a tractable convex optimization problem, through appropriate Bernstein approximation together with relaxation on link scheduling. We further leverage Lagrangian relaxation-based optimization techniques to propose an efficient distributed algorithm for the original problem. Extensive simulation results show that, the proposed algorithm achieves higher multicast rates, compared to a state-of-the-art non-network coding algorithm in multi-hop CRNs, and a conservative robust algorithm that treats the link capacity as a constant value in the optimization.

Toward Optimum Crowdsensing Coverage With Guaranteed Performance

Mobile crowdsensing networks have emerged to show elegant data collection capability in loosely cooperative network. However, in the sense of coverage quality, marginal works have considered the efficient (less participants) and effective (more coverage) designs for mobile crowdsensing network. We investigate the optimal coverage problem in distributed crowdsensing networks. In that, the sensing quality and the information delivery are jointly considered. Different from the conventional coverage problem, ours only select a subset of mobile users, so as to maximize the crowdsensing coverage with limited budget. We formulate our concerns as an optimal crowdsensing coverage problem, and prove its NP-completeness. In tackling this difficulty, we also prove the submodular property in our problem. Leveraging the favorable property in submodular optimization, we present the greedy algorithm with approxima√ tion ratio O( √k), where k is the number of selected users. Such that the information delivery and sensing coverage ratio could be guaranteed. Finally, we make extensive evaluations for the proposed scheme, with trace-driven tests. Evaluation results show that the proposed scheme could outperform the random selection by 2× with a random walk model, and over 3× with real trace data, in terms of crowdsensing coverage. Besides, the proposed scheme achieves near optimal solution comparing with the bruteforce search results.

SoundWrite: Text Input on Surfaces through Mobile Acoustic Sensing

Interacting with explosively growing mobile devices is becoming imperative. This paper presents SoundWrite, a mobile acoustic sensing system that enables text input into commercial off-the-shelf devices without any accessories. SoundWrite leverages the embedded microphone to capture subtle audio signals emitted from writing text on common found surfaces (eg., a wood table). It then extracts distinguishable features from both time and frequency information of received signals to recognize the text. We prototype SoundWrite on Smartphones as an Android application, and perform in-depth evaluation. The evaluation results validate the effectiveness and robustness of SoundWrite, and demonstrate that it could achieve an average recognition accuracy of above 90%.

Demodulation-free protocol identification in heterogeneous wireless networks

Nowadays various wireless network protocols play respective roles to fulfill different demands. To better adapt to this heterogeneity and coexistence situation, it is critical for nodes to identify the available networks with high accuracy and low cost. Unlike traditional demodulation-based identification method, which is expensive and complexing, in this paper, we propose a novel conception called demodulation-free protocol identification. This method only employs the features of physical layer samples. We first extract features that can be used to identify different protocols. Specifically, a sparse sequence based Precision-Stable Folding Algorithm (PSFA) is proposed to detect periodicity feature, which is common in wireless network protocols. Then we construct a prototype with USRP to identify three commonly used protocols in the 2.4GHz ISM band. Experiment results show that under low or moderate channel utilization, the accuracy is above 90%. We also show that the computational complexity is polynomial.

You Can Act Locally With Efficiency: Influential User Identification in Mobile Social Networks

As mobile social networks grow rapidly, influential user identification has attracted much more attention. Previous studies either need large message overhead to achieve global maxima in influence computation or focus on relatively stable network topology. To tackle the dynamic topology, we present an influential user identification scheme that fully exploits the active mobile users, in which the stable-state property could be leveraged under information potential construction scheme. We also propose an efficient routing algorithm for reaching the global maxima without depending on specific routing protocols. The proposed scheme is validated with extensive simulations using both synthetic random-walk and real-world mobility traces. The results demonstrate that it achieves considerable performance on influential user identification and route construction with little overhead. Furthermore, we present a case of mobile data offloading, and the results show that our scheme could reduce the efficient data traffic by up to 79.2%, compared with a baseline without data offloading.

DCNC: throughput maximization via delay controlled network coding for wireless mesh networks

Network coding NC can greatly improve the performance of wireless mesh networks WMNs in terms of throughput and reliability, and so on. However, NC generally performs a batch-based transmission scheme, the main drawback of this scheme is the inevitable increase in average packet delay, that is, a large batch size may achieve higher throughput but also induce larger average packet delay. In this work, we put our focus on the tradeoff between the average throughput and packet delay; in particular, our ultimate goal is to maximize the throughput for real-time traffic under the premise of diversified and time-varying delay requirements. To tackle this problem, we propose DCNC, a delay controlled network coding protocol, which can improve the throughput for real-time traffic by dynamically controlling the delay in WMNs. To define an appropriate control foundation, we first build up a delay prediction model to capture the relationship between the average packet delay and the encoding batch size. Then, we design a novel freedom-based feedback scheme to efficiently reflect the reception of receivers in a reliable way. Based on the predicted delay and current reception status, DCNC utilizes the continuous encoding batch size adjustment to control delay and further improve the throughput. Extensive simulations show that, when faced with the diversified and time-varying delay requirements, DCNC can constantly fulfill the delay requirements, for example, achieving over 95% efficient packet delivery ratio EPDR in all instances under good channel quality, and also obtains higher throughput than the state-of-art protocol. Copyright

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.