Maotian Zhang

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.

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%.

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.

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.

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%.

Poster: Sonicnect: Accurate Hands-Free Gesture Input System with Smart Acoustic Sensing

This work presents Sonicnect, an acoustic sensing system with smartphone that enables accurate hands-free gesture input. Sonicnect leverages the embedded microphone in the smartphone to capture the subtle audio signals generated with fingers touching on the table. It supports 9 commonly used gestures (click, flip, scroll and zoom, etc) with above 92% recognition accuracy, and the minimum gesture movement could be 2cm. Distinguishable features are then extracted by exploiting spatio-temporal and frequency properties of the subtle audio signals. We conduct extensive real environment experiments to evaluate its performance. The results validate the effectiveness and robustness of Sonicnect.