The flux of social media and the convenience of mobile connectivity have created a mobile data phenomenon that is expected to overwhelm mobile cellular networks in the foreseeable future. Despite the ...advent of 4G/LTE, the growth rate of wireless data has far exceeded the capacity increase of mobile networks. A fundamentally new design paradigm is required to tackle the ever growing wireless data challenge. In this article, we investigate the problem of massive content delivery over wireless networks, and present a systematic view of content-centric network design and its underlying challenges. Toward this end, we first review some of the recent advancements in information-centric networking, which provide the basis of how media contents can be labeled, distributed, and placed across the networks. We then formulate the content delivery task into a content rate maximization problem over a shared wireless channel, which, in contrast to the conventional wisdom that attempts to increase the bit rate of a unicast system, maximizes the content delivery capability with a fixed amount of wireless resources. This conceptually simple change enables us to exploit the content diversity and network diversity by leveraging the abundant computation sources (through application-layer encoding, pushing and caching, etc.) within the existing wireless networks. A network architecture that enables wireless network crowdsourcing for content delivery is then described, followed by an exemplary campus wireless network that encompasses the above concepts.
The recent paradigm of mobile crowd sensing (MCS) enables a broad range of mobile applications. A critical challenge for the paradigm is to incentivize phone users to be workers providing sensing ...services. While some theoretical incentive mechanisms for general-purpose crowdsourcing have been proposed, it is still an open issue as to how to incorporate the theoretical framework into the practical MCS system. In this paper, we propose an incentive mechanism based on a quality-driven auction (QDA). The mechanism is specifically for the MCS system, where the worker is paid off based on the quality of sensed data instead of working time, as adopted in the literature. We theoretically prove that the mechanism is truthful, individual rational, platform profitable, and social-welfare optimal. Moreover, we incorporate our incentive mechanism into a Wi-Fi fingerprint-based indoor localization system to incentivize the MCS-based fingerprint collection. We present a probabilistic model to evaluate the reliability of the submitted data, which resolves the issue that the ground truth for the data reliability is unavailable. We realize and deploy an indoor localization system to evaluate our proposed incentive mechanism and present extensive experimental results.
Device-to-device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D-enabled cellular networks, interference generated by D2D ...communications is usually viewed as an obstacle to cellular communications. However, in this paper, we present a new perspective on the role of D2D interference by taking security issues into consideration. We consider a large-scale D2D-enabled cellular network with eavesdroppers overhearing cellular communications. Using stochastic geometry, we model such a network and analyze the signal-to-interference-plus-noise ratio (SINR) distributions, connection probabilities and secrecy probabilities of both the cellular and D2D links. We propose two criteria for guaranteeing performances of secure cellular communications, namely the strong and weak performance guarantee criteria. Based on the obtained analytical results of link characteristics, we design optimal D2D link scheduling schemes under these two criteria respectively. Both analytical and numerical results show that the interference from D2D communications can enhance physical layer security of cellular communications and at the same time create extra transmission opportunities for D2D users.
In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the ...traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using `1 minimization decoding algorithm when it takes m = O(k log(n=k)) independent random walks with the length of each walk t = O(n=k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs.
Crowd sensing systems enable a wide range of data collection, where the data are usually tagged with private locations. How to incentivize users to participate in such systems while preserving ...location-privacy is coming up as a critical issue. To this end, we consider location-privacy protection when motivating users to sense data instead of viewing them separately. Without loss of generality, k-anonymity is utilized to reduce the risk of location-privacy disclosure. Specifically, we propose a location aggregation method to cluster users into groups for k-anonymity preserving, and meanwhile mitigating the incurred information loss. After that, an incentive mechanism is carefully designed to select efficient users and calculate rational compensations based on clustered groups obtained in location aggregation, where the influences of both the information loss and k-anonymity in location-privacy preserving are captured into group values and sensing costs. Through theoretical analysis and extensive performances evaluated on real and synthetic data, we find out that the incentive payment increases sharply with more stringent privacy protection and the information loss can be further mitigated compared with conventional methods.
Indoor localization has been an active research field for decades, where the received signal strength (RSS) fingerprinting based methodology is widely adopted and induces many important localization ...techniques such as the recently proposed one building the fingerprint database with crowd-sourcing. While efforts have been dedicated to improve the accuracy and efficiency of localization, the fundamental limits of RSS fingerprinting based methodology itself is still unknown in a theoretical perspective. In this paper, we present a general probabilistic model to shed light on a fundamental question: how good the RSS fingerprinting based indoor localization can achieve? Concretely, we present the probability that a user can be localized in a region with certain size, given the RSS fingerprints submitted to the system. We reveal the interaction among the localization accuracy, the reliability of location estimation and the number of measurements in the RSS fingerprinting based location determination. Moreover, we present the optimal fingerprints reporting strategy that can achieve the best accuracy for given reliability and the number of measurements, which provides a design guideline for the RSS fingerprinting based indoor localization facilitated by crowdsourcing paradigm.
Al-doped ZnO has attracted much attention as a transparent electrode. The graphene-like ZnO monolayer as a two-dimensional nanostructure material shows exceptional properties compared to bulk ZnO. ...Here, through first-principle calculations, we found that the transparency in the visible light region of Al-doped ZnO monolayer is significantly enhanced compared to the bulk counterpart. In particular, the 12.5 at% Al-doped ZnO monolayer exhibits the highest visible transmittance of above 99%. Further, the electrical conductivity of the ZnO monolayer is enhanced as a result of Al doping, which also occurred in the bulk system. Our results suggest that Al-doped ZnO monolayer is a promising transparent conducting electrode for nanoscale optoelectronic device applications.
The Scanning the Literature column provides concise summaries of selected papers that are recently published in the field of networking. Each summary describes the paper's main idea, methodology, and ...technical contributions. The purpose of the column is to bring the state-of-art of networking research to readers of IEEE Network magazine. Authors are also welcome to recommend their recently published work to the column, and papers with novel ideas, solid work, and significant contributions to the field are especially appreciated. Authors wishing to have their papers presented in the column should contact Xiaohua Tian at the email address below.
Scanning the Literature Tian, Xiaohua
IEEE network,
2024-March, 2024-3-00, Letnik:
38, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Summary form only. The Scanning the Literature column provides concise summaries of selected papers that are recently published in the field of networking. Each summary describes the paper's main ...idea, methodology, and technical contributions. The purpose of the column is to bring the state-of-art of networking research to readers of IEEE Network magazine. Authors are also welcome to recommend their recently published work to the column, and papers with novel ideas, solid work, and significant contributions to the field are especially appreciated. Authors wishing to have their papers presented in the column should contact Xiaohua Tian at the email address below.
Crowdsourcing systems allocate tasks to a group of workers over the Internet, which have become an effective paradigm for human-powered problem solving, such as image classification, optical ...character recognition, and proofreading. In this paper, we focus on incentivizing crowd workers to label a set of multi-class labeling tasks under strict budget constraint. We properly profile the tasks' difficulty levels and workers' quality in crowdsourcing systems, where the collected labels are aggregated with sequential Bayesian approach. To stimulate workers to undertake crowd labeling tasks, the interaction between workers and the platform is modeled as a reverse auction. We reveal that the platform utility maximization could be intractable, for which an incentive mechanism that determines the winning bid and payments with polynomial-time computation complexity is developed. Moreover, we theoretically prove that our mechanism is truthful, individually rational, and budget feasible. Through extensive simulations, we demonstrate that our mechanism utilizes budget efficiently to achieve high platform utility with polynomial computation complexity.
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