Luoyang Fang

Exploiting Mobile Big Data: Sources, Features, and Applications

The worldwide rollout of 4G LTE mobile communication networks has accelerated the proliferation of the mobile Internet and spurred a new wave of mobile applications on smartphones. This new wave has provided mobile operators an enormous opportunity to collect a huge amount of data to monitor the technical and transactional aspects of their networks. Recent research on mobile big data mining have shown its great potential for diverse purposes ranging from improving traffic management, enabling personal and contextual services, to monitoring city dynamics and so on. The mobile big data research has a multi-disciplinary nature that demands distinct knowledge from mobile communications, signal processing, and data mining. The research field of mobile big data has emerged quickly in recent years, but is somewhat fragmented. This article aims to provide an integrated picture of this emerging field to bridge multiple disciplines and hopefully to inspire future research.

Mobile Big Data: The Fuel for Data-Driven Wireless

In the past decade, the smart phone evolution has accelerated the proliferation of the mobile Internet and spurred a new wave of mobile applications, leading to an unprecedented mobile data volume generated from the mobile devices, content servers, and network operators, which are mainly nonstructured. In this big data era, such nonstructured data fragments are pieced together such that, drastically differing from the traditional practice where services determine and define the data, data is becoming a proactive entity that may drive and even create new services. Compared with the so-termed 5V characteristics of generic big data, namely volume, variety, velocity, veracity, and value, mobile big data is distinct in its unique multidimensional, personalized, multisensory, and real-time features. In this survey, we provide in-depth and comprehensive coverage on the features, sources and applications of mobile big data, as well as the current state-of-the-art, challenges and opportunities for research and development in this field, with an emphasis on the user modeling, infrastructure supporting, data management, and knowledge discovery aspects.

A new DFT-based frequency estimator for single-tone complex sinusoidal signals

Frequency estimation for single-tone complex sinusoidal signals under additive white Gaussian noise is a classical and fundamental problem in many applications, such as communications, radar, sonar and power systems. In this paper, we propose a new algorithm by interpolating discrete Fourier transform (DFT) samples. Different from other existing interpolation methods for frequency estimation, our algorithm is based on a much simpler expression and has mathematically tractable bias expression in closed form, which can potentially assist future bias correction. Simulations confirm that our proposed algorithm outperforms all existing alternatives in the literature with comparable complexity.

Named Data Networking over WDM-Based Optical Networks

Named data networking (NDN) is a typical implementation of information-centric networking (ICN), which has been widely investigated in recent years for its potential as the next-generation network architecture. Optical networks are important as the demand for Internet bandwidth is enormous and they can be used to attain extremely high bandwidth. In this article, we study optical named data networking (ONDN), which is a complete solution for NDN deployment over optical networks based on wavelength-division multiplexing (WDM). To the best of our knowledge, this is the first attempt to deploy NDN over optical networks. We first introduce the basic network architecture of ONDN, based on which we discuss the intuitive packet routing and forwarding approach in ONDN, and propose an innovative alternative to improve network performance. In our proposed approach, a novel packet type, called a response packet, is proposed to reserve wavelength along the routing path for data transmission. Packet aggregation in ONDN is discussed to improve the efficiency of wavelength utilization. In addition, the optical node structure of ONDN is developed and discussed to support all functionalities proposed in this article.

Case Study: User Identification for Mobile Privacy

To facilitate the novel mobile data-driven applications and services as discussed in previous chapters, mobile big data with spatiotemporal information may need to be released to third parties or even to the public. However, direct data publishing may lead to a significant subscriber’s privacy leakage risk (Cheng et al. IEEE Netw 31(1):72–79, 2017), immediately resulting in data availability issues. To protect subscribers’ privacy, the common practice is to anonymize the dataset by replacing subscribers’ identifiers (e.g., name, social security number, etc.) with randomly generated strings.

Case Study: Demand Forecasting for Predictive Network Managements

The mobile big data collected by mobile network operators can also benefit the management of mobile networks as stated previously.

Source and Collection

Mobile data can be collected from various sources in the mobile network. These data are usually divided into two categories.

Location Privacy in Mobile Big Data: User Identifiability via Habitat Region Representation

Mobile big data collected by mobile network operators is of interest to many research communities and industries for its remarkable values. However, such spatiotemporal information may lead to a harsh threat to subscribers’ privacy. This work focuses on subscriber privacy vulnerability assessment in terms of user identifiability across two datasets with significant detail reduced mobility representation. In this paper, we propose an innovative semantic spatiotemporal representation for each subscriber based on the geographic information, termed as daily habitat region, to approximate the subscriber’s daily mobility coverage with far lesser information compared with original mobility traces. The daily habitat region is realized via convex hull extraction on the user’s daily spatiotemporal traces. As a result, user identification can be formulated to match two records with the maximum similarity score between two convex hull sets, obtained by our proposed similarity measures based on cosine distance and permutation hypothesis test. Experiments are conducted to evaluate our proposed innovative mobility representation and user identification algorithms, which also demonstrate that the subscriber’s mobile privacy is under a severe threat even with significantly reduced spatiotemporal information.

Cross-object coding and allocation (COCA) for distributed storage systems

Distributed storage systems (DSSs) are widely employed in data centers and sensing networks to resist storage node failures. Structured redundancy is introduced to DSS by various coding schemes to efficiently account for failures of storage nodes. The allocation of the coded data blocks to storage nodes is another factor that impacts the data reliability. In this paper, we investigate the coding and allocation problem on multiple data objects in DSS. We propose a cross-object coding and allocation (COCA), which amounts to encoding and symmetric allocation on one large virtual data object aggregated by multiple data objects. We first explore the benefits of the proposed COCA scheme and find its reliability improvement in terms of joint successful recovery probability. However, such reliability improvement comes at the cost of increased data retrieval complexity. Hence, an optimization problem is formulated to explore the tradeoff between data reliability and data retrieval complexity. By employing a coalition formation game to model the process of the data objects grouping, we also propose a coalition-formation-based grouping algorithm to provide a suboptimal solution with greatly reduced computation complexity. Simulations validate the reliability improvement of our proposed COCA scheme and the effectiveness of our proposed coalition-formation-based algorithm.

Cooperative Content Download-and-Share: Motivating D2D in Cellular Networks

Device-to-device (D2D) communications is regarded as a promising technology for future cellular networks, which can significantly improve both the network spectral efficiency and individual user experience. However, the lack of motivation to utilize the D2D links hinders the deployment of D2D communications in cellular networks. We study the cooperative downloading problem via D2D communications, and propose a scheme, termed as cooperative content download-and-share (CoCoDaS), to stimulate the demand of D2D communications by a simple pricing model in cellular networks, in order to offload traffic burden from base station. In CoCoDaS, the users with the same content demand cooperatively download a large-sized file from the base station (BS) and share their downloaded segments of such content with each other via D2D communications. Simulations verify the efficiency of our proposed CoCoDaS scheme in terms of the reduction of the BS’s burden on data services and the utilization of D2D communications.