Enabling UAV Cellular with Millimeter-Wave Communication: Potentials and Approaches

To support high data rate urgent or ad hoc communications, we consider mmWave UAV cellular networks and the associated challenges and solutions. To enable fast beamforming training and tracking, we first investigate a hierarchical structure of beamforming codebooks and design of hierarchical codebooks with different beam widths via the sub-array techniques. We next examine the Doppler effect as a result of UAV movement and find that the Doppler effect may not be catastrophic when high gain directional transmission is used. We further explore the use of millimeter wave spatial division multiple access and demonstrate its clear advantage in improving the cellular network capacity. We also explore different ways of dealing with signal blockage and point out that possible adaptive UAV cruising algorithms would be necessary to counteract signal blockage. Finally, we identify a close relationship between UAV positioning and directional millimeter wave user discovery, where update of the former may directly impact the latter and vice versa.Comment: This paper explores the potentials and approaches to exploit mmWave communication to establish a UAV cellular. It is to appear in IEEE Communications Magazin

Spatio-Temporal Data Mining: A Survey of Problems and Methods

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differs from relational data for which computational approaches are developed in the data mining community for multiple decades, in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data mining community. In this article we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data mining problems in each of these categories.Comment: Accepted for publication at ACM Computing Survey

Millimeter Wave Channel Measurements and Implications for PHY Layer Design

There has been an increasing interest in the millimeter wave (mmW) frequency regime in the design of next-generation wireless systems. The focus of this work is on understanding mmW channel properties that have an important bearing on the feasibility of mmW systems in practice and have a significant impact on physical (PHY) layer design. In this direction, simultaneous channel sounding measurements at 2.9, 29 and 61 GHz are performed at a number of transmit-receive location pairs in indoor office, shopping mall and outdoor environments. Based on these measurements, this paper first studies large-scale properties such as path loss and delay spread across different carrier frequencies in these scenarios. Towards the goal of understanding the feasibility of outdoor-to-indoor coverage, material measurements corresponding to mmW reflection and penetration are studied and significant notches in signal reception spread over a few GHz are reported. Finally, implications of these measurements on system design are discussed and multiple solutions are proposed to overcome these impairments.Comment: 13 pages, 8 figures, Accepted for publication at the IEEE Transactions on Antennas and Propagatio

Modeling, Analysis and Optimization of Multicast Device-to-Device Transmission

Multicast device-to-device (D2D) transmission is important for applications like local file transfer in commercial networks and is also a required feature in public safety networks. In this paper we propose a tractable baseline multicast D2D model, and use it to analyze important multicast metrics like the coverage probability, mean number of covered receivers and throughput. In addition, we examine how the multicast performance would be affected by certain factors like mobility and network assistance. Take the mean number of covered receivers as an example. We find that simple repetitive transmissions help but the gain quickly diminishes as the repetition time increases. Meanwhile, mobility and network assistance (i.e. allowing the network to relay the multicast signals) can help cover more receivers. We also explore how to optimize multicasting, e.g. by choosing the optimal multicast rate and the optimal number of retransmission times.Comment: 14 pages; 8 figures; submitted to IEEE Transactions on Wireless Communication

Asymptotic Scaling Laws of Wireless Adhoc Network with Physical Layer Caching

We propose a physical layer (PHY) caching scheme for wireless adhoc networks. The PHY caching exploits cache-assisted multihop gain and cache-induced dual-layer CoMP gain, which substantially improves the throughput of wireless adhoc networks. In particular, the PHY caching scheme contains a novel PHY transmission mode called the cache-induced dual-layer CoMP which can support homogeneous opportunistic CoMP in the wireless adhoc network. Compared with traditional per-node throughput scaling results of \Theta\left(1/\sqrt{N}\right), we can achieve O(1) per node throughput for a cached wireless adhoc network with N nodes. Moreover, we analyze the throughput of the PHY caching scheme for regular wireless adhoc networks and study the impact of various system parameters on the PHY caching gain.Comment: 15 pages, 12 figures, accepted by IEEE Transactions on Wireless Communication

Cognitive Internet of Things: A New Paradigm beyond Connection

Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named Cognitive Internet of Things (CIoT), to empower the current IoT with a `brain' for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning

A Taxonomy of Peer-to-Peer Based Complex Queries: a Grid perspective

Grid superscheduling requires support for efficient and scalable discovery of resources. Resource discovery activities involve searching for the appropriate resource types that match the user's job requirements. To accomplish this goal, a resource discovery system that supports the desired look-up operation is mandatory. Various kinds of solutions to this problem have been suggested, including the centralised and hierarchical information server approach. However, both of these approaches have serious limitations in regards to scalability, fault-tolerance and network congestion. To overcome these limitations, organising resource information using Peer-to-Peer (P2P) network model has been proposed. Existing approaches advocate an extension to structured P2P protocols, to support the Grid resource information system (GRIS). In this paper, we identify issues related to the design of such an efficient, scalable, fault-tolerant, consistent and practical GRIS system using a P2P network model. We compile these issues into various taxonomies in sections III and IV. Further, we look into existing works that apply P2P based network protocols to GRIS. We think that this taxonomy and its mapping to relevant systems would be useful for academic and industry based researchers who are engaged in the design of scalable Grid systems

A Study on Application of Spatial Data Mining Techniques for Rural Progress

This paper focuses on the application of Spatial Data mining Techniques to efficiently manage the challenges faced by peripheral rural areas in analyzing and predicting market scenario and better manage their economy. Spatial data mining is the task of unfolding the implicit knowledge hidden in the spatial databases. The spatial Databases contain both spatial and non-spatial attributes of the areas under study. Finding implicit regularities, rules or patterns hidden in spatial databases is an important task, e.g. for geo-marketing, traffic control or environmental studies. In this paper the focus is on the effective use of Spatial Data Mining Techniques in the field of Economic Geography constrained to the rural areasComment: International Conference on Innovative Computing, information and communication technology ICICT09; souvenir pp no 6

Intrinsic Point of Interest Discovery from Trajectory Data

This paper presents a framework for intrinsic point of interest discovery from trajectory databases. Intrinsic points of interest are regions of a geospatial area innately defined by the spatial and temporal aspects of trajectory data, and can be of varying size, shape, and resolution. Any trajectory database exhibits such points of interest, and hence are intrinsic, as compared to most other point of interest definitions which are said to be extrinsic, as they require trajectory metadata, external knowledge about the region the trajectories are observed, or other application-specific information. Spatial and temporal aspects are qualities of any trajectory database, making the framework applicable to data from any domain and of any resolution. The framework is developed under recent developments on the consistency of nonparametric hierarchical density estimators and enables the possibility of formal statistical inference and evaluation over such intrinsic points of interest. Comparisons of the POIs uncovered by the framework in synthetic truth data to thousands of parameter settings for common POI discovery methods show a marked improvement in fidelity without the need to tune any parameters by hand.Comment: 10 pages, 9 figure

Deep Representation Learning for Social Network Analysis

Social network analysis is an important problem in data mining. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e., network embeddings, so that the network topology structure and other attribute information can be effectively preserved. Network representation leaning facilitates further applications such as classification, link prediction, anomaly detection and clustering. In addition, techniques based on deep neural networks have attracted great interests over the past a few years. In this survey, we conduct a comprehensive review of current literature in network representation learning utilizing neural network models. First, we introduce the basic models for learning node representations in homogeneous networks. Meanwhile, we will also introduce some extensions of the base models in tackling more complex scenarios, such as analyzing attributed networks, heterogeneous networks and dynamic networks. Then, we introduce the techniques for embedding subgraphs. After that, we present the applications of network representation learning. At the end, we discuss some promising research directions for future work