Showing papers in "IEEE Network in 2014"

5G wireless backhaul networks: challenges and research advances

Abstract: 5G networks are expected to achieve gigabit-level throughput in future cellular networks. However, it is a great challenge to treat 5G wireless backhaul traffic in an effective way. In this article, we analyze the wireless backhaul traffic in two typical network architectures adopting small cell and millimeter wave communication technologies. Furthermore, the energy efficiency of wireless backhaul networks is compared for different network architectures and frequency bands. Numerical comparison results provide some guidelines for deploying future 5G wireless backhaul networks in economical and highly energy-efficient ways.

NFV: state of the art, challenges, and implementation in next generation mobile networks (vEPC)

Abstract: As mobile network users look forward to the connectivity speeds of 5G networks, service providers are facing challenges in complying with connectivity demands without substantial financial investments. Network function virtualization (NFV) is introduced as a new methodology that offers a way out of this bottleneck. NFV is poised to change the core structure of telecommunications infrastructure to be more cost-efficient. In this article, we introduce an NFV framework, and discuss the challenges and requirements of its use in mobile networks. In particular, an NFV framework in the virtual environment is proposed. Moreover, in order to reduce signaling traffic and achieve better performance, this article proposes a criterion to bundle multiple functions of a virtualized evolved packet core in a single physical device or a group of adjacent devices. The analysis shows that the proposed grouping can reduce the network control traffic by 70 percent.

Challenges in 5G: how to empower SON with big data for enabling 5G

Abstract: While an al dente character of 5G is yet to emerge, network densification, miscellany of node types, split of control and data plane, network virtualization, heavy and localized cache, infrastructure sharing, concurrent operation at multiple frequency bands, simultaneous use of different medium access control and physical layers, and flexible spectrum allocations can be envisioned as some of the potential ingredients of 5G. It is not difficult to prognosticate that with such a conglomeration of technologies, the complexity of operation and OPEX can become the biggest challenge in 5G. To cope with similar challenges in the context of 3G and 4G networks, recently, self-organizing networks, or SONs, have been researched extensively. However, the ambitious quality of experience requirements and emerging multifarious vision of 5G, and the associated scale of complexity and cost, demand a significantly different, if not totally new, approach toward SONs in order to make 5G technically as well as financially feasible. In this article we first identify what challenges hinder the current self-optimizing networking paradigm from meeting the requirements of 5G. We then propose a comprehensive framework for empowering SONs with big data to address the requirements of 5G. Under this framework we first characterize big data in the context of future mobile networks, identifying its sources and future utilities. We then explicate the specific machine learning and data analytics tools that can be exploited to transform big data into the right data that provides a readily useable knowledge base to create end-to-end intelligence of the network. We then explain how a SON engine can build on the dynamic models extractable from the right data. The resultant dynamicity of a big data empowered SON (BSON) makes it more agile and can essentially transform the SON from being a reactive to proactive paradigm and hence act as a key enabler for 5G's extremely low latency requirements. Finally, we demonstrate the key concepts of our proposed BSON framework through a case study of a problem that the classic 3G/4G SON fails to solve.

Visible light communications for 5G wireless networking systems: from fixed to mobile communications

Abstract: Visible light communication, considered as a potential access option for 5G wireless communications, is gaining extensive attention VLC has strengths in energy efficiency and ultra wide bandwidth, but also has weakness in transmission range and obstacles in transmission paths This article aims to provide a conclusive investigation of the latest progress in research on VLC, which can be used as part of 5G wireless communication systems This work highlights the strengths and weaknesses of VLC in comparison with RF-based communications, especially in spectrum, spatial reuse, security and energy efficiency The article also investigates various lighting sources proposed for VLC systems It summarizes the literature work on VLC networking into two categories: fixed and mobile VLC communications

Toward efficient and privacy-preserving computing in big data era

Abstract: Big data, because it can mine new knowledge for economic growth and technical innovation, has recently received considerable attention, and many research efforts have been directed to big data processing due to its high volume, velocity, and variety (referred to as "3V") challenges. However, in addition to the 3V challenges, the flourishing of big data also hinges on fully understanding and managing newly arising security and privacy challenges. If data are not authentic, new mined knowledge will be unconvincing; while if privacy is not well addressed, people may be reluctant to share their data. Because security has been investigated as a new dimension, "veracity," in big data, in this article, we aim to exploit new challenges of big data in terms of privacy, and devote our attention toward efficient and privacy-preserving computing in the big data era. Specifically, we first formalize the general architecture of big data analytics, identify the corresponding privacy requirements, and introduce an efficient and privacy-preserving cosine similarity computing protocol as an example in response to data mining's efficiency and privacy requirements in the big data era.

Smart grid neighborhood area networks: a survey

Abstract: Smart grid is an intelligent power network featured by its two-way flows of electricity and information. With an integrated communication infrastructure, smart grid manages the operation of all connected components to provide reliable and sustainable electricity supplies. Many advanced communication technologies have been identified for their applications in different domains of smart grid networks. This paper focuses on wireless communication networking technologies for smart grid neighborhood area networks (NANs). In particular, we aim to offer a comprehensive survey to address various important issues on implementation of smart grid NANs, including network topology, gateway deployment, routing algorithms, and security. We will identify four major challenges for the implementation of NANs, including timeliness management, security assurance, compatibility design, and cognitive spectrum access, based on which the future research directions are suggested.

CAP: community activity prediction based on big data analysis

Abstract: Crowd sensing harnesses the power of the crowd by mobilizing a large number of users carrying various mobile and networked devices to collect data with the intrinsic multi-modal and large-volume features. With traditional methods, it is highly challenging to analyze the vast data volume generated by crowd sensing. In the era of big data, although several individual-oriented approaches are proposed to analyze human behavior based on big data, the common features of individual activity have not been fully investigated. In this article, we design a novel community- centric framework for community activity prediction based on big data analysis. Specifically, we propose an approach to extract community activity patterns by analyzing the big data collected from both the physical world and virtual social space. The proposed approach consists of community detection based on singular value decomposition and clustering, and community activity modeling based on tensors. The proposed approach is evaluated with a case study where a real dataset collected over a 15-month period is analyzed.

Building a network highway for big data: architecture and challenges

Abstract: Big data, with their promise to discover valuable insights for better decision making, have recently attracted significant interest from both academia and industry. Voluminous data are generated from a variety of users and devices, and are to be stored and processed in powerful data centers. As such, there is a strong demand for building an unimpeded network infrastructure to gather geologically distributed and rapidly generated data, and move them to data centers for effective knowledge discovery. The express network should also be seamlessly extended to interconnect multiple data centers as well as interconnect the server nodes within a data center. In this article, we take a close look at the unique challenges in building such a network infrastructure for big data. Our study covers each and every segment in this network highway: the access networks that connect data sources, the Internet backbone that bridges them to remote data centers, as well as the dedicated network among data centers and within a data center. We also present two case studies of real-world big data applications that are empowered by networking, highlighting interesting and promising future research directions.

Harnessing renewable energy in cloud datacenters: opportunities and challenges

Abstract: The proliferation of cloud computing has promoted the wide deployment of largescale datacenters with tremendous power consumption and high carbon emission. To reduce power cost and carbon footprint, an increasing number of cloud service providers have considered green datacenters with renewable energy sources, such as solar or wind. However, unlike the stable supply of grid energy, it is challenging to utilize and realize renewable energy due to the uncertain, intermittent and variable nature. In this article, we provide a taxonomy of the state-of-the-art research in applying renewable energy in cloud computing datacenters from five key aspects, including generation models and prediction methods of renewable energy, capacity planning of green datacenters, intra-datacenter workload scheduling and load balancing across geographically distributed datacenters. By exploring new research challenges involved in managing the use of renewable energy in datacenters, this article attempts to address why, when, where and how to leverage renewable energy in datacenters, also with a focus on future research avenues.

Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop

Abstract: Network traffic monitoring and analysis is of theoretical and practical significance for optimizing network resource and improving user experience. However, existing solutions, which usually rely on a high-performance server with large storage capacity, are not scalable for detailed analysis of a large volume of traffic data. In this article, we present a traffic monitoring and analysis system for large-scale networks based on Hadoop, an open-source distributed computing platform for big data processing on commodity hardware. This system has been deployed in the core network of a large cellular network and extensively evaluated. The results demonstrate that the system can efficiently processes 4.2 Tbytes of traffic data from 123 Gb/s links with high performance and low cost every day.

Securing building management systems using named data networking

Abstract: Recently, building automation and management systems, BASs and BMSs, have shift- ed from using proprietary protocols and specialized hardware toward widespread adoption of IP-based open standard technologies. While the IP protocol suite improves software and hardware interoperability, practical large-scale BMS deploy- ments face challenges, including the complexity of network addressing and other con- figuration, reliance on middleware for even relatively simple tasks, and a lack of security. In this article, we propose a data-centric BMS design that uses named data networking, one of the proposed information-centric networking architecture designs. Our sensor data acquisition system uses a hierarchical namespace for data, encryp- tion keys, and access control lists, implements encryption-based access control, and provides a web browser-based data visualization interface that communicates in NDN. Our design has been deployed on a UCLA campus testbed that captures, archives, and visualizes data from industry standard sensors.

Networking solutions for connecting bluetooth low energy enabled machines to the internet of things

Abstract: The next wave driving the expansion of the Internet will come from the Internet of Things. Bluetooth LE is a rapidly emerging ultra-low-power radio technology expected to be incorporated in billions of IoT devices in the next few years. Consequently, it is particularly important to specify Internet connectivity solutions for Bluetooth LE. In this article we present such solutions based on the ongoing standardization work in the IETF and Bluetooth Special Interest Group. We prove the feasibility of a complete IP-based protocol stack on constrained devices and illustrate its performance, highlighting key trade-offs. In addition, we discuss gateway operation covering global IPv6 connectivity and proxy-cache functionality.

Heterogeneous statistical QoS provisioning over 5G mobile wireless networks

Abstract: As a critical step towards the next new era of mobile wireless networks, recently 5G mobile wireless networks have received significant research attention and efforts from both academia and industry. The 5G mobile wireless networks are expected to provide different delay-bounded QoS guarantees for a wide spectrum of services, applications, and users with extremely diverse requirements. Since the time-sensitive services in 5G multimedia wireless networks may vary dramatically in both a large range from milliseconds to a few seconds and diversity from uniform/ constant delay-bound to different/variable delay-bound guarantees among different wireless links, the delay-bound QoS requirements for different types of services promote the newly emerging heterogeneous statistical delay-bounded QoS provisioning over 5G mobile wireless networks, which, however, imposes many new challenging issues not encountered before in 4G wireless networks. To overcome these new challenges, in this article we propose a novel heterogeneous statistical QoS provisioning architecture for 5G mobile wireless networks. First, we develop and analyze the new heterogeneous statistical QoS system model by applying and extending the effective capacity theory. Then, through the wireless coupling channels, we apply our proposed heterogeneous statistical QoS architecture to efficiently implement the following powerful 5G-candidate wireless techniques: 1) device-to-device networks; 2) full-duplex networks; and 3) cognitive radio networks, respectively, for providing heterogeneous statistical delay-bounded QoS guarantees. Finally, using the simulation experiments we show that our proposed architecture and schemes significantly outperform the existing traditional statistical delay-bounded QoS provisioning schemes in terms of satisfying the heterogeneous delay-bounded QoS requirements while maximizing the aggregate system throughput over 5G mobile wireless networks.

Information-centric networking for machine-to-machine data delivery: A case study in smart grid applications

Abstract: Largely motivated by the proliferation of content-centric applications in the Internet, information-centric networking has attracted the attention of the research community. By tailoring network operations around named information objects instead of end hosts, ICN yields a series of desirable features such as the spatiotemporal decoupling of communicating entities and the support of in-network caching. In this article, we advocate the introduction of such ICN features in a new, rapidly transforming communication domain: the smart grid. With the rapid introduction of multiple new actors, such as distributed (renewable) energy resources and electric vehicles, smart grids present a new networking landscape where a diverse set of multi-party machine-to-machine applications are required to enhance the observability of the power grid, often in real time and on top of a diverse set of communication infrastructures. Presenting a generic architectural framework, we show how ICN can address the emerging smart grid communication challenges. Based on real power grid topologies from a power distribution network in the Netherlands, we further employ simulations to both demonstrate the feasibility of an ICN solution for the support of real-time smart grid applications and further quantify the performance benefits brought by ICN against the current host-centric paradigm. Specifically, we show how ICN can support real-time state estimation in the medium voltage power grid, where high volumes of synchrophasor measurement data from distributed vantage points must be delivered within a very stringent end-to-end delay constraint, while swiftly overcoming potential power grid component failures.

OSNT: open source network tester

Abstract: Despite network monitoring and testing being critical for computer networks, current solutions are both extremely expensive and inflexible. Into this lacuna we launch the Open Source Network Tester, a fully open source traffic generator and capture system. Our prototype implementation on the NetFPGA-10G supports 4 × 10 Gb/s traffic generation across all packet sizes, and traffic capture is supported up to 2 × 10Gb/s with naive host software. Our system implementation provides methods for scaling and coordinating multiple generator/capture systems, and supports 6.25 ns timestamp resolution with clock drift and phase coordination maintained by GPS input. Additionally, our approach has demonstrated lower-cost than comparable commercial systems while achieving comparable levels of precision and accuracy; all within an open-source framework extensible with new features to support new applications, while permitting validation and review of the implementation.

Cloud computing meets mobile wireless communications in next generation cellular networks

Abstract: In next generation cellular networks, cloud computing will have profound impacts on mobile wireless communications. On the one hand, the integration of cloud computing into the mobile environment enables MCC systems. On the other hand, the powerful computing platforms in the cloud for radio access networks lead to a novel concept of C-RAN. In this article we study the topology configuration and rate allocation problem in C-RAN with the objective of optimizing the end-to-end performance of MCC users in next generation cellular networks. We use a decision theoretical approach to tackle the delayed channel state information problem in C-RAN. Simulation results show that the design and operation of future mobile wireless networks can be significantly affected by cloud computing, and the proposed scheme is capable of achieving substantial performance gains over existing schemes.

BASA: building mobile Ad-Hoc social networks on top of android

Abstract: Mobile ad-hoc social networks (MASNs) are emerging as a self-configuring and self-organizing social networking paradigm, which enhance local interactions among mobile and handheld device users. However, the MASNs cannot be directly derived on demand for various Android systems from existing social networks (SNs) without having access to end-to-end IT network infrastructure. In this article, we propose a detailed solution called BASA which would help in rapidly building local mobile ad-hoc social networks on top of the current Android platform. BASA establishes a four-layer system architecture according to the underlying challenges and requirements in MASNs. BASA is implemented in an EU project named "SOCITIES". Our prototype shows that BASA is flexible to expedite various services.

Traffic identification engine: an open platform for traffic classification

Abstract: The availability of open source traffic classification systems designed for both experimental and operational use, can facilitate collaboration, convergence on standard definitions and procedures, and reliable evaluation of techniques. In this article, we describe Traffic Identification Engine (TIE), an open source tool for network traffic classification, which we started developing in 2008 to promote sharing common implementations and data in this field. We designed TIE?s architecture and functionalities focusing on the evaluation, comparison, and combination of different traffic classification techniques, which can be applied to both live traffic and previously captured traffic traces. Through scientific collaborations, and thanks to the support of the open source community, this platform gradually evolved over the past five years, supporting an increasing number of functionalities, some of which we highlight in this article through sample use cases.

An enhanced public key infrastructure to secure smart grid wireless communication networks

Abstract: It is expected that the smart grid will radically add new functionalities to legacy electrical power systems. However, we believe that this will in turn introduce many new security risks. With the smart grid's backbone communication networks and subnetworks, there are possible scenarios when these subnetworks can become vulnerable to attacks. Ensuring security in these networks is challenging because most devices are resource constrained. In addition, different protocols that are used in these networks use their own set of security requirements. In this article, the security requirements of smart grid communication networks are firstly identified. We then point out that although public key infrastructure (PKI) is a viable solution, it has some difficulties to satisfy the requirements in availability, privacy preservation, and scalability. To complement the functions of PKI, we introduce some novel mechanisms so that those security requirements can be met. In particular, we propose a mechanism to efficiently resist Denial-of-Service (DoS) attacks, and some suggestions to the security protocol design for different application categories.

Introduction to the quagga routing suite

Abstract: Off-the-shelf routing products are often constrained by being designed to meet specific needs, in terms of both software and hardware. Networking professionals may face problems that require customization of software, or additional processing facilities or data storage, which are not provided for by those products. The Quagga Routing Suite provides implementations of several common routing protocols, distributed over multiple processes communicating via IPC, and support for their development, with source code provided under a modification-friendly license. Quagga can help networking professionals build such custom solutions, in combination with other open source software packages. Quagga also provides a path for network researchers to increase the visibility of their work, making it available to a wider community for potential testing and use, increasing the impact of that research.

Adaptive multimedia streaming in information-centric networks

Abstract: ICN has received a lot of attention in recent years, and is a promising approach for the Future Internet design. As multimedia is the dominating traffic in today's and (most likely) the Future Internet, it is important to consider this type of data transmission in the context of ICN. In particular, the adaptive streaming of multimedia content is a promising approach for usage within ICN, as the client has full control over the streaming session and has the possibility to adapt the multimedia stream to its context (e.g. network conditions, device capabilities), which is compatible with the paradigms adopted by ICN. In this article we investigate the implementation of adaptive multimedia streaming within networks adopting the ICN approach. In particular, we present our approach based on the recently ratified ISO/IEC MPEG standard Dynamic Adaptive Streaming over HTTP and the ICN representative Content-Centric Networking, including baseline evaluations and open research challenges.

Keyflow: a prototype for evolving SDN toward core network fabrics

Abstract: The large bulk of packets/flows in future core networks will require a highly efficient header processing in the switching elements. Simplifying lookup in core network switching elements is capital to transport data at high rates and with low latency. Flexible network hardware combined with agile network control is also an essential property for future software-defined networking. We argue that only further decoupling between the control and data planes will unlock the flexibility and agility in SDN for the design of new network solutions for core networks. This article proposes a new approach named KeyFlow to build a flexible network-fabricbased model. It replaces the table lookup in the forwarding engine by elementary operations relying on a residue number system. This provides us tools to design a stateless core network by still using OpenFlow centralized control. A proof of concept prototype is validated using the Mininet emulation environment and OpenFlow 1.0. The results indicate RTT reduction above 50 percent, especially for networks with densely populated flow tables. KeyFlow achieves above 30 percent reduction in keeping active flow state in the network.

Big data: transforming the design philosophy of future internet

Abstract: Big data opens the era of the fourth paradigm for science discovery through datadriven computing. This new paradigm applies to the design of the future Internet, which currently faces issues in supporting new applications, efficient resource utilization, and continuous evolvement. We observe several technological transformations in network architecture, services, and applications, and point out the grand opportunities for designing future Internet architecture, communication models, and resource management mechanisms enabled by the availability of massive network data. In particular, we envision in the future Internet: 1) computational complexity replaces state complexity in the control plane; 2) data intelligence enables user choices and rewards innovations; and 3) correlations from data analytics help solve inherently hard optimization problems. Finally, we identify the key challenges in data-driven Internet design and outline future research directions.

Carrier aggregation/channel bonding in next generation cellular networks: methods and challenges

Abstract: To provide cellular systems with additional spectral resources, the wireless industry is considering the aggregation of frequency carriers in licensed, unlicensed, and shared access (SA) bands. In this article, we focus on reliable carrier aggregation/channel bonding (CA/CB) techniques, in which when CA/CB between the licensed, unlicensed, and SA carriers is performed, the licensed carrier is used for the primary and secondary carriers, and the unlicensed and SA carriers operate as additional secondary carriers. We provide a taxonomy of the use of CA/CB in cellular networks and highlight the differences between different CA/CB approaches. We make the case that although the licensed primary carrier can give reliable transmission of control signaling, due to the nature of unlicensed and SA bands, for the efficient aggregation of secondary carriers there is a need for new CA/CB methods. To illustrate our case for novel CA/CB methods, we provide examples for different network environments where intelligent CA/CB decisions can increase throughput compared to the traditional CA/CB methods. Finally, we highlight challenges in the design of novel CA/CB techniques in unlicensed and SA bands.

Toward transcoding as a service: energy-efficient offloading policy for green mobile cloud

Abstract: In this article we investigate energy-efficient offloading policy for transcoding as a service (TaaS) in a generic mobile cloud system. Computation on mobile devices can be offloaded to a mobile cloud system that consists of a dispatcher at the front end and a set of service engines at the back end. Particularly, a transcoding task can be executed on the mobile device (i.e. mobile execution) or offloaded and scheduled by the dispatcher to one of the service engines in the cloud (i.e. cloud execution). We aim to minimize the energy consumption of transcoding on the mobile device and service engines in the cloud while achieving low delay. For the mobile device, we formulate its offloading policy under delay deadline as a constrained optimization problem. We find an operational region on which execution mode, that is, mobile execution or cloud execution, is more energy efficient for the mobile device. For the cloud, we propose an online algorithm to dispatch transcoding tasks to service engines, with an objective to reduce energy consumption while achieving queue stability. By appropriately choosing the control variable, the proposed algorithm outperforms alternative algorithms, with lower time average energy consumption and time average queue length on the service engines. The proposed offloading policy can reduce energy consumption on both mobile devices and the cloud jointly, which provides guidelines for the design of green mobile cloud.

DROPv2: energy efficiency through network function virtualization

Abstract: Future Internet devices and network infrastructures need to be significantly more energy-efficient, scalable, and flexible in order to realize the extremely virtualized and optimized ICT/network infrastructures In this respect, this article presents a recent extension of an open source software framework, the Distributed Router Open Platform (DROP), to enable a novel distributed paradigm for network function virtualization through the integration of software defined network and information technology (IT) platforms, as well as for the control/management of flexible IP router platforms To answer the need for increased energy efficiency of the network function virtualization paradigms, DROP includes sophisticated power management mechanisms, which are exposed by means of the green abstraction layer (GAL), under consideration for standardization in ETSI Moreover, the DROP architecture has been specifically designed to act as ?glue? among a large number of the most promising and well-known open source software projects, providing network dataor control-plane capabilities

Cascading effects in interdependent networks

Abstract: Modern systems are increasingly dependent upon and interacting with each other, and become interdependent networks. These interdependent networks may exhibit some interesting and even surprising behaviors due to the interdependency and the interplay between the constituent systems. In this article we focus on two important phenomena, namely cascading failure in cyber-physical systems (CPS) and information cascade in coupled social networks. Specifically, cascading failures may occur in CPS that exhibit functional interdependency between two constituent systems (e.g. smart grid); information cascade may happen in multiple social networks that are coupled together by so-called multi-membership individuals. This article explores these two types of cascading effects in interdependent networks by reviewing existing studies in the literature. We review different models in the literature to study the two types of cascading effects in interdependent networks, and highlight the key findings from these studies.

Distributed Resource Allocation in Cloud-Based Wireless Multimedia Social Networks

Abstract: With the rapid penetration of mobile devices, more users prefer to watch multime- dia live-streaming via their mobile terminals. Quality of service provision is normal- ly a critical challenge in such multimedia sharing environments. In this article, we propose a new cloud-based WMSN to efficiently deal with multimedia sharing and distribution. We first motivate the use of cloud computing and social contexts in sharing live streaming. Then our WMSN architecture is presented with the descrip- tion of the different components of the network. After that, we focus on distributed resource management and formulate the bandwidth allocation problem in a game- theoretical framework that is further implemented in a distributed manner. In addi- tion, we note the potential selfish behavior of mobile users for resource competition and propose a cheat-proof mechanism to motivate mobile users to share band- width. Illustrative results demonstrate the best responses of different users in the game equilibrium as well as the effectiveness of the proposed cheating avoidance scheme.

Toward 5G: when explosive bursts meet soft cloud

Abstract: Rapid growing demand for mobile data traffic challenges capacities and service provision in the next-generation (5G) cellular networks. Real measurement data from operating cellular networks indicates that the traffic models and scenarios disobey our traditional assumptions (i.e., expressing bursty nature). As a result, current network architectures and service management may cause experience deterioration of subscribers in future networks. In this article, we propose three approaches to alleviate the influence of various traffic bursts: baseband resource pool on a cloud platform as wireless infrastructure to enhance the capacity and flexibility of networks, cloud core networks to provide dynamic extension and service flow control abilities, and software-defined bearer networks to simplify service delivery instructed by core networks. Different from conventional stovepipe-like cloud computing network architectures, our proposed architecture interconnects and shares information between entities, breaking through horizontal device barriers and vertical layers. These cloud-based approaches not only avoid the potentially negative impact of bursts, but also provide a software-controlled end-to-end service management framework for future cellular networks. In addition, by taking advantage of open interfaces of cloud-based network elements, service control algorithms and network APIs could also be implemented to realize smart and soft 5G cellular networks.

Spatial big data and wireless networks: experiences, applications, and research challenges

Abstract: In this article we demonstrate that spatial big data can play a key role in many emerging wireless networking applications. We also argue that spatial and spatiotemporal problems have their own very distinct role in the big data context compared to the commonly considered relational problems. We describe three major application scenarios for spatial big data, each imposing specific design and research challenges. We then present our work on developing highly scalable parallel processing frameworks for spatial data in the Hadoop framework using the MapReduce computational model. Our results show that using Hadoop enables highly scalable implementations of algorithms for common spatial data processing problems. However, development of these implementations requires significant specialized knowledge, demonstrating the need for development of more user-friendly alternatives.