Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks

The next generation wireless networks (i.e. 5G and beyond), which would be extremely dynamic and complex due to the ultra-dense deployment of heterogeneous networks (HetNets), poses many critical challenges for network planning, operation, management and troubleshooting. At the same time, generation and consumption of wireless data are becoming increasingly distributed with ongoing paradigm shift from people-centric to machine-oriented communications, making the operation of future wireless networks even more complex. In mitigating the complexity of future network operation, new approaches of intelligently utilizing distributed computational resources with improved context-awareness becomes extremely important. In this regard, the emerging fog (edge) computing architecture aiming to distribute computing, storage, control, communication, and networking functions closer to end users, have a great potential for enabling efficient operation of future wireless networks. These promising architectures make the adoption of artificial intelligence (AI) principles which incorporate learning, reasoning and decision-making mechanism, as natural choices for designing a tightly integrated network. Towards this end, this article provides a comprehensive survey on the utilization of AI integrating machine learning, data analytics and natural language processing (NLP) techniques for enhancing the efficiency of wireless network operation. In particular, we provide comprehensive discussion on the utilization of these techniques for efficient data acquisition, knowledge discovery, network planning, operation and management of the next generation wireless networks. A brief case study utilizing the AI techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on communication networks and services, (To appear

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: RAN, core network, and caching. We also present a general overview of 5G cellular networks composed of software defined network (SDN), network function virtualization (NFV), caching, and mobile edge computing (MEC) capable of meeting latency and other 5G requirements.Comment: Accepted in IEEE Communications Surveys and Tutorial

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks including definition, architecture and advantages. Next, a comprehensive survey of issues on computing, caching and communication techniques at the network edge is presented respectively. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks such as cloud technology, SDN/NFV and smart devices are discussed. Finally, open research challenges and future directions are presented as well

A study of research trends and issues in wireless ad hoc networks

Ad hoc network enables network creation on the fly without support of any predefined infrastructure. The spontaneous erection of networks in anytime and anywhere fashion enables development of various novel applications based on ad hoc networks. However, at the same ad hoc network presents several new challenges. Different research proposals have came forward to resolve these challenges. This chapter provides a survey of current issues, solutions and research trends in wireless ad hoc network. Even though various surveys are already available on the topic, rapid developments in recent years call for an updated account on this topic. The chapter has been organized as follows. In the first part of the chapter, various ad hoc network's issues arising at different layers of TCP/IP protocol stack are presented. An overview of research proposals to address each of these issues is also provided. The second part of the chapter investigates various emerging models of ad hoc networks, discusses their distinctive properties and highlights various research issues arising due to these properties. We specifically provide discussion on ad hoc grids, ad hoc clouds, wireless mesh networks and cognitive radio ad hoc networks. The chapter ends with presenting summary of the current research on ad hoc network, ignored research areas and directions for further research

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories and features/objectives of the papers) of this survey are now available publicly. Accepted by Elsevier Journal of Systems Architectur

The Future is Unlicensed: Coexistence in the Unlicensed Spectrum for 5G

5G has to fulfill the requirements of ultra-dense, scalable, and customizable networks such as IoT while increasing spectrum and energy efficiency. Given the diversity of envisaged applications and scenarios, one crucial property for 5G New Radio (NR) is flexibility: flexible UL/DL allocation, bandwidths, or scalable transmission time interval, and most importantly operation at different frequency bands. In particular, 5G should exploit the spectral opportunities in the unlicensed spectrum for expanding network capacity when and where needed. However, unlicensed bands pose the challenge of "coexisting networks", which mostly lack the means of communication for negotiation and coordination. This deficiency is further exacerbated by the heterogeneity, massive connectivity, and ubiquity of IoT systems and applications. Therefore, 5G needs to provide mechanisms to coexist and even converge in the unlicensed bands. In that regard, WiFi, as the most prominent wireless technology in the unlicensed bands, is both a key enabler for boosting 5G capacity and competitor of 5G cellular networks for the shared unlicensed spectrum. In this work, we describe spectrum sharing in 5G and present key coexistence solutions, mostly in the context of WiFi. We also highlight the role of machine learning which is envisaged to be critical for reaching coexistence and convergence goals by providing the necessary intelligence and adaptation mechanisms.Comment: 7 pages, 4 figure

Resource Management of energy-aware Cognitive Radio Networks and cloud-based Infrastructures

The field of wireless networks has been rapidly developed during the past decade due to the increasing popularity of the mobile devices. The great demand for mobility and connectivity makes wireless networking a field whose continuous technological development is very important as new challenges and issues are arising. Many scientists and researchers are currently engaged in developing new approaches and optimization methods in several topics of wireless networking. This survey paper study works from the following topics: Cognitive Radio Networks, Interactive Broadcasting, Energy Efficient Networks, Cloud Computing and Resource Management, Interactive Marketing and Optimization

Cloud Computing - Architecture and Applications

In the era of Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such humongous volume of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such computing framework for large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade. This book presents some critical applications in cloud frameworks along with some innovation design of algorithms and architecture for deployment in cloud environment. It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.Comment: Edited Volume published by Intech Publishers, Croatia, June 2017. 138 pages. ISBN 978-953-51-3244-8, Print ISBN 978-953-51-3243-1. Link: https://www.intechopen.com/books/cloud-computing-architecture-and-application

The Role of Computational Outage in Dense Cloud-Based Centralized Radio Access Networks

Centralized radio access network architectures consolidate the baseband operation towards a cloud-based platform, thereby allowing for efficient utilization of computing assets, effective inter-cell coordination, and exploitation of global channel state information. This paper considers the interplay between computational efficiency and data throughput that is fundamental to centralized RAN. It introduces the concept of computational outage in mobile networks, and applies it to the analysis of complexity constrained dense centralized RAN networks. The framework is applied to single-cell and multi-cell scenarios using parameters drawn from the LTE standard. It is found that in computationally limited networks, the effective throughput can be improved by using a computationally aware policy for selecting the modulation and coding scheme, which sacrifices spectral efficiency in order to reduce the computational outage probability. When signals of multiple base stations are processed centrally, a computational diversity benefit emerges, and the benefit grows with increasing user density.Comment: 7 pages, 10 figures, IEEE Global Telecommunication Conference (GLOBECOM), 2014, to appea

Delay-Tolerant Networking for Long-Term Animal Tracking

Enabling Internet connectivity for mobile objects that do not have a permanent home or regular movements is a challenge due to their varying energy budget, intermittent wireless connectivity, and inaccessibility. We present a hardware and software framework that offers robust data collection, adaptive execution of sensing tasks, and flexible remote reconfiguration of devices deployed on nomadic mobile objects such as animals. The framework addresses the overall complexity through a multi-tier architecture with low tier devices operating on a tight energy harvesting budget and high tier cloud services offering seamless delay-tolerant presentation of data to end users. Based on our multi-year experience of applying this framework to animal tracking and monitoring applications, we present the main challenges that we have encountered, the design of software building blocks that address these challenges, and examples of the data we collected on flying foxes.Comment: 14 pages, 5 figure