In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The PASCAL Visual Object Classes Challenge

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Deep Learning in Mobile and Wireless Networking: A Survey

Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Among others, mainly radio frequency identification (RFID), wireless sensor network (WSN), and smart mobile technologies are leading this evolutionary trend. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. Staying true to the IoT vision, we propose a smart hospital system (SHS), which relies on different, yet complementary, technologies, specifically RFID, WSN, and smart mobile, interoperating with each other through a Constrained Application Protocol (CoAP)/IPv6 over low-power wireless personal area network (6LoWPAN)/representational state transfer (REST) network infrastructure. The SHS is able to collect, in real time, both environmental conditions and patients’ physiological parameters via an ultra-low-power hybrid sensing network (HSN) composed of 6LoWPAN nodes integrating UHF RFID functionalities. Sensed data are delivered to a control center where an advanced monitoring application (MA) makes them easily accessible by both local and remote users via a REST web service. The simple proof of concept implemented to validate the proposed SHS has highlighted a number of key capabilities and aspects of novelty, which represent a significant step forward compared to the actual state of the art.

An IoT-Aware Architecture for Smart Healthcare Systems

Ad-hoc networking, though an attractive solution for many applications, still has many unsolved issues, such as the hiddenterminal problem, flexible and prompt access, QoS provisioning, and efficient broadcast service. In this paper we present a MAC architecture able to solve the above issues in environments with no power consumption limitations, such as networks for inter-vehicle communications. This new architecture is based on a completely distributed access technique, RR-ALOHA, capable of dynamically establishing, for each active terminal in the network, a reliable single-hop broadcast channel on a slotted/framed structure. Though the proposed architecture uses a slotted channel it can be adapted to operate on the physical layer of different standards, including the UMTS Terrestrial Radio Access TDD, and IEEE 802.11. The paper presents the mechanisms that compose the new MAC: the basic RR-ALOHA protocol, an efficient broadcast service and the reservation of point-to-point channels that exploit parallel transmissions. Some basic performance figures are discussed to prove the effectiveness of the protocol.

ADHOC MAC: new MAC architecture for ad hoc networks providing efficient and reliable point-to-point and broadcast services

Cognitive radio networks (CRNs) are composed of cognitive, spectrum-agile devices capable of changing their configurations on the fly based on the spectral environment. This capability opens up the possibility of designing flexible and dynamic spectrum access strategies with the purpose of opportunistically reusing portions of the spectrum temporarily vacated by licensed primary users. On the other hand, the flexibility in the spectrum access phase comes with an increased complexity in the design of communication protocols at different layers. This work focuses on the problem of designing effective routing solutions for multi-hop CRNs, which is a focal issue to fully unleash the potentials of the cognitive networking paradigm. We provide an extensive overview of the research in the field of routing for CRNs, clearly differentiating two main categories: approaches based on a full spectrum knowledge, and approaches that consider only local spectrum knowledge obtained via distributed procedures and protocols. In each category we describe and comment on proposed design methodologies, routing metrics and practical implementation issues. Finally, possible future research directions are also proposed.

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Routing in cognitive radio networks: Challenges and solutions

Optimization models and methods for planning wireless mesh networks

A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today’s digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.

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A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Ad-hoc networking is under evaluation for inter-vehicle communication architectures. In spite of the large existing literature, many problems generated by the peculiarity of this specific scenario are still unsolved. In this paper we present a novel MAC protocol, RR-ALOHA, able to guarantee a reliable single-hop broadcast communication in an ad-hoc network environment where the hidden terminals problem exists. This protocol is designed for the inter-vehicle communication architecture based on UTRA-TDD slotted physical channel, but can be easily modified to operate on other standard physical layers. According to this protocol any active terminal can reserve a channel by capturing a slot in the frame. Reliable communication is guaranteed, after access, even in presence of hidden terminals thanks to the information exchanged by the active terminals. The operation of the protocol is completely distributed and enables also to install channels of different speeds to satisfy QoS of different services. The protocol feasibility and some basic performance figures are discussed to prove the effectiveness of the protocol itself in the inter-vehicle communication scenario.

Matteo Cesana

Papers

ADHOC MAC: new MAC architecture for ad hoc networks providing efficient and reliable point-to-point and broadcast services

Routing in cognitive radio networks: Challenges and solutions

Optimization models and methods for planning wireless mesh networks

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

RR-ALOHA, a Reliable R-ALOHA broadcast channel for ad-hoc inter-vehicle communication networks