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

The rapid evolution of wireless technologies as well as their pervasive diffusion in everyday’s life has dramatically changed the world in the last two decades. On one side, service providers/operators can nowadays leverage new wireless technologies to reach new types of customers in a cost effective way; on the other side, end users are actually given richer and more flexible “connectivity opportunities” to obtain the required services. The down side of this scenario deals with the increased complexity in the management of all the system components. Users/customers are becoming more and more demanding in terms of the perceived quality of service (QoS), eventually expecting the very same quality they experience in wired networks in the wireless realm. To this extent, advanced wireless systems must be able to support and manage high-quality services, thus matching the heterogeneous requirements coming from the users. Concurrently, risks and security threats are inherent in any wireless network, and there is an increasing demand for effective security-enforcing techniques to attain authentication, privacy, confidentiality, data integrity, privacy, access control, and non-repudiation, when operating and/or using wireless networks. In this scenario, the deployment of security measures and the provision of QoS represent two critical and tightly coupled challenges in the design of network services, architectures, and protocols in today’s inter-networked world. Such challenges are even made harder in mobile wireless scenarios where the network infrastructure may feature mobile, energy and resource-constrained network devices. To this extent, the success of specific wireless networking technologies is often dependant on the capability of the specific technology to support QoS, on one side, and to provide security, on the other side. The aim of this special issue is to provide an upto-date snapshot on advanced solutions to support the design and deployment of secure and efficient services in wireless mobile networks. The special issue is composed of seven high-quality contributions, each one focusing on a specific component of the aforementioned framework. The first batch of four papers deals with security aspects. The first two papers in the batch focus on security solutions for Wireless Mesh Networks (WMNs); namely, the first one, DSA-Mesh: a Distributed Security Architecture forWMNs, proposes a fully distributed security architecture to ensure security and data confidentiality of the communications occurring in the backbone of a WMN; the proposed solution provides a distributed authentication and access control scheme for the wireless mesh routers taking part in the network, as well as a dynamic key distribution algorithm that supports layer-2 encryption. Whilst the wireless mesh architecture addressed in the first paper is general, in the second manuscript onDifferentiated Security in WMNs, the authors target the design of securemechanisms for IEEE 802.11s-basedWMNs. The rationale andmain contribution of the work is the replication in the wireless environment of the virtual LAN structures typical of the wired networks to achieve protection against internal/external attacks including selective forwarding attacks, routing attacks, and eavesdropping. The third contribution, Fast 802.11 handovers with 802.1X re-authentications, targets the issue of supporting secure client mobility in multi-access point Wireless LAN deployments. Namely, the authors analyze the impact of pre-authentication and re-authentication of a mobile client when handing over between two contiguous WLAN access points, and further propose an advance algorithm to reduce the handover latency while maintaining the authentication of the client itself. The last manuscript dealing with security aspects, Characterizing The Greedy Behavior in Wireless Ad Hoc Networks, switches the focus from static wireless network (wireless mesh and wireless LANs) to dynamic wireless ad hoc networks, by analyzing the network-wide impact of greedy behaviors of single wireless nodes. The authors further introduce a rational greedy strategy in the radio

Security for QoS assured wireless and mobile networks

Real-time measurements on the occupancy status of indoor and outdoor spaces can be exploited in many scenarios (HVAC and lighting system control, building energy optimization, allocation and reservation of spaces, etc.). Traditional systems for occupancy estimation rely on environmental sensors (CO2, temperature, humidity) or video cameras. In this paper, we depart from such traditional approaches and propose a novel occupancy estimation system which is based on the capture of Wi-Fi management packets from users' devices. The system, implemented on a low-cost ESP8266 microcontroller, leverages a supervised learning model to adapt to different spaces and transmits occupancy information through the MQTT protocol to a web-based dashboard. Experimental results demonstrate the validity of the proposed solution in four different indoor university spaces.

Occupancy Estimation Using Low-Cost Wi-Fi Sniffers

This special issue aims to consolidate and disseminate the latest developments and advances in the emerging area of multihop cognitive radio networks (CRNs). The five articles selected to be part of this special issue address recent research issues/advances at different layers of the cognitive protocol stack, shedding light on different aspects related to the design of effective multihop CRNs.

Networking over multi-hop cognitive networks [Guest Editorial]

Accurate and efficient resource utilization predictions are of vital importance for the future generation of mobile wireless networks. By anticipating network resource demand, the operator can perform proactive resource allocation and predictive network control to improve network resource efficiency. In this paper, we exploit deep and transfer learning algorithms for multi-step resource utilization prediction in radio networks. In particular, we propose long short-term memory network-based architectures with transfer learning for the multi-step prediction task, in order to address scalability, computation time and data storage limitations of current implementations for large-scale networks. We carry out extensive experiments on a dataset collected from an LTE field network. When predicting physical resource block percentage utilization, our approach achieves state of the art results with root mean square error below 12 for a four-hour-ahead prediction, in half of the computation time required by deep learning methods without transfer learning.

Transfer Learning for Multi-Step Resource Utilization Prediction

Wireless Mesh Networks (WMNs) are composed of a mix of fixed and mobile nodes interconnected via wireless links to form a multihop ad hoc network. They contain three types of network devices: Mesh Routers (MRs), Mesh Access Points (MAPs) and Mesh Clients (MCs). The functionality of both the MRs and the MAPs is twofold: they act as classical access points towards the MCs, whereas they have the capability to set up a Wireless Distribution System (WDS) by connecting to other mesh routers or access points through point to point wireless links. Both MRs and MAPs are often fixed and electrically powered devices. Furthermore, the MAPs are geared with some kind of broadband wired connectivity (ADSL, fiber, etc . . . ) and act as gateways toward the wired backbone. The structure of a WMN is illustrated in Figure 1.

Matteo Cesana

Papers

Security for QoS assured wireless and mobile networks

Occupancy Estimation Using Low-Cost Wi-Fi Sniffers

Networking over multi-hop cognitive networks [Guest Editorial]

Transfer Learning for Multi-Step Resource Utilization Prediction

On the Design of Wireless Mesh Networks