https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

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Internet of Things for Smart Cities

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 Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

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Smart Grid — The New and Improved Power Grid: A Survey

Improved mobility and connectivity is one of the many challenges to be faced in future generation wireless net- works. In this paper, we first propose a novel mobility model whose aim is to describe the group mobility behavior in quite a general manner. Moreover, we present a possible on-line algo- rithm to aggregate terminals whose links remain stable over the time, i.e., showing correlated mobility patterns. Finally, we present simulation results to validate our terminal aggregation algorithms.

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On group mobility patterns and their exploitation to logically aggregate terminals in wireless networks

The exponential growth of media streaming traffic will have a strong impact on the bandwidth consumption of the future wireless infrastructure. One key challenge is to deliver services taking into account the stringent requirements of mobile video streaming, e.g., the users' expected Quality-of-Service. Admission control and resource allocation can strongly benefit from the use of anticipatory information such as the prediction of future user's demand and expected channel gain. In this paper, we use this information to formulate an optimal admission control scheme that maximizes the number of accepted users into the system with the constraint that not only the current but also the expected demand of all users must be satisfied. Together with the optimal set of accepted users, the optimal resource scheduling is derived. In order to have a solution that can be computed in a reasonable time, we propose a low complexity heuristic. Numerical results show the performance of the proposed scheme with respect to the state of the art.

Anticipatory Admission Control and Resource Allocation for Media Streaming in Mobile Networks

The Internet of Things is expected to increase the amount of data produced and exchanged in the network, due to the huge number of smart objects that will interact with one another. The related information management and transmission costs are increasing and becoming an almost unbearable burden, due to the unprecedented number of data sources and the intrinsic vastness and variety of the datasets. In this paper, we propose RAZOR, a novel lightweight algorithm for data compression and classification, which is expected to alleviate both aspects by leveraging the advantages offered by data mining methods for optimizing communications and by enhancing information transmission to simplify data classification. In particular, RAZOR leverages the concept of motifs, recurrent features used for signal categorization, in order to compress data streams: in such a way, it is possible to achieve compression levels of up to an order of magnitude, while maintaining the signal distortion within acceptable bounds and allowing for simple lightweight distributed classification. In addition, RAZOR is designed to keep the computational complexity low, in order to allow its implementation in the most constrained devices. The paper provides results about the algorithm configuration and a performance comparison against state-of-the-art signal processing techniques.

RAZOR: A Compression and Classification Solution for the Internet of Things

Mobile video delivery forms the largest part of the traffic in cellular networks. Thus optimizing the resource allocation to satisfy a user's quality of experience is becoming paramount in modern communications. This paper belongs to the line of research known as anticipatory networking that makes use of prediction of wireless capacity to improve communication performance. In particular, we focus on the problem of optimal resource allocation for steady video delivery under maximum average quality constraints for multiple users. We formulate the problem as a piecewise linear program and provide a heuristic algorithm, which solution is close to optimal. Based on our formulation we are now able to trade off minimum video quality, average quality and offered network capacity.

Anticipatory quality-resource allocation for multi-user mobile video streaming

In this paper we discuss the exploitation of aggregated mobility patterns and physical proximity of nodes in a so-called ambient network, i.e., a wireless network with heterogeneous nodes and access techniques. We advocate to use the knowledge about node movements and geographical positions to create routing groups of adjacent nodes, which might be beneficial in order to decrease signaling overhead and increase transmission efficiency. Basically, routing groups (RGs) consist of aggregated logical structures which are built and maintained at the application layer. Their aim is to decrease the signaling overhead between group of nodes and access points and, at the same time, to improve connectivity by exploiting technology diversity and relaying techniques. On this matter, we describe a validation through simulation of a previously developed analytical work which is useful to evaluate the effectiveness of RG structures. Finally, we show the validity of the RG approach in terms of throughput and connectivity performance.

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Nicola Bui

Papers

On group mobility patterns and their exploitation to logically aggregate terminals in wireless networks

Anticipatory Admission Control and Resource Allocation for Media Streaming in Mobile Networks

RAZOR: A Compression and Classification Solution for the Internet of Things

Anticipatory quality-resource allocation for multi-user mobile video streaming

On the exploitation of user aggregation strategies in heterogeneous wireless networks