This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Monte Carlo simulation, thereby providing and introduction to the remaining papers of this special issue. Lastly, it discusses new interesting research horizons.

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An introduction to MCMC for machine learning

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

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

Smart Grid - The New and Improved Power Grid:

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A Break in the Clouds: Towards a Cloud Definition

Motivated by the recent explosion of interest around blockchains, we examine whether they make a good fit for the Internet of Things (IoT) sector. Blockchains allow us to have a distributed peer-to-peer network where non-trusting members can interact with each other without a trusted intermediary, in a verifiable manner. We review how this mechanism works and also look into smart contracts—scripts that reside on the blockchain that allow for the automation of multi-step processes. We then move into the IoT domain, and describe how a blockchain-IoT combination: 1) facilitates the sharing of services and resources leading to the creation of a marketplace of services between devices and 2) allows us to automate in a cryptographically verifiable manner several existing, time-consuming workflows. We also point out certain issues that should be considered before the deployment of a blockchain network in an IoT setting: from transactional privacy to the expected value of the digitized assets traded on the network. Wherever applicable, we identify solutions and workarounds. Our conclusion is that the blockchain-IoT combination is powerful and can cause significant transformations across several industries, paving the way for new business models and novel, distributed applications.

Blockchains and Smart Contracts for the Internet of Things

In this article we provide an overview of pricing concepts for broadband multiservice networks. We review the notions of flat pricing, priority pricing, Paris-Metro pricing, smart-market pricing, responsive pricing, expected capacity pricing, edge pricing, and effective bandwidth pricing. We use numerous evaluation criteria, including network, economic, and social efficiency, as well as their suitability in using pricing as a means for congestion control. Some of the schemes are based on best-effort networks, and are thus unable to provide the user with quality of service (QoS) guarantees. Others build on networks with connection admission control functions and are thus able to provide individual QoS guarantees. We particularly investigate the relevant time frame over which pricing schemes are assumed to operate. The majority of the schemes work on short time frames (on the order of minutes), which makes them applicable to use pricing as an additional means for controlling congestion. We also consider technical aspects such as compliance with existing networking technologies or computational overheads associated with billing and accounting.

An overview of pricing concepts for broadband IP networks

Rapid deployment of IEEE 802.11 wireless local area networks (WLANs) and their increasing quality of service (QoS) requirements motivate extensive performance evaluations of the upcoming 802.11e QoS-aware enhanced distributed coordination function (EDCA). Most of the analytical studies up-to-date have been based on one of the three major performance models in legacy distributed coordination function analysis, requiring a large degree of complexity in solving multidimensional Markov chains. Here, we expose the common guiding principle behind these three seemingly different models. Subsequently, by abstracting, unifying, and extending this common principle, we propose a new unified performance model and analysis method to study the saturation throughput and delay performance of EDCA, under the assumption of a finite number of stations and ideal channel conditions in a single-hop WLAN. This unified model combines the strengths of all three models, and thus, is easy to understand and apply; on the other hand, it helps increase the understanding of the existing performance analysis. Despite its appealing simplicity, our unified model and analysis are validated very well by simulation results. Ultimately, by means of the proposed model, we are able to precisely evaluate the differentiation effects of EDCA parameters on WLAN performance in very broad settings, a feature which is essential for network design.

A unified model for the performance analysis of IEEE 802.11e EDCA

Variable bit rate (VBR) compressed video is expected to become one of the major loading factors in high-speed packet networks such as ATM-based B-ISDN. However, recent measurements based on long empirical traces (complete movies) revealed that VBR video traffic possesses self-similar (or fractal) characteristics, meaning that the dependence in the traffic stream lasts much longer than traditional models can capture.In this paper, we present a unified approach which, in addition to accurately modeling the marginal distribution of empirical video records, also models directly both the short and the long-term empirical autocorrelation structures. We also present simulation results using synthetic data and compare with results based on empirical video traces.Furthermore, we extend the application of efficient estimation techniques based on importance sampling that we had used before only for simple fractal processes. We use importance sampling techniques to efficiently estimate low probabilities of packet losses that occur when a multiplexer is fed with synthetic traffic from our self-similar VBR video model.

Modeling and simulation of self-similar variable bit rate compressed video: a unified approach

In order to increase the penetration of electric vehicles, a network of fast charging stations that can provide drivers with a certain level of quality of service (QoS) is needed. However, given the strain that such a network can exert on the power grid, and the mobility of loads represented by electric vehicles, operating it efficiently is a challenging and complex problem. In this paper, we examine a network of charging stations equipped with an energy storage device and propose a scheme that allocates power to them from the grid, as well as routes customers. We examine three scenarios, gradually increasing their complexity. In the first one, all stations have identical charging capabilities and energy storage devices, draw constant power from the grid and no routing decisions of customers are considered. It represents the current state of affairs and serves as a baseline for evaluating the performance of the proposed scheme. In the second scenario, power to the stations is allocated in an optimal manner from the grid and in addition a certain percentage of customers can be routed to nearby stations. In the final scenario, optimal allocation of both power from the grid and customers to stations is considered. The three scenarios are evaluated using real traffic traces corresponding to weekday rush hour from a large metropolitan area in the US. The results indicate that the proposed scheme offers substantial improvements of performance compared to the current mode of operation; namely, more customers can be served with the same amount of power, thus enabling the station operators to increase their profitability. Further, the scheme provides guarantees to customers in terms of the probability of being blocked (and hence not served) by the closest charging station to their location. Overall, the paper addresses key issues related to the efficient operation, both from the perspective of the power grid and the drivers satisfaction, of a network of charging stations.

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Michael Devetsikiotis

Papers

Blockchains and Smart Contracts for the Internet of Things

An overview of pricing concepts for broadband IP networks

A unified model for the performance analysis of IEEE 802.11e EDCA

Modeling and simulation of self-similar variable bit rate compressed video: a unified approach

Electric Power Allocation in a Network of Fast Charging Stations