Michael Devetsikiotis

An overview of pricing concepts for broadband IP networks

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.

Blockchains and Smart Contracts for the Internet of Things

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.

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

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.

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

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.

Electric Power Allocation in a Network of Fast Charging Stations

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.

A survey on next generation mobile WiMAX networks: objectives, features and technical challenges

In order to meet the requirements of 4G mobile networks targeted by the cellular layer of IMT-advanced, next generation mobile WiMAX devices based on IEEE 802.16m will incorporate sophisticated signal processing, seamless handover functionalities between heterogeneous technologies and advanced mobility mechanisms. This survey provides a description of key projected features of the physical (PHY) and medium access control (MAC) layers of 802.16m, as a major candidate for providing aggregate rates at the range of Gbps to high-speed mobile users. Moreover, a new unified method for simulation modeling, namely the evaluation methodology (EVM), introduced in 802.16m, is also presented.

An algorithmic approach to the optimization of importance sampling parameters in digital communication system simulation

Importance sampling is recognized as a potentially powerful method for reducing simulation runtimes when estimating the bit error rate (BER) of communications systems using Monte Carlo simulation. Analytically, minimizing the variance of the importance sampling (IS) estimator with respect to the biasing parameters has typically yielded solutions for systems for which the BER could be found analytically. A technique for finding an asymptotically optimal set of biasing parameter values, in the sense that as the resolution of the search and the number of runs used both approach infinity, the algorithm converges to the true optimum, is proposed. The algorithm determines the amount of biasing that minimizes a statistical measure of the variance of the BER estimate and exploits a theoretically justifiable relationship, for small sample sizes, between the BER estimate and the amount of biasing. The translation biasing scheme is considered, although the algorithm is applicable to other parametric IS techniques. Only mild assumptions are required of the noise distribution and system. Experimentally, improvement factors ranging from two to eight orders of magnitude are obtained for a number of distributions for both linear and nonlinear systems with memory. >

Modelling prioritized MPEG video using TES and a frame spreading strategy for transmission in ATM networks

This paper presents an efficient transmission mechanism, using frame spreading, for variable bitrate (VBR) MPEG compressed video, through an ATM multiplexer, such as a cable head-end. A priority scheme is implemented in a software MPEG encoder which produces a proportionate traffic in both (i.e., high and low) priority partitions for all three frame types (intraframe, predicted and interpolated) used in MPEG. An ATM multiplexer with a pushout buffer scheme is implemented for the study, in order to provide priority scheduling at the multiplexer for the two priority partitions. The multiplexer is fed with VBR MPEG traffic and performance statistics such as the cell loss ratios are studied for various frame spreading scenarios. Two statistical models are developed using TES (transform expand sample) for VBR MPEG video having two levels of priority. The first model is matched with the empirical histogram and autocorrelation function of each frame type (I, P and B). The second model is created with the assumption of a gamma distribution for the number of bits in each frame type. Experiments are conducted using both models and the results are compared.

Local energy storage sizing in plug-in hybrid electric vehicle charging stations under blocking probability constraints

Plug-in hybrid electric vehicles (PHEV) are becoming gradually more attractive than internal combustion engine vehicles, even though the current electrical grid is not potentially able to support the required power demand increase to introduce charging stations. Acknowledging that design and development of charging stations has crucial importance, this paper introduces a candidate PHEV charging station architecture, along with a quantitative stochastic model, that allows us to analyze the performance of the system by using arguments from queuing theory and economics. A relevant component of the proposed architecture is the capability of the charging stations to store excess power obtained from the grid. The goal is to design a general architecture which will be able to sustain grid stability, while providing a required level of quality of service; and to describe a general methodology to analyze the performance of such stations with respect to the traffic characteristics, energy storage size, pricing and cost parameters. Our results indicate that significant gains in net cost/profit and useful insights can be made with the right choice of storage size. Such considerations are crucial in this early stage of designing the smart grid and charging stations of the future.

Demand response control for PHEV charging stations by dynamic price adjustments

Because of their economical operation and low environmental pollution, PHEVs (Plug-in Hybrid Electric Vehicles) are rapidly substituting gasoline vehicles. However, there still exist obstacles to proliferating their use, such as their relatively short driving range and long battery charging time. At the same time, it is recognized that the current increasing trend of PHEV use will have a serious impact on the stability of power grids (i.e., electricity providers). Along with improving the performance of PHEVs, the installation of charging stations, which addresses such problems, is essentially required in smart grid communities. This paper proposes an operational framework for multiple PHEV charging stations. To maintain the power grid stability, regulating electric supply for charging stations through support planning is an attractive approach. In this direction, we determine a condition under which customers can receive improved QoS (Quality of Service), and provide an algorithm which allocates PHEVs into the condition. Our analysis is based on a multi-queue system, used as a model of charging stations whose dynamics we investigate. Specifically, our interest is the performance change when demand responses (i.e., the behavior of customers) are controlled. We proceed with our investigation in two steps: In the first step, we consider the PHEV allocation problem. We formulate an optimization problem which can minimize the waiting time of customers and obtain its solution. Then, we additionally regard the size constraint of charging stations and propose an optimal PHEV allocation algorithm. In the second step, we modify this algorithm to work in realistic scenarios. If PHEVs do not receive any incentives (or penalties), there is no restriction to control their allocation. At the station side, we suggest price control methods and show that the optimal allocation can be attained by them. For each step, we provide test results to validate our analysis.