202 research outputs found
Imprecise Markov Models for Scalable and Robust Performance Evaluation of Flexi-Grid Spectrum Allocation Policies
The possibility of flexibly assigning spectrum resources with channels of
different sizes greatly improves the spectral efficiency of optical networks,
but can also lead to unwanted spectrum fragmentation.We study this problem in a
scenario where traffic demands are categorised in two types (low or high
bit-rate) by assessing the performance of three allocation policies. Our first
contribution consists of exact Markov chain models for these allocation
policies, which allow us to numerically compute the relevant performance
measures. However, these exact models do not scale to large systems, in the
sense that the computations required to determine the blocking
probabilities---which measure the performance of the allocation
policies---become intractable. In order to address this, we first extend an
approximate reduced-state Markov chain model that is available in the
literature to the three considered allocation policies. These reduced-state
Markov chain models allow us to tractably compute approximations of the
blocking probabilities, but the accuracy of these approximations cannot be
easily verified. Our main contribution then is the introduction of
reduced-state imprecise Markov chain models that allow us to derive guaranteed
lower and upper bounds on blocking probabilities, for the three allocation
policies separately or for all possible allocation policies simultaneously.Comment: 16 pages, 7 figures, 3 table
Modelling Spectrum Assignment in a Two-Service Flexi-Grid Optical Link with Imprecise Continuous-Time Markov Chains
Flexi-grid optical networks (Gerstel et al., 2012) are a novel paradigm for managing the capacity of optical fibers more efficiently. The idea is to divide the spectrum in small frequency slices, and to consider an allocation policy that adaptively assigns one or multiple contiguous slices to incoming bandwidth requests, depending on their size. However, as new requests arrive and old requests are served and return resources to the free pool, the spectrum might become fragmented, leading to inefficiency and unfairness.
It is therefore necessary to quantify the performance of a given spectrum allocation policy, for example by determining the probability that a bandwidth request is blocked, in the sense that it cannot be allocated because there are not enough contiguous free slices.
To determine blocking probabilities for an optical link with traffic requests of two different sizes and a random allocation policy, Kim et al. (2015) use a Markov chain. Unfortunately, the number of states of this Markov chain grows exponentially with the number of available frequency slices, making it infeasible to determine blocking probabilities for large systems.
Therefore, Kim et al. (2015) also consider a second Markov chain, with a highly reduced state space and approximate transition rates, to obtain approximations of these blocking probabilities. In this contribution, we first show how to construct such full and reduced-state Markov chains for two other allocation policies, and compare these with the random policy.
Next, we introduce a so-called imprecise Markov chain, which has the same reduced state space but imprecise (interval-valued) transition rates, and explain how it can be used to determine guaranteed upper and lower bounds for --- instead of approximations of --- blocking probabilities, for different families of allocation policies
Enabling privacy in a gaming framework for smart electricity and water grids
Serious games are potentially powerful tools to influence users' preferences and attitudes. However, privacy concerns related to the misuse of data gathered from the players may emerge in online-gaming interactions. This work proposes a privacy-friendly framework for a gaming platform aimed at reducing energy and water usage, where players are grouped in teams with the challenge of maintaining the aggregated consumption of its members below a given threshold. We discuss a communication protocol which enables the team members to compute their overall consumption with- out disclosing individual measurements. Moreover, the protocol prevents the gaming platform from learning the consumption data and challenge objectives of the players. Correctness and truthfulness checks are included in the protocol to detect cheaters declaring false consumption data or providing altered game results. The security and performance of the framework are assessed, showing that scalability is ensured thanks to the limited data exchange and lightweight cryptographic operations
Multiservice UAVs for Emergency Tasks in Post-disaster Scenarios
UAVs are increasingly being employed to carry out surveillance, parcel
delivery, communication-support and other specific tasks. Their equipment and
mission plan are carefully selected to minimize the carried load an overall
resource consumption. Typically, several single task UAVs are dispatched to
perform different missions. In certain cases, (part of) the geographical area
of operation may be common to these single task missions (such as those
supporting post-disaster recovery) and it may be more efficient to have
multiple tasks carried out as part of a single UAV mission using common or even
additional specialized equipment.
In this paper, we propose and investigate a joint planning of multitask
missions leveraging a fleet of UAVs equipped with a standard set of accessories
enabling heterogeneous tasks. To this end, an optimization problem is
formulated yielding the optimal joint planning and deriving the resulting
quality of the delivered tasks. In addition, a heuristic solution is developed
for large-scale environments to cope with the increased complexity of the
optimization framework. The developed joint planning of multitask missions is
applied to a specific post-disaster recovery scenario of a flooding in the San
Francisco area. The results show the effectiveness of the proposed solutions
and the potential savings in the number of UAVs needed to carry out all the
tasks with the required level of quality
An optimisation-based energy disaggregation algorithm for low frequency smart meter data
An algorithm for the non-intrusive disaggregation of energy consumption into its end-uses, also known as non-intrusive appliance load monitoring (NIALM), is presented. The algorithm solves an optimisation problem where the objective is to minimise the error between the total energy consumption and the sum of the individual contributions of each appliance. The algorithm assumes that a fraction of the loads present in the household is known (e.g. washing machine, dishwasher, etc.), but it also considers unknown loads, treating them as a single load. The performance of the algorithm is then compared to that obtained by two state of the art disaggregation approaches implemented in the publicly available NILMTK framework. The first one is based on Combinatorial Optimization, the second one on a Factorial Hidden Markov Model. The results show that the proposed algorithm performs satisfactorily and it even outperforms the other algorithms from some perspectives
Privacy-Friendly Load Scheduling of Deferrable and Interruptible Domestic Appliances in Smart Grids
The massive integration of renewable energy sources in the power grid ecosystem
with the aim of reducing carbon emissions must cope with their intrinsically
intermittent and unpredictable nature. Therefore, the grid must improve its
capability of controlling the energy demand by adapting the power consumption
curve to match the trend of green energy generation. This could be done by
scheduling the activities of deferrable and/or interruptible electrical appliances.
However, communicating the users' needs about the usage of their appliances
also leaks sensitive information about their habits and lifestyles, thus arising
privacy concerns.
This paper proposes a framework to allow the coordination of energy consumption
without compromising the privacy of the users: the service requests
generated by the domestic appliances are divided into crypto-shares using Shamir
Secret Sharing scheme and collected through an anonymous routing protocol by
a set of schedulers, which schedule the requests by directly operating on the
shares. We discuss the security guarantees provided by our proposed infrastructure
and evaluate its performance, comparing it with the optimal scheduling
obtained by means of an Integer Linear Programming formulation
Privacy-friendly appliance load scheduling in smart grids
Abstract—The massive integration of renewable energy sources into the power grid ecosystem with the aim of reducing carbon emissions must cope with their intrinsically intermittent and unpredictable nature. Therefore, the grid must improve its capability of controlling the energy demand by adapting the power consumption curve to match the trend of green energy generation. This could be done by scheduling the activities of deferrable electrical appliances. However, communicating the users ’ needs about the usage of the electrical appliances leaks sensitive information about habits and lifestyles of the customers, thus arising privacy concerns. This paper proposes a privacy-preserving framework to allow the coordination of energy con-sumption without compromising the privacy of the users: the ser-vice requests generated by the domestic appliances are diveded in crypto-shares using Shamir Secret Sharing scheme and collected through an anonymous routing protocol based on Crowds by a set of schedulers, which schedule the requests operating directly on the shares. We discuss the security guarantees provided by our proposed infrastructure and evaluate its performance, comparing it with the optimal scheduling obtained through an Integer Linear Programming formulation. I
Evaluation of the Precision-Privacy Tradeoff of Data Perturbation for Smart Metering
Abstract:
Smart grid users and standardization committees require that utilities and third parties collecting metering data employ techniques for limiting the level of precision of the gathered household measurements to a granularity no finer than what is required for providing the expected service. Data aggregation and data perturbation are two such techniques. This paper provides quantitative means to identify a tradeoff between the aggregation set size, the precision on the aggregated measurements, and the privacy level. This is achieved by formally defining an attack to the privacy of an individual user and calculating how much its success probability is reduced by applying data perturbation. Under the assumption of time-correlation of the measurements, colored noise can be used to even further reduce the success probability. The tightness of the analytical results is evaluated by comparing them to experimental data
A privacy-friendly game-theoretic distributed scheduling system for domestic appliances
open3Game-theoretic Demand Side Management (DSM)
systems have been investigated as a decentralized approach for
the collaborative scheduling of the usage of domestic electrical
appliances within a set of households. Such systems allow for the
shifting of the starting time of deferrable devices according to
the current energy price or power grid condition, in order to
reduce the individual monthly bill or to adjust the power load
experienced by the grid while meeting the users’ preferences
about the time of use. The drawback of DSM distributed
protocols is that they require each user to communicate his/her
own energy consumption patterns to the other users, which may
leak sensitive information regarding private habits.
This paper proposes a distributed Privacy-Friendly DSM
system which preserves users’ privacy by integrating data aggregation
and perturbation techniques: users decide their schedule
according to aggregated consumption measurements perturbed
by means of Additive White Gaussian Noise (AWGN). We
evaluate the noise power and the size of the set of users required
to achieve a given privacy level, quantified by means of the
Kullback-Leibler divergence. The performance of our proposed
DSM system are compared to the ones obtained by a benchmark
system which does not support privacy preservation in terms of
social cost, peak demand and convergence time. Results show
that privacy can be preserved at the cost of increasing the peak
demand and the number of game iterations, whereas social cost
is only marginally incremented.C Rottondi; A Barbato; G VerticaleRottondi, CRISTINA EMMA MARGHERITA; Barbato, Antimo; Verticale, Giacom
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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