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

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

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

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

A privacy-friendly gaming framework in smart electricity and water grids

Serious games can be used to push consumers of common-pool resources toward socially responsible consumption patterns. However, gamified interactions can result in privacy leaks and potential misuses of player-provided data. In the Smart Grid ecosystem, a smart metering framework providing some basic cryptographic primitives can enable the implementation of serious games in a privacy-friendly manner. This paper presents a smart metering architecture in which the users have access to their own high-frequency data and can use them as the input data to a multi-party secure protocol. Authenticity and correctness of the data are guaranteed by the usage of a public blockchain. The framework enables a gaming platform to administer a set of team game activities aimed at promoting a more sustainable usage of energy and water. We discuss and assess the performance of a protocol based on Shamir secret sharing scheme, which enables the members of the teams to calculate their overall consumption and to compare it with those of other teams without disclosing individual energy usage data. Additionally, the protocol impedes that the game platform learns the meter readings of the players (either individual or aggregated) and their challenge objectives

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

Reducing probes for quality of transmission estimation in optical networks with active learning

Estimating the quality of transmission (QoT) of a lightpath before its establishment is a critical procedure for efficient design and management of optical networks. Recently, supervised machine learning (ML) techniques for QoT estimation have been proposed as an effective alternative to well-established, yet approximated, analytic models that often require the introduction of conservative margins to compensate for model inaccuracies and uncertainties. Unfortunately, to ensure high estimation accuracy, the training set (i.e., the set of historical field data, or "samples," required to train these supervised ML algorithms) must be very large, while in real network deployments, the number of monitored/monitorable lightpaths is limited by several practical considerations. This is especially true for lightpaths with an above-threshold bit error rate (BER) (i.e., malfunctioning or wrongly dimensioned lightpaths), which are infrequently observed during network operation. Samples with above-threshold BERs can be acquired by deploying probe lightpaths, but at the cost of increased operational expenditures and wastage of spectral resources. In this paper, we propose to use active learning to reduce the number of probes needed for ML-based QoT estimation. We build an estimation model based on Gaussian processes, which allows iterative identification of those QoT instances that minimize estimation uncertainty. Numerical results using synthetically generated datasets show that, by using the proposed active learning approach, we can achieve the same performance of standard offline supervised ML methods, but with a remarkable reduction (at least 5% and up to 75%) in the number of training samples

Enabling Privacy in a Distributed Game-Theoretical Scheduling System for Domestic Appliances

Demand side management (DSM) makes it possible to adjust the load experienced by the power grid while reducing the consumers' bill. Game-theoretic DSM is an appealing decentralized approach for collaboratively scheduling the usage of domestic electrical appliances within a set of households while meeting the users' preferences about the usage time. The drawback of distributed DSM protocols is that they require each user to communicate his/her own energy consumption patterns, which may leak sensitive information regarding private habits. This paper proposes a distributed privacy-friendly DSM system that 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. We evaluate the noise power and the number of users required to achieve a given privacy level, quantified by means of the increase of the information entropy of the aggregated energy consumption pattern. The performance of our proposed DSM system is compared to the one of a benchmark system that does not support privacy preservation in terms of total bill, peak demand, and convergence time. Results show that privacy can be improved at the cost of increasing the peak demand and the number of game iterations, whereas the total bill is only marginally incremented