Privacy-cost trade-offs in smart electricity metering systems

Trade-offs between privacy and cost are studied for a smart grid consumer, whose electricity consumption is monitoredin almost real time by the utility provider (UP) through smart meter (SM) readings. It is assumed that an electrical battery isavailable to the consumer, which can be utilized both to achieve privacy and to reduce the energy cost by demand shaping.Privacy is measured via the mean squared distance between the SM readings and a target load profile, while time-of-use (ToU)pricing is considered to compute the cost incurred. The consumer can also sell electricity back to the UP to further improve theprivacy-cost trade-off. Two privacy-preserving energy management policies (EMPs) are proposed, which differ in the way the targetload profile is characterized. A more practical EMP, which optimizes the energy management less frequently, is also considered.Numerical results are presented to compare the privacy-cost trade-off of these EMPs, considering various privacy indicators

A general analytical approach for opportunistic cooperative systems with spatially random relays

This paper investigates an opportunistic cooperative system with multiple relays. The locations of the relays are essentially random due to their unpredictable mobility and are thus assumed to form a spatial Poisson process. A general analytical approach to performance analysis is developed to accommodate the randomness of the locations as well as the underlying channels. The outage probability of the system is derived based on the theory of point processes. In particular, two relay selection criteria, namely the best forward channel selection and the best worse channel selection, are used as examples to illustrate the proposed approach. The accuracy of the analytical results is verified by Monte-Carlo simulations with various system configurations. © 2011 IEEE.published_or_final_versio

On the tradeoffs between network state knowledge and secrecy

In this paper, the impact of network-state knowledge on the feasibility of secrecy is studied in the context of non-colluding active eavesdropping. The main contribution is the investigation of several scenarios in which increasing the available knowledge at each of the network components leads to some paradoxical observations in terms of the average secrecy capacity and average information leakage. These observations are in the context of a broadcast channel similar to the time-division downlink of a single-cell cellular system. Here, providing more knowledge to the eavesdroppers makes them more conservative in their attacks, and thus, less harmful in terms of average information leakage. Similarly, providing more knowledge to the transmitter makes it more careful and less willing to transmit, which reduces the expected secrecy capacity. These findings are illustrated with a numerical analysis that shows the impact of most of the network parameters in the feasibility of secrecy. © 2013 NICT

Joint Power Allocation and Beamforming for Energy-Efficient Two-Way Multi-Relay Communications

© 2017 IEEE. This paper considers the joint design of user power allocation and relay beamforming in relaying communications, in which multiple pairs of single-antenna users exchange information with each other via multiple-antenna relays in two time slots. All users transmit their signals to the relays in the first time slot while the relays broadcast the beamformed signals to all users in the second time slot. The aim is to maximize the system's energy efficiency (EE) subject to quality-of-service (QoS) constraints in terms of exchange throughput requirements. The QoS constraints are nonconvex with many nonlinear cross-terms, so finding a feasible point is already computationally challenging. The sum throughput appears in the numerator while the total consumption power appears in the denominator of the EE objective function. The former is a nonconcave function and the latter is a nonconvex function, making fractional programming useless for EE optimization. Nevertheless, efficient iterations of low complexity to obtain its optimized solutions are developed. The performance of the multiple-user and multiple-relay networks under various scenarios is evaluated to show the merit of the proposed method

Learning-based content caching with time-varying popularity profiles

Content caching at the small-cell base stations (sBSs) in a heterogeneous wireless network is considered. A cost function is proposed that captures the backhaul link load called the "offloading loss", which measures the fraction of the requested files that are not available in the sBS caches. Previous approaches minimize this offloading loss assuming that the popularity profile of the content is time-invariant and perfectly known. However, in many practical applications, the popularity profile is unknown and time-varying. Therefore, the analysis of caching with non-stationary and statistically dependent popularity profiles (assumed unknown, and hence, estimated) is studied in this paper from a learning-theoretic perspective. A probably approximately correct (PAC) result is derived, in which a high probability bound on the offloading loss difference, i.e., the error between the estimated (outdated) and the optimal offloading loss, is investigated. The difference is a function of the Rademacher complexity of the set of all probability measures on the set of cached content items, the β-mixing coefficient, 1/√t (t is the number of time slots), and a measure of discrepancy between the estimated and true popularity profiles

Cooperative Non-orthogonal Multiple Access With Simultaneous Wireless Information and Power Transfer

Accepted by IEEE Journal on Selected Areas in Communications (JSAC

Linear transceiver design for amplify-and-forward MIMO relay systems under channel uncertainties

Proceedings of the IEEE Wireless Communications and Networking Conference, 2010, p. 1-6In this paper, robust joint design of linear relay precoders and destination equalizers for amplify-and-forward (AF) MIMO relay systems under Gaussian channel uncertainties is investigated. After incorporating the channel uncertainties into the robust design based on the Bayesian framework, a closed-form solution is derived to minimize the mean-square-error (MSE) of the received signal at the destination. The effectiveness of the proposed robust transceiver is verified by simulations. ©2010 IEEE.published_or_final_versionThe IEEE Wireless Communications and Networking Conference (WCNC), Sydney, Australia, 18-21 April 2010. In Proceedings of WCNC, 2010, p. 1-

Superimposed training-based channel estimation and data detection for OFDM amplify-and-forward cooperative systems under high mobility

In this paper, joint channel estimation and data detection in orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) cooperative systems under high mobility is investigated. Unlike previous works on cooperative systems in which a number of subcarriers are solely occupied by pilots, partial data-dependent superimposed training (PDDST) is considered here, thus preserving the spectral efficiency. First, a closed-form channel estimator is developed based on the least squares (LS) method with Tikhonov regularization and a corresponding data detection algorithm is proposed using the linear minimum mean square error (LMMSE) criterion. In the derived channel estimator, the unknown data is treated as part of the noise and the resulting data detection may not meet the required performance. To address this issue, an iterative method based on the variational inference approach is derived to improve performance. Simulation results show that the data detection performance of the proposed iterative algorithm initialized by the LMMSE data detector is close to the ideal case with perfect channel state information. © 2006 IEEE.published_or_final_versio

Prioritizing consumers in smart grid: A game theoretic approach

This paper proposes an energy management technique for a consumer-to-grid system in smart grid. The benefit to consumers is made the primary concern to encourage consumers to participate voluntarily in energy trading with the central power station (CPS) in situations of energy deficiency. A novel system model motivating energy trading under the goal of social optimality is proposed. A single-leader multiple-follower Stackelberg game is then studied to model the interactions between the CPS and a number of energy consumers (ECs), and to find optimal distributed solutions for the optimization problem based on the system model. The CPS is considered as a leader seeking to minimize its total cost of buying energy from the ECs, and the ECs are the followers who decide on how much energy they will sell to the CPS for maximizing their utilities. It is shown that the game, which can be implemented distributedly, possesses a socially optimal solution, in which the sum of the benefits to all consumers is maximized, as the total cost to the CPS is minimized. Numerical analysis confirms the effectiveness of the game. © 2010-2012 IEEE

PMU Placement Optimization for Efficient State Estimation in Smart Grid

© 1983-2012 IEEE. This paper investigates phasor measurement unit (PMU) placement for informative state estimation in smart grid by incorporating various constraints for observability. Observability constitutes an important property for PMU placement to characterize the depth of the buses' reachability by the placed PMUs, but addressing it solely by binary linear programming as in many works still does not guarantee a good estimate for the grid state. Some existing works have considered optimization of some estimation indices by ignoring the observability requirements for computational ease and thus potentially lead to trivial results such as acceptance of the estimate for an unobserved state component as its unconditional mean. In this work, the PMU placement optimization problem is considered by minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state subject to observability constraints, which incorporate operating conditions such as presence of zero injection buses, contingency of measurement loss, and limitation of communication channels per PMU. The proposed design is thus free from the fundamental shortcomings in the existing PMU placement designs. The problems are posed as large scale binary nonlinear optimization problems involving thousands of binary variables, for which this paper develops efficient algorithms for computational solutions. Their performance is analyzed in detail through numerical examples on large scale IEEE power networks. The solution method is also shown to be extendable to AC power flow models, which are formulated by nonlinear equations