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Directional wireless power transfer (WPT) technology provides a promising energy solution to remotely recharge the Internet of things sensors using directional antennas. Under a harvest-then-transmit protocol, the access point can adaptively allocate the transmit power among multiple energy directions to maximize the social welfare of the sensors, i.e., downlink sum received energy or uplink sum rate, based on full or quantized channel gains reported from the sensors. However, such power allocation can be challenged if each sensor belongs to a different agent and works in a competitive way. In order to maximize their own utilities, the sensors have the incentives to falsely report their channel gains, which unfortunately reduces the social welfare. To tackle this problem, we design the strategy-proof mechanisms to ensure that each sensor’s dominant strategy is to truthfully reveal its channel gain regardless of other sensors’ strategies. Under the benchmark full channel gain reporting (CGR) scheme, we adopt the Vickrey-Clarke-Groves (VCG) mechanism to derive the price functions for both downlink and uplink, where the truthfulness is guaranteed by asking each sensor to pay the social welfare loss of all other sensors attributable to its presence. For the 1-bit CGR scheme, the problem is more challenging due to the severe information asymmetry, where each sensor has true valuation of full channel gain but may report the false information of quantized channel gain. We prove that the classic VCG mechanism is no longer truthful and then propose two threshold-based price functions for both downlink and uplink, where the truthfulness is ensured by letting each sensor pay its own achievable utility improvement due to its participation. The numerical results validate the truthfulness of the proposed mechanism designs. [ABSTRACT FROM AUTHOR]

Abstract This paper analyzes the ongoing transition to sustainable energy in Australia, moving from traditional large-scale plants to distributed renewable generation by studying the time series of coal and gas consumption as well as onshore wind and solar. Even though most of energy generation, especially in the form of electricity is currently being generated from coal and gas, a quantitative assessment of their evolution is necessary to understand whether, and to which extent, renewables are competing in the marketplace with conventional production means. A well-accepted innovation diffusion model is used to capture and interpret the underlying dynamics of the competitive transition in generation. The results show that renewables are exerting a competitive pressure on coal and collaborate with gas towards the transition. The view that gas should play a key role in transition is confirmed by our findings, because it is found to have a competitive role towards coal, while aiding the uptake of renewables. Highlights • Analysis of the ongoing transition to sustainable energy in Australia. • Study to understand whether renewables are competing in the marketplace. • A diffusion model used to capture the dynamics of the competitive transition. • Renewables exert a competitive pressure on coal and cooperate with gas. • Gas plays a competitive role towards coal, and aids the uptake of renewables. [ABSTRACT FROM AUTHOR]

This paper presents a multi-nodal intertemporal Cournot gaming model with transmission constraints and uses it to simulate energy-only and capacity-energy market designs in the presence of uncertainties stemming from intermittent renewable power generation. Both market paradigms are compared to a competitive benchmark in order to determine which one performs better in a concentrated market with significant penetration of wind and solar generation. As a specific example, the model is applied to the South Australian zone of the Australian National Electricity Market (NEM). The simulation results for the time interval 2013–2030 show that the capacity-energy market has the potential to induce significant new capacity and push prices much closer to the competitive level in contrast to the current energy-only market design. [ABSTRACT FROM AUTHOR]

We apply computational Game Theory to a unification of physics-based models that represent decision-making across a number of agents within both cooperative and competitive processes. Here the competitors try to both positively influence their own returns, while negatively affecting those of their competitors. Modelling these interactions with the so-called Boyd–Kuramoto–Lanchester (BKL) complex dynamical system model yields results that can be applied to business, gaming and security contexts. This paper studies a class of decision problems on the BKL model, where a large set of coupled, switching dynamical systems are analysed using game-theoretic methods. Due to their size, the computational cost of solving these BKL games becomes the dominant factor in the solution process. To resolve this, we introduce a novel Nash Dominant solver, which is both numerically efficient and exact. The performance of this new solution technique is compared to traditional exact solvers, which traverse the entire game tree, as well as to approximate solvers such as Myopic and Monte Carlo Tree Search (MCTS). These techniques are assessed, and used to gain insights into both nonlinear dynamical systems and strategic decision making in adversarial environments. • Demonstrating how merging dynamical systems and game theory produces complex models. • Developing solvers that significantly reduce the cost of solving such models. • Using these switched dynamical systems to investigate adversarial decision making. [ABSTRACT FROM AUTHOR]

The power grid is a critical infrastructure that must be protected against potential threats. While modern technologies at the center of the ongoing smart grid evolution increase its operational efficiency, they also make it more susceptible to malicious attacks such as false data injection to electronic monitoring systems. This paper presents a game-theoretic approach to smart grid security by combining quantitative risk management techniques with decision making on protective measures. The consequences of data injection attacks are quantified using a risk assessment process where the well-known conditional value-at-risk (CVaR) measure provides an estimate of the defender's loss due to load shed in simulated scenarios. The calculated risks are then incorporated into a stochastic security game model as input parameters. The decisions on defensive measures are obtained by solving the game using dynamic programming techniques which take into account resource constraints. Thus, the formulated security game provides an analytical framework for choosing the best response strategies against attackers and minimizing potential risks. The theoretical results obtained are demonstrated through numerical examples. Simulation results show that different risk measures lead to different defense strategies, but the CVaR measure prioritizes high-loss tail events. [ABSTRACT FROM PUBLISHER]

The increasing uptake of electric vehicles suggests that vehicle charging will have a significant impact on the electricity grid. Finding ways to shift this charging to off-peak periods has been recognized as a key challenge for integration of electric vehicles into the electricity grid on a large scale. In this paper, electric vehicle charging is formulated as a receding horizon optimization problem that takes into account the present and anticipated constraints of the distribution network over a finite charging horizon. The constraint set includes transformer and line limitations, phase unbalance, and voltage stability within the network. By using a linear approximation of voltage drop within the network, the problem solution may be computed repeatedly in near real time, and thereby take into account the dynamic nature of changing demand and vehicle arrival and departure. It is shown that this linear approximation of the network constraints is quick to compute, while still ensuring that network constraints are respected. The approach is demonstrated on a validated model of a real network via simulations that use real vehicle travel profiles and real demand data. Using the optimal charging method, high percentages of vehicle uptake can be sustained in existing networks without requiring any further network upgrades, leading to more efficient use of existing assets and savings for the consumer. [ABSTRACT FROM PUBLISHER]

Several least-cost planning studies have suggested a significant shift away from coal toward gas, renewables and potentially nuclear in the long term. Using a Cournot gaming model to analyze the ramifications that market power in Australia's concentrated market may hold for a transition to a low-carbon electricity sector suggests that the substantial influence on price outcomes of some players will need to be fixed before we can have faith in the least-cost carbon abatement strategy. [ABSTRACT FROM AUTHOR]

Wind generation variability in an energy-only market such Australian National Electricity Market (NEM) can create significant revenue uncertainties for incumbent generators and substantially increase price risks faced by retailers. This paper presents a Cournot game model to formally analyze how high volatility of wind generation in a concentrated energy-only market can raise the peak/shoulder period (of typically low wind generation) prices to offset the foregone revenue during off-peak periods (of high wind generation). A Monte Carlo simulation around a Cournot game is formulated as an inter-temporal nonlinear optimization problem to assess these issues. The model is implemented for the South Australian zone of the Australian NEM that has experienced high growth in wind generation in recent years. The model results support some of the observed spot pricing behavior in the region in recent years. These findings have significant ramifications for the efficacy of the energy-only market in scenarios with high penetration of intermittent generation. [ABSTRACT FROM PUBLISHER]

This paper studies a constrained optical signal-to-noise ratio (OSNR) optimization problem in optical networks from the perspective of system performance. A system optimization problem is formulated with the objective of achieving an OSNR target for each channel while satisfying the total power constraint. In order to establish existence of a unique optimal solution, the conditions are derived, which can be used as a basis for an admission control scheme. The original problem is then converted to a relaxed system problem by using a barrier function and solved by a distributed iterative algorithm. Next, the system optimization framework developed is compared to the game theoretic one in [1]. The effects of parameters in both formulations are investigated to study efficiency of Nash equilibria in the OSNR game and pricing mechanisms affecting overall system performance. The theoretical analysis is supported by numerical simulations and experiments conducted on an optical fiber link. [ABSTRACT FROM AUTHOR]

We consider a scenario where devices with multiple networking capabilities access networks with heterogeneous characteristics. In such a setting, we address the problem of efficient utilization of multiple access networks by devices via optimal assignment of traffic flows with given utilities to different networks. We develop and analyze a device middleware functionality that monitors network characteristics and employs a Markov Decision Process (MDP) based control scheme that in conjunction with stochastic characterization of the available bit rate and delay of the networks generates an optimal policy for allocation of flows to different networks. The optimal policy maximizes, under available bit rate and delay constraints on the access networks, a discounted reward which is a function of the flow utilities. The flow assignment policy is periodically updated and is consulted by the flows to dynamically perform network selection during their lifetimes. We perform measurement tests to collect traces of available bit rate and delay characteristics on Ethernet and WLAN networks on a work day in a corporate work environment. We implement our flow assignment framework in ns-2 and simulate the system performance for a set of elastic video-like flows using the collected traces. We demonstrate that the MDP based flow assignment policy leads to significant enhancement in the QoS provisioning (higher rate allocation, lower packet delays and packet loss rates) for the flows and better access network utilization, as compared to policies that allocate flows to different networks using greedy approaches or heuristics like average available bit rate on the networks. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the optimization of malicious software removal or patch deployment processes across multiple networks. The well-known classical epidemic model is adapted to model malware propagation in this multi-network framework. The trade-off between the infection spread and the patching costs is captured in a cost function, leading to an optimal control problem. In the single network case the optimal feedback controller is found by solving an associated Hamilton–Jacobi–Bellman equation. This control law is numerically compared to the proportional response strategy typically assumed by the epidemic model. In the higher dimensional multiple-networks case, the system is linearized to derive feedback controllers using pole-placement, linear quadratic regulator (LQR) optimal control, and optimal control, where the measurement errors in the number of infected clients are explicitly modeled. The resulting patching strategies are analyzed numerically and their results are compared. [Copyright &y& Elsevier]

Abstract: We present a non-equilibrium analysis and control approach for the Active Queue Management (AQM) problem in communication networks. Using simplified fluid models, we carry out a bifurcation study of the complex dynamic queue behavior to show that non-equilibrium methods are essential for analysis and optimization in the AQM problem. We investigate an ergodic theoretic framework for stochastic modeling of the non-equilibrium behavior in deterministic models and use it to identify parameters of a fluid model from packet level simulations. For computational tractability, we use set-oriented numerical methods to construct finite-dimensional Markov models, including control Markov chains and hidden Markov models. Subsequently, we develop and analyze an example AQM algorithm using a Markov Decision Process (MDP) based control framework. The control scheme developed is optimal with respect to a reward function, defined over the queue size and aggregate flow rate. We implement and simulate our illustrative AQM algorithm in the ns-2 network simulator. The results obtained confirm the theoretical analysis and exhibit promising performance when compared with well-known alternative schemes under persistent non-equilibrium queue behavior. [Copyright &y& Elsevier]

This paper initiates a study toward developing and applying randomized algorithms for stability of high-speed communication networks. The focus is on congestion and delay-based flow controllers for sources, which are "utility maximizers" for individual users. First, we introduce a nonlinear algorithm for such source flow controllers, which uses as feedback aggregate congestion and delay information from bottleneck nodes of the network, and depends on a number of parameters, among which are link capacities, user preference for utility, and pricing. We then linearize this nonlinear model around its unique equilibrium point and perform a robustness analysis for a special symmetric case with a single bottleneck node. The "symmetry" here captures the scenario when certain utility and pricing parameters are the same across all active users, for which we derive closed-form necessary and sufficient conditions for stability and robustness under parameter variations. In addition, the ranges of values for the utility and pricing parameters for which stability is guaranteed are computed exactly. These results also admit counterparts for the case when the pricing parameters vary across users, but the utility parameter values are still the same. In the general nonsymmetric case, when closed-form derivation is not possible, we construct specific randomized algorithms which provide a probabilistic estimate of the local stability of the network. In particular, we use Monte Carlo as well as quasi-Monte Carlo techniques for the linearized model. The results obtained provide a complete analysis of congestion con- trol algorithms for internet style networks with a single bottleneck node as well as for networks with general random topologies. [ABSTRACT FROM AUTHOR]

We analyze the stability properties of an end-to-end congestion control scheme under fixed heterogeneous delays, and for general network topologies. The scheme analyzed is based on the congestion control game of [1], with the starting point being the unique Nash equilibrium of that game. We prove global stability of this solution (and hence of the congestion control algorithm) under a mild symmetricity condition. We further demonstrate the stability of the algorithm numerically for various delays, user numbers, and topologies. [ABSTRACT FROM AUTHOR]

Incentive schemes and policies play an important role in reducing carbon emissions from electricity generation. This paper investigates designing tax and subsidy incentives towards a reliable and low emission electricity market, using Australia's National Electricity Market as a case study. In this work, a novel framework is proposed to design interactive tax/subsidy incentives on both emission reduction and resource adequacy in competitive electricity markets as a game model. In our model, market participants decide on their capacity expansion/retirement strategies considering the impact of designed incentive schemes on their long-term operation such that the desired levels of emission reduction and fast response generation are achieved in the network. The simulation results for Australia's electricity market during 2017–2052, indicate the necessity of incentive policies, in spite of the cost reduction trajectory for renewable technologies, to reach the emission intensity reduction above 45% in the market by 2052. In 80% emission intensity reduction scenario, the designed incentive schemes highly encourage the investment on synchronous renewables, +17 GW, storage technologies, +15.7 GW, and transmission lines, +1.6 GW, to support high additional penetration of Variable Renewable Energy, wind and solar, +39 GW, which paves the way to transition to a green and reliable electricity market. • Our model designs emission abatement and reliability schemes in electricity markets. • The schemes are designed considering the strategic behaviour of market players. • The designed emission policies must be dynamic due to outage of aging generators. • Reliability schemes are taken into account to enable high penetration of renewables. • Emission abatement schemes might lower the average prices at some years. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) have seen great research interest in recent years, due to both their ability to represent structure in data and generate novel samples. Anomaly detection, which discerns novel samples or patterns, is a well-known problem that can be studied using GANs with a fresh perspective, especially in novel application domains such as wireless communication networks. For these models to achieve an accurate representation of the underlying data distribution, significant volumes of data are required. If this data source is not centralised (e.g. stored at multiple hosts or data centres), non-standard training methods are required to achieve comparable performance to the centralised case. This paper presents the key collaborative training methods that have emerged in recent years that draw on the GAN's modular structure to achieve high performance while balancing computation, storage, and communication requirements and demonstrates their application to the task of anomaly detection using cognitive radios. [ABSTRACT FROM AUTHOR]

This paper studies the complexity of solving two classes of non-cooperative games in a distributed manner, in which the players communicate with a set of system nodes over noisy communication channels. The complexity of solving each game class is defined as the minimum number of iterations required to find a Nash equilibrium (NE) of any game in that class with $\epsilon $ accuracy. First, we consider the class $\mathcal {G}$ of all $N$ -player non-cooperative games with a continuous action space that admit at least one NE. Using information-theoretic inequalities, a lower bound on the complexity of solving $\mathcal {G}$ is derived which depends on the Kolmogorov $2\epsilon $ -capacity of the constraint set and the total capacity of the communication channels. Our results indicate that the game class $\mathcal {G}$ can be solved at most exponentially fast. We next consider the class of all $N$ -player non-cooperative games with at least one NE such that the players’ utility functions satisfy a certain (differential) constraint. We derive lower bounds on the complexity of solving this game class under both Gaussian and non-Gaussian noise models. Finally, we derive upper and lower bounds on the sample complexity of a class of quadratic games. It is shown that the complexity of solving this game class scales according to $\Theta \left ({\frac {1}{\epsilon ^{2}}}\right)$ where $\epsilon $ is the accuracy parameter. [ABSTRACT FROM AUTHOR]

Stability and power sharing properties of droop controlled inverter-based microgrid systems depend on various design factors. Little explored is the effect of component mismatches and parameters drifts on the stability, steady state behaviour and power sharing properties of these systems. In this paper, the behaviour of frequency droop controlled inverter based microgrid systems in the presence of non-identical clocks is analysed. It is shown that power sharing between converters in a microgrid can be sensitive to clock mismatches. Our proposal shows that a coordination control that uses sparse inter-node communications is useful in ensuring desired active power sharing. Conditions are derived to ensure stability in the presence of the proposed controller and simulation results are presented. [ABSTRACT FROM AUTHOR]

Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms, e.g., pump-storage hydro, large-scale, or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping, and price arbitrage capabilities. In this paper, we propose a stochastic bilevel optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile energy-only electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic/regulated generation, storage, and transmission players is modeled in the lower level problem using an extended stochastic (Bayesian) Cournot-based game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, and a 30-bus IEEE system are considered as the case studies. Our numerical results indicate that 50% price volatility reduction in the SA electricity market can be achieved by installing either 430-MWh regulated storage or 530-MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms. [ABSTRACT FROM PUBLISHER]

Component mismatches and parameters drifts drastically affect stability and long-term operation of droop-controlled inverter-based microgrids. This paper analyzes and illustrates the impact of design variations and parameter drifts between angle droop controlled inverter-interfaced sources in a microgrid. It is shown that microgrid stability is very sensitive to parameter drifts, especially in frequency. A coordination control scheme that uses internode communications is proposed for improving the stability margin and ensuring the desired power sharing. Conditions for stability are derived and simulation results are presented to validate the performance of the proposal. [ABSTRACT FROM PUBLISHER]

This survey provides a structured and comprehensive overview of research on security and privacy in computer and communication networks that use game-theoretic approaches. We present a selected set of works to highlight the application of game theory in addressing different forms of security and privacy problems in computer networks and mobile applications. We organize the presented works in six main categories: security of the physical and MAC layers, security of self-organizing networks, intrusion detection systems, anonymity and privacy, economics of network security, and cryptography. In each category, we identify security problems, players, and game models. We summarize the main results of selected works, such as equilibrium analysis and security mechanism designs. In addition, we provide a discussion on the advantages, drawbacks, and future direction of using game theory in this field. In this survey, our goal is to instill in the reader an enhanced understanding of different research approaches in applying game-theoretic methods to network security. This survey can also help researchers from various fields develop game-theoretic solutions to current and emerging security problems in computer networking. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of having convex or concave formulations of optimization problems for interference coupled wireless systems. An axiomatic framework for interference functions proposed by Yates in 1995 is used to model interference coupling in our paper. The paper shows that under certain natural assumptions, the exponential transformation is the unique transformation (up to a positive constant) for “convexification” of resource allocation problems for linear interference functions. Furthermore, it is shown that under certain intuitive assumptions, it is sufficient to check for the joint concavity (convexity) of sum of weighted functions of SINR (inverse SINR) with respect to s (p=e^s, where p is the power vector of the users), if we would like the resulting resource allocation problem to be concave (convex). This paper characterizes the largest class of utility functions and the largest class of interference functions (respectively), which allow a convex and concave formulation of a problem for interference coupled wireless systems. It extends previous literature on log-convex interference functions and provides boundaries on the class of problems in wireless systems, which can be algorithmically tackled by convex optimization techniques. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we investigate stability, bifurcation and oscillations arising in a single-link communication network model with a large number of heterogeneous users adopting a Transmission Control Protocol (TCP)-like rate control scheme with an Active Queue Management (AQM) router. In the system considered, different user delays are known and fixed but taken from a given distribution. It is shown that for any given distribution of delays, there exists a critical amount of feedback (due to AQM) at which the equilibrium loses stability and a limit cycling solution develops via a Hopf bifurcation. The nature (criticality) of the bifurcation is investigated with the aid of Lyapunov–Schmidt perturbation method. The results of the analysis are numerically verified and provide valuable insights into dynamics of the AQM control system. [Copyright &y& Elsevier]

• Support Vector Machines are designed to withstand noise in data. • But they are vulnerable to integrity attacks by adversaries. • Projecting data to lower dimensional spaces in specific directions may reduce the adversary's effects. • Game theory can be used to predict the adversary's actions and take proactive precautions. Support Vector Machines (SVMs) are vulnerable to integrity attacks, where malicious attackers distort the training data in order to compromise the decision boundary of the learned model. With increasing real-world applications of SVMs, malicious data that is classified as innocuous may have harmful consequences. This paper presents a novel framework that utilizes adversarial learning, nonlinear data projections, and game theory to improve the resilience of SVMs against such training-data-integrity attacks. The proposed approach introduces a layer of uncertainty through the use of random projections on top of the learners, making it challenging for the adversary to guess the specific configurations of the learners. To find appropriate projection directions, we introduce novel indices that ensure the contraction of the data and maximize the detection accuracy. Experiments with benchmark data sets show increases in detection rates up to 13.5% for OCSVMs and up to 14.1% for binary SVMs under different attack algorithms when compared with the respective base algorithms. [ABSTRACT FROM AUTHOR]

In the classical electricity grid power demand is nearly instantaneously matched by power supply. In this paradigm, the changes in power demand in a low voltage distribution grid are essentially nothing but a disturbance that is compensated for by control at the generators. The disadvantage of this methodology is that it necessarily leads to a transmission and distribution network that must cater for peak demand. So-called smart meters and smart grid technologies provide an opportunity to change this paradigm by using demand side energy storage to moderate instantaneous power demand so as to facilitate the supply-demand match within network limitations. A receding horizon model predictive control method can be used to implement this idea. In this paradigm demand is matched with supply, such that the required customer energy needs are met but power demand is moderated, while ensuring that power flow in the grid is maintained within the safe operating region, and in particular peak demand is limited. This enables a much higher utilisation of the available grid infrastructure, as it reduces the peak-to-base demand ratio as compared to the classical control methodology of power supply following power demand. This paper investigates this approach for matching energy demand to generation in the last mile of the power grid while maintaining all network constraints through a number of case studies involving the charging of electric vehicles in a typical suburban low voltage distribution network in Melbourne, Australia. [ABSTRACT FROM AUTHOR]

Constraints on outputs or measured states arise in many practical engineering problems, yet are not readily handled by conventional extremum seeking techniques that require the output or states to be mapped to a corresponding input region. In this work, an alternative approach utilising non-linear programming techniques along with measurements of the constraint functions is proposed to enable a constrained extremum to be sought directly. Under reasonable assumptions and tuning, the proposed approach is shown to provide semi-global practical asymptotic stability guarantees for a class of MIMO plants. Simulation results are provided to demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]