Electrical and Electronic Engineering - Research Publications
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ItemA framework for optimization under limited information(SPRINGER, 2013-03)
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ItemAn Information Analysis of Iterative Algorithms for Network Utility Maximization and Strategic Games(IEEE, 2019)A variety of resource allocation problems on networked systems, for example, those in cyber-physical systems or Internet-of-things applications, require distributed solution methods. Modern distributed algorithms usually require bandwidth-limited digital communication between the system and its users, who are often modeled as independent decision makers with individual preferences. This paper presents a quantitative information flow and knowledge gain analysis of decentralized iterative algorithms with bounded trajectories in the context of convex network utility maximization problems and strategic games with a unique Nash equilibrium solution. First, a novel generic framework is introduced to quantify knowledge gain in network resource allocation problems using entropy by taking into account priors in the solution space. Second, a general result is presented on the interplay between quantization of information and distributed algorithm performance both for linear and sublinear convergence. Third, information flow in distributed algorithms is studied and a lower bound is derived on the total amount of information exchanged for convergence under uniform quantization. The well-known primal-dual decomposition algorithm is used as an example to illustrate the results. Finally, convergence guarantees for distributed algorithms with estimation are investigated. This paper establishes specific links between information concepts and iterative algorithms in addition to building a foundation for integrating learning schemes into distributed optimization.
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ItemAdversarial Behavior in Network Games(SPRINGER BIRKHAUSER, 2015-03-01)This paper studies the effects of and countermeasures against adversarial behavior in network resource allocation mechanisms such as auctions and pricing schemes. It models the heterogeneous behavior of users, which ranges from altruistic to selfish and to malicious, within the analytical framework of game theory. A mechanism design approach is adopted to quantify the effect of adversarial behavior, which ranges from extreme selfishness to destructive maliciousness. First, the well-known result on the Vicrey–Clarke–Groves (VCG) mechanism losing its efficiency property in the presence of malicious users is extended to the case of divisible resource allocation to motivate the need to quantify the effect of malicious behavior. Then, the Price of Malice of the VCG mechanism and of some other network mechanisms are derived. In this context, the dynamics and convergence properties of an iterative distributed pricing algorithm are analyzed. The resistance of a mechanism to collusions is investigated next, and the effect of collusion of some malicious users is quantified. Subsequently, the assumption that the malicious user has information about the utility function of selfish users is relaxed, and a regression-based iterative learning scheme is presented and applied to both pricing and auction mechanisms. Differentiated pricing as a method to counter adversarial behaviors is proposed and briefly discussed. The results obtained are illustrated with numerical examples and simulations.
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ItemPerformance Analysis of Gradient-Based Nash Seeking Algorithms Under Quantization(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016-12-01)This paper investigates the impact of quantized inter-agent communications on the asymptotic and transient behavior of gradient-based Nash-seeking algorithms in non-cooperative games. Using the information-theoretic notion of entropy power, we establish a universal lower bound on the asymptotic rate of exponential mean-square convergence to the Nash equilibrium (NE). This bound depends on the inter-agent data rate and the local behavior of the agents' utility functions, and is independent of the quantizer structure. Next, we study transient performance and derive an upper bound on the average time required to settle inside a specified ball around the NE, under uniform quantization. Furthermore, we establish an upper bound on the probability that agents' actions lie outside this ball, and show that this bound decays double-exponentially with time. Finally, we propose an adaptive quantization scheme that allows the gradient algorithm to converge to the NE despite quantized inter-agent communications.
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ItemTransition to sustainable energy generation in Australia: Interplay between coal, gas and renewables(Pergamon Press, 2019-08-01)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.
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ItemTruthful Mechanism Design for Wireless Powered Network With Channel Gain Reporting(Institute of Electrical and Electronics Engineers (IEEE), 2019-11-01)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.
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ItemSupport vector machines resilient against training data integrity attacks(Elsevier BV, 2019-12-01)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.
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ItemDistributed Real-Time IoT for Autonomous Vehicles(Institute of Electrical and Electronics Engineers, 2019-02-01)Real-time Internet of Things (IoT) applications have stringent delay requirements when implemented over distributed sensing and communication networks in smart traffic control. They require the system to reach a permissible neighbourhood of an optimum solution with a tolerable delay. The performance of such applications mostly depends on the delay introduced by the underlying optimization algorithms, with the localized computational capability. In this paper, we study a smart traffic control scenario-a real-time IoT application, where a group of autonomous vehicles independently decide on their lane velocities, in collaboration with road-side units to efficiently utilize intersections with minimal environmental impact. We decompose this problem as an unconstrained network utility maximization problem. A consensus-based, constant step-size gradient descent algorithm is proposed to obtain a near-optimal solution. We analyze the delay-accuracy tradeoff in reaching a near-optimal velocity. Delay is measured in terms of the number of iterations required before the scheduling operation can be done for a particular tolerance. The operation of the algorithm under quantized message passing is also studied. On contrary to the existing methods to intersection management problems, our approach studies the limit at which an optimization algorithm fails to cater for the requirements of a real-time application and must fall back for a pareto-optimal solution, due to the communication constraints. We used simulation of urban mobility to incorporate the microscopic behavior of traffic flows to our simulations and compared our solution with traditional and state-of-the-art intersection management techniques.
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ItemAn Information-Based Learning Approach to Dual Control(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2015-11)Dual control aims to concurrently learn and control an unknown system. However, actively learning the system conflicts directly with any given control objective for it will disturb the system during exploration. This paper presents a receding horizon approach to dual control, where a multiobjective optimization problem is solved repeatedly and subject to constraints representing system dynamics. Balancing a standard finite-horizon control objective, a knowledge gain objective is defined to explicitly quantify the information acquired when learning the system dynamics. Measures from information theory, such as entropy-based uncertainty, Fisher information, and relative entropy, are studied and used to quantify the knowledge gained as a result of the control actions. The resulting iterative framework is applied to Markov decision processes and discrete-time nonlinear systems. Thus, the broad applicability and usefulness of the presented approach is demonstrated in diverse problem settings. The framework is illustrated with multiple numerical examples.