Intervention in Power Control Games With Selfish Users

Yuanzhang Xiao,Jaeok Park,van der Schaar, Mihaela IEEE 2012 IEEE journal of selected topics in signal processi Vol.6 No.2

<P>We study the power control problem in single-hop wireless ad hoc networks with selfish users. Without incentive schemes, selfish users tend to transmit at their maximum power levels, causing excessive interference to each other. In this paper, we study a class of incentive schemes based on intervention to induce selfish users to transmit at desired power levels. In a power control scenario, an intervention scheme can be implemented by introducing an intervention device that can monitor the power levels of users and then transmit power to cause interference to users if necessary. Focusing on first-order intervention rules based on individual transmit powers, we derive conditions on the intervention rates and the power budget to achieve a desired outcome as a (unique) Nash equilibrium with intervention and propose a dynamic adjustment process to guide users and the intervention device to the desired outcome. We also analyze the effect of using aggregate receive power instead of individual transmit powers. Our results show that intervention schemes can be designed to achieve any positive power profile while using interference from the intervention device only as a threat. Lastly, simulation results are presented to illustrate the performance improvement from using intervention schemes and the theoretical results.</P>

Repeated Games with Intervention: Theory and Applications in Communications

Yuanzhang Xiao,Jaeok Park,van der Schaar, M. IEEE 2012 IEEE TRANSACTIONS ON COMMUNICATIONS Vol.60 No.10

<P>In communication systems where users share common resources, selfish behavior usually results in suboptimal resource utilization. There have been extensive works that model communication systems with selfish users as one-shot games and propose incentive schemes to achieve Pareto-optimal outcomes. However, in many communication systems, due to strong negative externalities among users, the sets of feasible payoffs in one-shot games are nonconvex. Thus, it is possible to expand the set of feasible payoffs by having users choose different action profiles in an alternating manner. In this paper, we formulate a model of repeated games with intervention. First, by using repeated games we can convexify the set of feasible payoffs in one-shot games. Second, by using intervention in repeated games we can achieve a larger set of equilibrium payoffs and loosen requirements for users' patience to achieve a target payoff. We study the problem of maximizing a welfare function defined on users' payoffs. We characterize the limit set of equilibrium payoffs. Given the optimal equilibrium payoff, we derive the sufficient condition on the discount factor and the intervention capability to achieve it, and design corresponding equilibrium strategies. We illustrate our analytical results with power control and flow control.</P>

Bidirectional Energy Trading and Residential Load Scheduling with Electric Vehicles in the Smart Grid

Byung-Gook Kim,Shaolei Ren,van der Schaar, M.,Jang-Won Lee IEEE 2013 IEEE journal on selected areas in communications Vol.31 No.7

<P>Electric vehicles (EVs) will play an important role in the future smart grid because of their capabilities of storing electrical energy in their batteries during off-peak hours and supplying the stored energy to the power grid during peak hours. In this paper, we consider a power system with an aggregator and multiple customers with EVs and propose novel electricity load scheduling algorithms which, unlike previous works, jointly consider the load scheduling for appliances and the energy trading using EVs. Specifically, we allow customers to determine how much energy to purchase from or to sell to the aggregator while taking into consideration the load demands of their residential appliances and the associated electricity bill. We propose two different approaches: a collaborative and a non-collaborative approach. In the collaborative approach, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare of the power system. In the non-collaborative approach, we model the energy scheduling problem as a non-cooperative game among self-interested customers, where each customer determines its own load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers in the non-collaborative approach, we propose a tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. In both approaches, we also consider the uncertainty in the load demands, with which customers' actual energy consumption may vary from the scheduled energy consumption. To study the impact of the uncertainty, we use the worst-case-uncertainty approach and develop distributed load scheduling algorithms that provide the guaranteed minimum performances in uncertain environments. Subsequently, we show when energy trading leads to an increase in the social welfare and we determine what are the customers' incentives to participate in the energy trading in various usage scenarios including practical environments with uncertain load demands.</P>

Online Learning in BitTorrent Systems

Izhak-Ratzin, R.,Hyunggon Park,van der Schaar, M. IEEE 2012 IEEE transactions on parallel and distributed syst Vol.23 No.12

<P>We propose a BitTorrent-like protocol based on an online learning (reinforcement learning) mechanism, which can replace the peer selection mechanisms in the regular BitTorrent protocol. We model the peers' interactions in the BitTorrent-like network as a repeated stochastic game, where the strategic behaviors of the peers are explicitly considered. A peer that applies the reinforcement learning (RL)-based mechanism uses the observations on the associated peers' statistical reciprocal behaviors to determine its best responses and estimate the corresponding impact on its expected utility. The policy determines the peer's resource reciprocations such that the peer can maximize its long-term performance. We have implemented the proposed mechanism and incorporated it into an existing BitTorrent client. Our experiments performed on a controlled Planetlab testbed confirm that the proposed protocol 1) promotes fairness and provides incentives to contributed resources, i.e., high capacity peers improve their download completion time by up to 33 percent, 2) improves the system stability and robustness, i.e., reduces the peer selection fluctuations by 57 percent, and (3) discourages free-riding, i.e., peers reduce their uploads to free-riders by 64 percent as compared to the regular BitTorrent protocol.</P>

Adaptive Contextual Learning for Unit Commitment in Microgrids With Renewable Energy Sources

Lee, Hyun-Suk,Tekin, Cem,van der Schaar, Mihaela,Lee, Jang-Won IEEE 2018 IEEE journal of selected topics in signal processi Vol.12 No.4

<P>In this paper, we study a unit commitment (UC) problem where the goal is to minimize the operating costs of a microgrid that involves renewable energy sources. Since traditional UC algorithms use <I>a priori</I> information about uncertainties such as the load demand and the renewable power outputs, their performances highly depend on the accuracy of the <I>a priori</I> information, especially in microgrids due to their limited scale and size. This makes the algorithms impractical in settings where the past data are not sufficient to construct an accurate prior of the uncertainties. To resolve this issue, we develop an adaptively partitioned contextual learning algorithm for UC (AP-CLUC) that learns the best UC schedule and minimizes the total cost over time in an online manner without requiring any <I>a priori</I> information. AP-CLUC effectively learns the effects of the uncertainties on the cost by adaptively considering context information strongly correlated with the uncertainties, such as the past load demand and weather conditions. For AP-CLUC, we first prove an analytical bound on the performance, which shows that its average total cost converges to that of the optimal policy with perfect <I> a priori</I> information. Then, we show via simulations that AP-CLUC achieves competitive performance with respect to the traditional UC algorithms with perfect <I>a priori</I> information, and it achieves better performance than them even with small errors on the information. These results demonstrate the effectiveness of utilizing the context information and the adaptive management of the past data for the UC problem.</P>

Near-Optimal Deviation-Proof Medium Access Control Designs in Wireless Networks

Khoa Tran Phan,Jaeok Park,van der Schaar, M. IEEE 2012 IEEE/ACM transactions on networking Vol.20 No.5

<P>Distributed medium access control (MAC) protocols are essential for the proliferation of low-cost, decentralized wireless local area networks (WLANs). Most MAC protocols are designed with the presumption that nodes comply with prescribed rules. However, selfish nodes have natural motives to manipulate protocols in order to improve their own performance. This often degrades the performance of other nodes as well as that of the overall system. In this paper, we propose a class of protocols that limit the performance gain from selfish manipulation while incurring only a small efficiency loss. The proposed protocols are based on the idea of a review strategy, with which nodes collect signals about the actions of other nodes over a period of time, use a statistical test to infer whether or not other nodes are following the prescribed behavior, and trigger a punishment if a deviation is inferred. We consider the cases of private and public signals and provide analytical and numerical results to demonstrate the properties of the proposed protocols.</P>

Uncovering oxysterol-binding protein (OSBP) as a target of the anti-enteroviral compound TTP-8307

Albulescu, Lucian,Bigay, Joë,lle,Biswas, Bishyajit,Weber-Boyvat, Marion,Dorobantu, Cristina M.,Delang, Leen,van der Schaar, Hilde M.,Jung, Young-Sik,Neyts, Johan,Olkkonen, Vesa M.,van Kuppeveld, F Elsevier 2017 ANTIVIRAL RESEARCH Vol.140 No.-

<P>The genus Enterovirus (e.g. poliovirus, coxsackievirus, rhinovirus) of the Picornaviridae family of positive strand RNA viruses includes many important pathogens linked to a range of acute and chronic diseases for which no approved antiviral therapy is available. Targeting a step in the life cycle that is highly conserved provides an attractive strategy for developing broad-range inhibitors of enterovirus infection. A step that is currently explored as a target for the development of antivirals is the formation of replication organelles, which support replication of the viral genome. To build replication organelles, enteroviruses rewire cellular machinery and hijack lipid homeostasis pathways. For example, enteroviruses exploit the PI4KIIII beta-PI4P-OSBP pathway to direct cholesterol to replication organelles. Here, we uncover that TTP-8307, a known enterovirus replication inhibitor, acts through the PI4KIIII-PI4P-OSBP pathway by directly inhibiting OSBP activity. However, despite a shared mechanism of 1TP-8307 with established OSBP inhibitors (itraconazole and OSW-1), we identify a number of notable differences between these compounds. The antiviral activity of TTP-8307 extends to other viruses that require OSBP, namely the picornavirus encephalomyocarditis virus and the flavivirus hepatitis C virus. (C) 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license</P>