The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform
Shankar Sastry - One of the best experts on this subject based on the ideXlab platform.
-
competitive statistical estimation with strategic Data sources
IEEE Transactions on Automatic Control, 2020Co-Authors: Tyler Westenbroek, Roy Dong, Lillian J Ratliff, Shankar SastryAbstract:In recent years, Data have played an increasingly important role in the economy as a good in its own right. In many settings, Data Aggregators cannot directly verify the quality of the Data they purchase, nor the effort exerted by Data sources when creating the Data. Recent work has explored mechanisms to ensure that the Data sources share high-quality Data with a single Data Aggregator, addressing the issue of moral hazard. Oftentimes, there is a unique, socially efficient solution. In this paper, we consider Data markets where there is more than one Data Aggregator. Since Data can be cheaply reproduced and transmitted once created, Data sources may share the same Data with more than one Aggregator, leading to free-riding between Data Aggregators. This coupling can lead to nonuniqueness of equilibria and social inefficiency. We examine a particular class of mechanisms that have received study recently in the literature, and we characterize all the generalized Nash (GN) equilibria of the resulting Data market. We show that, in contrast to the single-Aggregator case, there is either infinitely many GN equilibria or none. We also provide necessary and sufficient conditions for all equilibria to be socially inefficient. In our analysis, we identify the components of these mechanisms that give rise to these undesirable outcomes, showing the need for research into mechanisms for competitive settings with multiple Data purchasers and sellers.
-
competitive statistical estimation with strategic Data sources
arXiv: Computer Science and Game Theory, 2019Co-Authors: Tyler Westenbroek, Roy Dong, Lillian J Ratliff, Shankar SastryAbstract:In recent years, Data has played an increasingly important role in the economy as a good in its own right. In many settings, Data Aggregators cannot directly verify the quality of the Data they purchase, nor the effort exerted by Data sources when creating the Data. Recent work has explored mechanisms to ensure that the Data sources share high quality Data with a single Data Aggregator, addressing the issue of moral hazard. Oftentimes, there is a unique, socially efficient solution. In this paper, we consider Data markets where there is more than one Data Aggregator. Since Data can be cheaply reproduced and transmitted once created, Data sources may share the same Data with more than one Aggregator, leading to free-riding between Data Aggregators. This coupling can lead to non-uniqueness of equilibria and social inefficiency. We examine a particular class of mechanisms that have received study recently in the literature, and we characterize all the generalized Nash equilibria of the resulting Data market. We show that, in contrast to the single-Aggregator case, there is either infinitely many generalized Nash equilibria or none. We also provide necessary and sufficient conditions for all equilibria to be socially inefficient. In our analysis, we identify the components of these mechanisms which give rise to these undesirable outcomes, showing the need for research into mechanisms for competitive settings with multiple Data purchasers and sellers.
-
statistical estimation with strategic Data sources in competitive settings
Conference on Decision and Control, 2017Co-Authors: Tyler Westenbroek, Roy Dong, Lillian J Ratliff, Shankar SastryAbstract:In this paper, we introduce a preliminary model for interactions in the Data market. Recent research has shown ways in which a single central Data Aggregator can design mechanisms to ensure it receives high quality Data from a collection of users, even when the sources have an aversion to producing and reporting such estimates to the Aggregator. However, we have shown that these mechanisms often break down in more realistic models, where multiple Data Aggregators are in competition for the users' Data. We formulate the competition that arises between the Aggregators as a game, and show this game admits either no Nash equilibria, or a continuum of Nash Equilibria. In the latter case, there is a fundamental ambiguity in who bears the burden of incentivizing different Data sources. We are also able to calculate the price of anarchy, which measures how much social welfare is lost between the Nash equilibrium and the social optimum, i.e. between non-cooperative strategic play and cooperation.
-
statistical estimation with strategic Data sources in competitive settings
arXiv: Computer Science and Game Theory, 2017Co-Authors: Tyler Westenbroek, Roy Dong, Lillian J Ratliff, Shankar SastryAbstract:In this paper, we introduce a preliminary model for interactions in the Data market. Recent research has shown ways in which a Data Aggregator can design mechanisms for users to ensure the quality of Data, even in situations where the users are effort-averse (i.e. prefer to submit lower-quality estimates) and the Data Aggregator cannot observe the effort exerted by the users (i.e. the contract suffers from the principal-agent problem). However, we have shown that these mechanisms often break down in more realistic models, where multiple Data Aggregators are in competition. Under minor assumptions on the properties of the statistical estimators in use by Data Aggregators, we show that there is either no Nash equilibrium, or there is an infinite number of Nash equilibrium. In the latter case, there is a fundamental ambiguity in who bears the burden of incentivizing different Data sources. We are also able to calculate the price of anarchy, which measures how much social welfare is lost between the Nash equilibrium and the social optimum, i.e. between non-cooperative strategic play and cooperation.
Yunhee Kang - One of the best experts on this subject based on the ideXlab platform.
-
an extended ogsa based service Data Aggregator by using notification mechanism
Lecture Notes in Computer Science, 2004Co-Authors: Yunhee KangAbstract:This paper presents an extended service Data Aggregator service based on notification mechanism in a Grid environment. To solve scalability problem in its infrastructure, the extended Aggregator aperiodically aggregates the Service Data Element(SDE) based on notification scheme about the kinds of Data which are gathered. The Aggregator parses messages and extracts information about the status of service as well as computing resources. In order to provide the persistent grid information service, we also apply Xindice DBMS to maintain SDEs on multiple collections for storing the collection of the resource information as well as its services.
Ping Wang - One of the best experts on this subject based on the ideXlab platform.
-
cooperative transmission for meter Data collection in smart grid
IEEE Communications Magazine, 2012Co-Authors: Dusit Niyato, Ping WangAbstract:Smart grid will become the next-generation electrical power system to provide reliable, efficient, secure, and cost-effective energy generation, distribution, and consumption. To achieve these goals, communications infrastructure and wireless networking will play an important role in supporting Data transfer and information exchange in smart grid. In this article, the application of cooperative transmission for the meter Data collection in smart grid is introduced. In a service area of smart grid, there are multiple communities composed of power consumption nodes (e.g., houses). The power consumption demand from the nodes is measured by a smart meter and transmitted to a meter Data management system (MDMS) through the Data Aggregator unit (DAU) using wireless broadband access. The community invests in and deploys a relay station to perform relay transmission to improve the transmission rate and avoid congestion at the DAU. As a result, the MDMS will have complete and correct power demand Data, which can be used to make better decisions on power supply. Since the communities in a service area of smart grid are rational, they will optimize the relay transmission strategy so that the total cost (i.e., power cost and transmission cost) is minimized. To analyze the relay transmission strategy of the community, the noncooperative game model is formulated, and the Nash equilibrium is considered as the solution. The proposed network architecture and analysis will be useful for the design and optimization of a wireless network for smart grid.
-
impact of packet loss on power demand estimation and power supply cost in smart grid
Wireless Communications and Networking Conference, 2011Co-Authors: Dusit Niyato, Ping Wang, Zhu Han, Ekram HossainAbstract:The evolving smart grid will use advanced Data communications and networking techniques to improve efficiency and reliability of electric power generation, transmission, distribution, and consumption. Packet loss performance of the Data communications networks used in smart grid will have impact on the cost of power supply. In this paper, we model and analyze the impact of packet loss performance on the optimization of cost for power supply in smart grid. To optimize (i.e., minimize) the cost of power supply, the power demand from consumers has to be estimated based on the power usage Data, which are transferred from smart meters through Data Aggregator unit (DAU) to the meter Data management system (MDMS). First, we present a model to optimize the cost of power supply given demand uncertainty. Then the probability distribution of power demand is estimated with and without packet loss. Subsequently, we analyze and show how the packet loss increases the cost of power supply. Next, a queueing model is used to quantify the packet loss due to congestion at DAU, and then the transmission rate from the DAU is optimized to minimize the impact of packet loss. The modeling and analysis of packet loss performance presented in this paper is a step toward optimal network design for future smart grid.
Tyler Westenbroek - One of the best experts on this subject based on the ideXlab platform.
-
competitive statistical estimation with strategic Data sources
IEEE Transactions on Automatic Control, 2020Co-Authors: Tyler Westenbroek, Roy Dong, Lillian J Ratliff, Shankar SastryAbstract:In recent years, Data have played an increasingly important role in the economy as a good in its own right. In many settings, Data Aggregators cannot directly verify the quality of the Data they purchase, nor the effort exerted by Data sources when creating the Data. Recent work has explored mechanisms to ensure that the Data sources share high-quality Data with a single Data Aggregator, addressing the issue of moral hazard. Oftentimes, there is a unique, socially efficient solution. In this paper, we consider Data markets where there is more than one Data Aggregator. Since Data can be cheaply reproduced and transmitted once created, Data sources may share the same Data with more than one Aggregator, leading to free-riding between Data Aggregators. This coupling can lead to nonuniqueness of equilibria and social inefficiency. We examine a particular class of mechanisms that have received study recently in the literature, and we characterize all the generalized Nash (GN) equilibria of the resulting Data market. We show that, in contrast to the single-Aggregator case, there is either infinitely many GN equilibria or none. We also provide necessary and sufficient conditions for all equilibria to be socially inefficient. In our analysis, we identify the components of these mechanisms that give rise to these undesirable outcomes, showing the need for research into mechanisms for competitive settings with multiple Data purchasers and sellers.
-
competitive statistical estimation with strategic Data sources
arXiv: Computer Science and Game Theory, 2019Co-Authors: Tyler Westenbroek, Roy Dong, Lillian J Ratliff, Shankar SastryAbstract:In recent years, Data has played an increasingly important role in the economy as a good in its own right. In many settings, Data Aggregators cannot directly verify the quality of the Data they purchase, nor the effort exerted by Data sources when creating the Data. Recent work has explored mechanisms to ensure that the Data sources share high quality Data with a single Data Aggregator, addressing the issue of moral hazard. Oftentimes, there is a unique, socially efficient solution. In this paper, we consider Data markets where there is more than one Data Aggregator. Since Data can be cheaply reproduced and transmitted once created, Data sources may share the same Data with more than one Aggregator, leading to free-riding between Data Aggregators. This coupling can lead to non-uniqueness of equilibria and social inefficiency. We examine a particular class of mechanisms that have received study recently in the literature, and we characterize all the generalized Nash equilibria of the resulting Data market. We show that, in contrast to the single-Aggregator case, there is either infinitely many generalized Nash equilibria or none. We also provide necessary and sufficient conditions for all equilibria to be socially inefficient. In our analysis, we identify the components of these mechanisms which give rise to these undesirable outcomes, showing the need for research into mechanisms for competitive settings with multiple Data purchasers and sellers.
-
statistical estimation with strategic Data sources in competitive settings
Conference on Decision and Control, 2017Co-Authors: Tyler Westenbroek, Roy Dong, Lillian J Ratliff, Shankar SastryAbstract:In this paper, we introduce a preliminary model for interactions in the Data market. Recent research has shown ways in which a single central Data Aggregator can design mechanisms to ensure it receives high quality Data from a collection of users, even when the sources have an aversion to producing and reporting such estimates to the Aggregator. However, we have shown that these mechanisms often break down in more realistic models, where multiple Data Aggregators are in competition for the users' Data. We formulate the competition that arises between the Aggregators as a game, and show this game admits either no Nash equilibria, or a continuum of Nash Equilibria. In the latter case, there is a fundamental ambiguity in who bears the burden of incentivizing different Data sources. We are also able to calculate the price of anarchy, which measures how much social welfare is lost between the Nash equilibrium and the social optimum, i.e. between non-cooperative strategic play and cooperation.
-
statistical estimation with strategic Data sources in competitive settings
arXiv: Computer Science and Game Theory, 2017Co-Authors: Tyler Westenbroek, Roy Dong, Lillian J Ratliff, Shankar SastryAbstract:In this paper, we introduce a preliminary model for interactions in the Data market. Recent research has shown ways in which a Data Aggregator can design mechanisms for users to ensure the quality of Data, even in situations where the users are effort-averse (i.e. prefer to submit lower-quality estimates) and the Data Aggregator cannot observe the effort exerted by the users (i.e. the contract suffers from the principal-agent problem). However, we have shown that these mechanisms often break down in more realistic models, where multiple Data Aggregators are in competition. Under minor assumptions on the properties of the statistical estimators in use by Data Aggregators, we show that there is either no Nash equilibrium, or there is an infinite number of Nash equilibrium. In the latter case, there is a fundamental ambiguity in who bears the burden of incentivizing different Data sources. We are also able to calculate the price of anarchy, which measures how much social welfare is lost between the Nash equilibrium and the social optimum, i.e. between non-cooperative strategic play and cooperation.
Dusit Niyato - One of the best experts on this subject based on the ideXlab platform.
-
cooperative transmission for meter Data collection in smart grid
IEEE Communications Magazine, 2012Co-Authors: Dusit Niyato, Ping WangAbstract:Smart grid will become the next-generation electrical power system to provide reliable, efficient, secure, and cost-effective energy generation, distribution, and consumption. To achieve these goals, communications infrastructure and wireless networking will play an important role in supporting Data transfer and information exchange in smart grid. In this article, the application of cooperative transmission for the meter Data collection in smart grid is introduced. In a service area of smart grid, there are multiple communities composed of power consumption nodes (e.g., houses). The power consumption demand from the nodes is measured by a smart meter and transmitted to a meter Data management system (MDMS) through the Data Aggregator unit (DAU) using wireless broadband access. The community invests in and deploys a relay station to perform relay transmission to improve the transmission rate and avoid congestion at the DAU. As a result, the MDMS will have complete and correct power demand Data, which can be used to make better decisions on power supply. Since the communities in a service area of smart grid are rational, they will optimize the relay transmission strategy so that the total cost (i.e., power cost and transmission cost) is minimized. To analyze the relay transmission strategy of the community, the noncooperative game model is formulated, and the Nash equilibrium is considered as the solution. The proposed network architecture and analysis will be useful for the design and optimization of a wireless network for smart grid.
-
impact of packet loss on power demand estimation and power supply cost in smart grid
Wireless Communications and Networking Conference, 2011Co-Authors: Dusit Niyato, Ping Wang, Zhu Han, Ekram HossainAbstract:The evolving smart grid will use advanced Data communications and networking techniques to improve efficiency and reliability of electric power generation, transmission, distribution, and consumption. Packet loss performance of the Data communications networks used in smart grid will have impact on the cost of power supply. In this paper, we model and analyze the impact of packet loss performance on the optimization of cost for power supply in smart grid. To optimize (i.e., minimize) the cost of power supply, the power demand from consumers has to be estimated based on the power usage Data, which are transferred from smart meters through Data Aggregator unit (DAU) to the meter Data management system (MDMS). First, we present a model to optimize the cost of power supply given demand uncertainty. Then the probability distribution of power demand is estimated with and without packet loss. Subsequently, we analyze and show how the packet loss increases the cost of power supply. Next, a queueing model is used to quantify the packet loss due to congestion at DAU, and then the transmission rate from the DAU is optimized to minimize the impact of packet loss. The modeling and analysis of packet loss performance presented in this paper is a step toward optimal network design for future smart grid.
