An Approximate Algorithm for Resource Allocation Using Combinatorial Auctions," presented at

Combinatorial Auctions (CAs), where users bid on combination of items, have emerged as a useful tool for resource allocation in distributed systems. However, two main difficulties exist to the adoption of CAs in timeconstrained environments. The first difficulty involves the computational complexity of winner determination. The second difficulty entails the computational complexity of eliciting utility valuations for all possible combinations of resources to different tasks. To address both issues, we developed a new algorithm, Seeded Genetic Algorithm (SGA) for finding high quality solutions quickly. SGA uses a novel representational schema that produces only feasible solutions. We compare the winner determination performance of our algorithm with Casanova, another local stochastic search procedure, on typically hard-tosolve bid distributions. We show that SGA converges to a better solution than Casanova for large problem sizes. However, for many bid distributions, exact winner determination using integer programming approaches is very fast, even for large problem sizes. In these cases, SGA can still provide significant time savings by eliminating the requirement for formulating all possible bids. 1

Bringing the Market to Level 4 Fusion: Investigation of Auction Methods for Sensor Resource Allocation," presented at

distributed ground based sensors and devices such as smart dust have energized research efforts towards devising a theoretical foundation for sensor management and for multi-sensor data fusion. Features such as ubiquitous sensing, wide bandwidth communications and distributed processing provide both challenges and opportunities for sensor and process control and optimization. New sensors and wideband communications provide an information rich environment for users. However, limited sensor processing and resources and the heterogeneity of the contributing sensors make the utilization of such sensors a very challenging problem. Traditional optimization techniques used in level-4 fusion do not have the ability to address such an environment. This paper describes the results of a research study that investigated the potential of market-based resource allocation algorithms to improve the performance of multi-sensor network systems in complex observing environments. Background and Problem Description Recent developments in sensor technology such as distributed ground-based sensors and devices such as smart dust have energized research efforts towards devising a theoretical foundation for sensor management and multi-sensor data fusion [1,2]. Network-centric warfare and surveillance, with features such as ubiquitous sensing, wide bandwidth communications and distributed processing provide both opportunities and challenges for sensor and process control and optimization. On one hand, new sensors and wideband communications provide an information rich environment for users (shown conceptually in Figure 1). However, limited sensor processing and resources such as battery capacities and computational capabilities and the underlying heterogeneity of the contributing sensors make the utilization of such sensors a very challenging problem. Distributed sensor networks can be utilized by multiple users operating in a semicooperative environment with little/n

Sensor Validation in Non-Destructive Evaluation using Clustering

Non-destructive evaluation (NDE) techniques for condition monitoring in remote solid structures have evolved vastly in the last few years. Algorithms for estimation of sensor integrity and for noise correction form a crucial aspect of NDE. This paper presents a sensor validation approach that verifies sensor integrity, identifies and corrects noise effects and selects the best possible array of sensors for multi-sensor fusion. The proposed methodology uses a novel change detection algorithm for noise correction and a clustering algorithm to isolate useful signal information from the sensor data. It was used for sensor selection in a NDE field study, where multiple sensors were used to examine a solid structure. The methodology achieved 97 % accuracy in the experiments, indicating its efficacy

Customer-Driven Sensor Management

Customer-driven sensor management advocates bringing ecommerce concepts and advances to bear in sensor management. In ecommerce, customer wants essentially drive the production process. Sensor management has traditionally followed a much less capitalistic process, producing information “goods ” based on pre-defined system goals and priorities. We explore here some of the possibilities of incorporating a customerdriven market-based approach to sensor management

Evolutionary optimization of transition probability matrices for credit decision-making

Statistical transition probability matrices (TPMs), which indicate the likelihood of obligor credit state migration over a certain time horizon, have been used in various credit decision-making applications. A standard approach of calculating TPMs is to form a one-year empirical TPM and then project it into the future based on Markovian and time-homogeneity assumptions. However, the one-year empirical TPM calculated from historical data generally does not satisfy desired properties. We propose an alternative methodology by formulating the problem as a constrained optimization problem requiring satisfaction of all the desired properties and minimization of the discrepancy between predicted multi-year TPMs and empirical evidence. The problem is high-dimensional, non-convex, and non-separable, and is not effectively solved by nonlinear programming methods. To address the difficulty, we investigated evolutionary algorithms (EAs) and problem representation schemas. A self-adaptive differential evolution algorithm JADE, together with a new representation schema that automates constraint satisfaction, is shown to be the most effective technique.Risk management Finance Evolutionary computations Constraints satisfaction Decision support systems

Abstract A Market-Based Sensor Management Approach

Given the explosion in number and types of sensor nodes, the next generation of sensor management systems must focus on identifying and acquiring valuable information from this potential flood of sensor data. Thus an emerging problem is deciding what to produce, where, for whom, and when. Identifying and making tradeoffs involved in information production is a difficult problem that market-based systems “solve ” by allowing user values, or utilities, to drive the selection process. Essentially this transforms the traditional “data driven ” approach (in which multiple sensors and information sources are used, with a focus on how to process the collected data) to a user-centered approach in which one or more users treat the information collection and distribution system as a market and vie to acquire goods and services (e.g., information collection, processing resources and network bandwidth). We describe our market-based approach to sensor management, and compare our prototype system to an information-theoretic system in a multi-sensor, multi-user simulation with promising results. This research is motivated in part, by rapid technology advances in network technology and in sensing. These advances allow near universal instrumentation and sensing with world-wide distribution. However while advances in service-oriented architectures and web-based tools have created “the plumbing ” for data distribution and access, improvements in optimization of these distributed resources for effective decision making have lagged behind the collection and distribution advances. Index Terms — sensor management, resource allocation, combinatorial auctions, level 4 data fusion, genetic algorithms, computational market systems. I

PSUTAC: A Trading Agent Designed from Heuristics to Knowledge

The Trading Agent Competition has provided a challenging game environment, where competing agents engage in complex decision-making activities in a simulated supply chain domain. We analyze our agent, PSUTAC, together with five other top performing teams on their general strategies for making interrelated procurement, sales, production, and delivery decisions. Heuristic methods are found to form the core of the agent strategies. However, organizing, maintaining, and applying these heuristics in such a complex and uncertain domain is a nontrivial task. We propose a knowledge-based approach to organize these heuristics. Compared with the architecture that PSUTAC used before, the new design has shown many improvements including ease of coding, testing, and transparency