5100963
Scalable Modeling and Performance Evaluation of Wireless Sensor Networks
A notable features of many proposed Wireless Sensor Networks (WSNs) deployments is their scale: hundreds to thousands of nodes linked together. In such systems, modeling the state of the entire system as a cross-product of the states of individual nodes results in the well-known state explosion problem. Instead, we represent the system state by the probability distribution on the state of each node. In other words, the system state represents the probability that a randomly picked node is in a certain state. Although such statistical abstraction of the global state loses some information, it is nevertheless useful in determining many performance metrics of systems that exhibit Markov behavior. We have previously developed a method for specifying the performance metrics of such systems in a probabilistic temporal logic called iLTL and for evaluating the behavior through a novel method for model checking iLTL properties. In this paper, we describe a method for estimating the probabilities in a Discrete Time Markov Chain (DTMC) model of a large-scale system. We also provide a statistical test so that we can reject estimated DTMCs if the actual system does not have Markov behavior. We describe results of experiments applying our method toWSNs in an experimental test-bed, as well as using simulations. The results of our experiments suggest that our model estimation and model checking method provides a systematic, precise and easy way of evaluating performance metrics of some large-scale WSNs.
61822837
Resilient Localization for Sensor Networks in Outdoor Environments
The process of computing the physical locations of nodes in a wireless sensor network is known as localization. Self-localization is critical for large-scale sensor networks because manual or assisted localization is often impractical due to time requirements, economic constraints or inherent limitations of deployment scenarios. We have developed a service for reliably localizing wireless sensor networks in environments conducive to ranging errors by using a custom hardware-software solution for acoustic ranging and a family of self-localization algorithms. The ranging solution improves on previous work, extending the practical measurement range threefold (20-30m) while maintaining a distance-invariant median measurement error of about 1% of maximum range (33cm). The localization scheme is based on least squares scaling with soft constraints. Evaluation using ranging results obtained from sensor network field experiments shows that the localization scheme is resilient against large-magnitude ranging errors and sparse range measurements, both of which are common in large-scale outdoor sensor network deployments
2254400
iLTLchecker: a probabilistic model checker for multiple DTMCs
iLTL is a probabilistic temporal logic that can specify properties of multiple discrete time Markov chains (DTMCs). In this paper, we describe two related tools: MarkovEstimator a tool to estimate a Markov transition matrix, and iLTLChecker, a tool to model check iLTL properties of DTMCs. These tools work together to verify iLTL properties of DTMCs.
18194762
Daily Stock Prediction Using Neuro-genetic Hybrids
We propose a neuro-genetic daily stock prediction model. Traditional indicators of stock prediction are utilized to produce useful input features of neural networks. The genetic algorithm optimizes the neural networks under a 2D encoding and crossover. To reduce the time in processing mass data, a parallel genetic algorithm was used on a Linux cluster system. It showed notable improvement on the average over the buy-and-hold strategy. We also observed that some companies were more predictable than others.
31388642
Modeling and Testing Agent Systems Based on Statecharts
This paper presents a testing method for an agent system against its specification described with Statecharts. Here, an agent system means an implementation of an agent application. Its testing method generates test sequences from the specification and checks whether the agent system behaves in accordance with the specification by executing the test sequences. For specification-based testing, we have extended Statecharts in order to describe the behavior of an agent system according to the concept of Agent UML. Then, we propose an approach to generating test sequences from the extended Statecharts. Our approach makes effective use of partial order methods for considering representative sequences of equivalent classes divided from all possible sequences. Therefore, we can efficiently manage a large number of possible sequences caused by the agents’ autonomy. As a result, we can reduce the number of test sequences and the number of executions to be tested.
40100233
Complexity Measures for Concurrent Programs Based on Information-Theoretic Metrics
Abstract We present a model and information-theoretic measures (entropy) which provide the complexity of concurrent programs. The information content, risk ratio and the amount of intertasking activity contained in a concurrent program are measured in terms of the information in the corresponding graph representation.
18311049
Testing of concurrent programs after specification changes
This paper describes a specification-based regression testing technique that can be applied for revalidating concurrent programs after specification changes. This kind of regression testing technique requires sequencing constraints which specify the precedence relations among synchronization events. In our method, the sequencing constraints are extracted automatically from Message Sequence Charts (MSCs) that are considered to be partial and nondeterministic specifications. We show how to identify the sequencing constraints affected by the modifications of a specification rather than creating new sequencing constraints from scratch to reduce the cost of regression testing. We also describe how to determine whether the affected sequencing constraints are satisfied by the program being tested.
30685161
Extending MSC for reactive systems
Message Sequence Charts (MSC) is a visual language that illustrates the scenarios of system operations, offering user-friendly, easy-to-understand behavioral descriptions. Furthermore, High-level MSC (HMSC) provides composition mechanisms to deal with the complexity of the system. However, for MSC to be used to describe complex reactive systems, there should be ways to deal with reactive behaviors that require the system to respond immediately. In this paper, we propose extensions of MSC to describe complex reactive behaviors in a systematic way. We define the formal semantics of the extended features based on process algebra, as well as the visual and textual syntax. The extensions allow structured, systematic and succinct descriptions of complex reactive behaviors.