209399014
Cooperative tracking with binary-detection sensor networks.
We present a novel method for tracking the movement of people or vehicles in open outdoor environments using sensor networks. Unlike other sensor network-based methods, which depend on determining distance to the target or the angle of arrival of the signal, our cooperative tracking approach requires only that a sensor be able to determine if an object is somewhere within the maximum detection range of the sensor. We propose cooperative tracking as a method for tracking moving objects and extrapolating their paths in the short term. By combining data from neighboring sensors, this approach enables tracking with a resolution higher than that of the individual sensors being used. We employ statistical estimation and approximation techniques to further increase the tracking precision, and to enable the system to exploit the tradeoff between accuracy and timeliness of the results. We analyze the behavior of the cooperative tracking algorithm through simulation, focusing on the effects of approximation techniques on the quality of estimates achieved. This work focuses on acoustic tracking, however the presented methodology is applicable to any sensing modality where the sensing range is relatively
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.
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
13194172
ActorNet: an actor platform for wireless sensor networks
We present actorNet, a mobile agent platform for wireless sensor networks (WSNs). WSNs are well-suited to multiagent systems: agent autonomy reduces the need for communication, saving precious energy. Mobile agents are also an intuitive technique for remotely reprogramming sensors deployed in the field. However, implementing agent programs directly on a WSN is complicated by the many limitations of sensor nodes, including limited memory, slow processors, low bandwidth and finite energy. ActorNet eases development by providing an abstract environment for lightweight concurrent object-oriented mobile code on WSNs. As such, it enables a wide range of new dynamic applications, including fully customizable queries and aggregation functions, in-network interactive debugging and high-level concurrent programming on the inherently parallel sensor network platform. Moreover, actorNet cleanly integrates all of these features into a fine-tuned, multi-threaded embedded Scheme interpreter that supports compact, maintainable programs---a significant advantage over primitive stack-based virtual machines.
1158464
Response to 'More comments on: A cohesion measure for object-oriented classes'
The authors insist that monotonicity is a necessary property of a good cohesion metric and the violation of the monotonicity property limits the application of CBMC. They also state that the augmented CBMC can also be used as a guideline for quality evaluation and restructuring of poorly designed classes. This paper raises the question about the necessity of monotonicity by analyzing the reason that causes CBMC to violate the monotonicity property. In addition, we give a detailed description of the restructuring procedure based on CBMC. Copyright c © 2003 John Wiley & Sons, Ltd.
15507852
Testing of concurrent programs based on message sequence charts
Most prior work on specification-based testing of concurrent programs have postulated an equivalence relation between specifications and programs. However the equivalence relation, which requires a specification and a program to encompass the same set of synchronization sequences, is not well-suited for resting concurrent programs against partial and nondeterministic specifications. We present a new specification-based approach to testing concurrent programs against Message Sequence Charts (MSCs) with partial and nondeterministic semantics. New conformance relations, named behavioral conformance and nondeterminacy conformance, are defined and the details of the proposed testing methodology based on the conformance relations are given. The effectiveness of our testing approach is investigated using a telephone switching system example.
7677555
Stock prediction based on financial correlation
In this paper, we propose a neuro-genetic stock prediction system based on financial correlation between companies. A number of input variables are produced from the relatively highly correlated companies. The genetic algorithm selects a set of informative input features among them for a recurrent neural network. It showed notable improvement over not only the buy-and-hold strategy but also the recurrent neural network using only the input variables from the target company.
10587534
Assessing and restructuring of classes based on cohesion
Classes are the fundamental concepts in the object-oriented paradigm. They are the basic units of object-oriented programs, and serve as the units of encapsulation, which promotes their modifiability and reusability. However, improper modeling in the design phase or uncontrolled changes during the maintenance phase can degrade the quality of classes, which leads to programs which are cumbersome to maintain or extend. The paper proposes a method for assessing the quality of classes and restructuring them. The authors define an abstraction as the most cohesive form of a class, and the cohesion indicates the extent to which a class approaches the abstraction. Assessment and restructuring are both based on the notion of class cohesion. Less cohesive classes can be transformed into a form of an abstraction by restructuring them.
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.