Alexander Robert Kutzke

Diagnosability evaluation for a system-level diagnosis algorithm for Wireless Sensor Networks

In a Wireless Sensor Network (WSN) where the sensors monitor the environment to the purpose of raising alarms about the occurrence of some events, we consider the problem of detecting false alarms (i.e. false positives). We adopt a system level diagnosys model, where all the sensors in the region where the alarms have been raised cooperate to execute mutual tests. The test outcomes are given as input to a diagnosis algorithm that identifies the faulty sensors and thus confirms or neglects the alarm. The diagnosis algorithm can succeed in this work if the tests among sensors are sufficient to achieve a given diagnosability of the system, that depends on topological properties of the WSN. This work considers the problem of determining a test strategy of the sensors in a WSN in order to ensure that the desired system diagnosability is met. In particular it is shown by simulation that appropriate testing strategies are successful to guarantee given levels of system diagnosabilities depending of the network topology or its density.

Energy-aware test connection assignment for the self-diagnosis of a wireless sensor network

Sensor nodes in Wireless Sensor Networks (WSNs) are prone to failures due to the fragile hardware, malicious attacks, or hostile or harsh environment. In order to assure reliable, long-term monitoring of the phenomenon under investigation, a major challenge is to detect node malfunctions as soon as possible and with an energy efficient approach. We address this problem by using a system-level diagnosis strategy in which the sink issues to the WSN a self-diagnosis task that involves a number of mutual tests among sensors. Based on the test outcomes, the sink executes the diagnosis procedure. This work presents an algorithm for the assignment of tests among the sensors of a WSN that assures the desired system diagnosability and that is aware of energy consumption. We show by simulation experiments that the present approach, as compared to a previous one, enables consistent energy savings on the sensors.

Energy-Aware Test Connection Assignment for the Diagnosis of a Wireless Sensor Network

In a Wireless Sensor Network (WSN) where the sensors raise alarms in response to anomalous events, we consider the problem of detecting false alarms (i.e. false positives) by using a system-level diagnosis approach. In particular, we assume that a set of t sensors in a geographic neighborhood generate the alarm, and the sink issues to the WSN a self test task that involves a number of reciprocal tests among the sensors in the WSN. Based on the test outcomes, the sink executes the diagnosis in order to identify the faulty sensors. This work presents an algorithm for the assignment of the tests (i.e. a strategy for the assignment of reciprocal tests among sensors) that assures the desired system diagnosability and that is aware of the energy consumption of the sensors. Simulation experiments show that, as compared to the existing approaches, our approach enables consistent energy savings on the sensors.

Promoting Mediation in Learning Error on Teaching Algorithms.

This paper addresses the results of a research on the adoption of a computer platform for mediation of learning errors in algorithm exercises for computer science students. The importance of the interaction between teacher and learner is discussed as a way to reconsider learning errors. The need to evaluate tools for mediation of learning errors is brought to the discussion. The essential activities a teacher should develop using a tool that helps in the practice of algorithm teaching are listed, and based on them, a method that promotes the interaction between teacher and learners is proposed as a mean to enhance the effectiveness in mediation of

An Automatic Method for Structuring and Recommending Exercises - In Light of Case-based Reasoning, Knowledge Representation and Error Mediation.

Case-based Reasoning (CBR) is a method for solving problems with similar retained solutions. CBR demands a knowledge representation that allows the reasoner to find similar cases by a query and the similarity rate is given by a distance in hierarchical tree structure, an ontology. The main goal of this research is to use CBR as a pedagogical tool supported by three pillars: Case-based reasoning, Knowledge representation and Error Mediation in Education. It is considered that the error has a role of importance in the pedagogical development, so it has to be mediated. The error mediation is used as a rule for a quantitative classification of exercises, it takes into account how many times an exercise have been uncorrected answered, the distance between exercises gives the similarity between them. This kind of automatically classification for exercises in a educational support systems is one of the main contributions of this research. This work suggests that the CBR cycle is useful in the designing of a tool for automatic creation of exams.

FARMA-ALG: An Application for Error Mediation in Computer Programming Skill Acquisition

The central problem of using students’ errors as a fundamental part of teaching and learning computer programming is presented in a critical manner. The literature review points out that previous research works have not accounted for the dynamic nature of mediation in educational interactions of the referred domain. To fill such a gap, an application named FARMA-ALG is introduced. The application aims to promote error mediation in teaching and learning computer programming, with effective teacher participation. The application details and preliminary tests are shown, highlighting relevant progress to error mediation.