Study and development of distributed detectors with fast convergence

• Grant number: 18/26040-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: May 01, 2019
• End date: January 31, 2022
• Field of knowledge: Engineering - Electrical Engineering - Telecommunications
• Principal Investigator: Vitor Heloiz Nascimento
• Grantee: Allan Eduardo Feitosa
• Host Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Nowadays there is a large interest in developing detection techniques using distributed networks, which have received a considerable attention due to their economy of energy and resource, robustness to node and link failure, scalability, and improved tracking performance. Detection algorithms may in general be based on Neyman-Pearson (NP) detectors, which maximize the probability of detection given a maximum false alarm rate, or on Bayesian detectors, which minimize the probability of error in detection. In the case of algorithms implemented in distributed networks, using connections only with neighboring nodes, each node must decide between concurrent hypotheses about the environment state where the network is inserted.The candidate's master degree project investigated the transient performance of distributed detectors, and a binary Maximum Likelihood detector was proposed - i.e., when the prior probabilities of the Bayesian detector are equal. The decision process was based on local measurements and on estimates shared among nodes using the distributed diffusion LMS estimation algorithm. A specific initialization of the developed algorithm accelerates its performance, significantly reducing the time required for the convergence of the error probability. In this project, we propose to generalize and deepen the already achieved results to novel situations, developing a more complete analysis of transient performance of distributed detectors. We will develop solutions for NP and Bayesian detectors using different prior probabilities and for situations with more than two hypotheses, looking for theoretical generalizations of the new results, as well as consider the case of nonstationary environments (tracking). (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

FEITOSA, ALLAN E.; NASCIMENTO, VITOR H.; LOPES, CASSIO G.; IEEE. LOW COMPLEXITY DISTRIBUTED ESTIMATION FOR IoT SENSOR NETWORKS. 2021 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP), v. N/A, p. 5-pg., 2021-01-01. (18/12579-7, 18/26040-2)

FEITOSA, ALLAN E.; NASCIMENTO, VITOR H.; LOPES, CASSIO G.; IEEE. A LOW-COMPLEXITY MAP DETECTOR FOR DISTRIBUTED NETWORKS. 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, v. N/A, p. 5-pg., 2020-01-01. (18/12579-7, 18/26040-2)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

FEITOSA, Allan Eduardo. Favor the tortoise over the hare: a study on an efficient detection algorithm for wireless sensor networks.. 2023. Doctoral Thesis - Universidade de São Paulo (USP). Escola Politécnica (EP/BC) São Paulo.