• Group Testing for Binary Marko...

Treffer: Group Testing for Binary Markov Sources : Data-Driven Group Queries for Cooperative Sensor Networks

Title:
Group Testing for Binary Markov Sources : Data-Driven Group Queries for Cooperative Sensor Networks
Authors:
HONG, Yao-Win Peter, SCAGLIONE, Anna
Source:
IEEE transactions on information theory. 54(8):3538-3551
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2008.
Publication Year:
2008
Physical Description:
print, 47 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie de l'information, Information theory, Théorie du signal et des communications, Signal and communications theory, Codage, codes, Coding, codes, Télécommunications, Telecommunications, Systèmes, réseaux et services de télécommunications, Systems, networks and services of telecommunications, Services et terminaux de télécommunications, Services and terminals of telecommunications, Télémesure. Télésurveillance. Téléalarme. Télécommande, Telemetry. Remote supervision. Telewarning. Remote control, Accès multiple, Multiple access, Acceso múltiple, Algorithme en arbre, Tree algorithm, Algoritmo del árbol, Algorithme récursif, Recursive algorithm, Algoritmo recursivo, Chaîne Markov, Markov chain, Cadena Markov, Codage source, Source coding, Collecte donnée, Data gathering, Recolección dato, Entropie, Entropy, Entropía, Evaluation performance, Performance evaluation, Evaluación prestación, Evénement rare, Rare event, Acontecimiento rara, Méthode récursive, Recursive method, Método recursivo, Ordonnancement, Scheduling, Reglamento, Performance algorithme, Algorithm performance, Resultado algoritmo, Requête, Query, Pregunta documental, Réseau capteur, Sensor array, Red sensores, Signal Markov, Markov signal, Señal Markov, Signal binaire, Binary signal, Señal binaria, Signal source réparti, Distributed source signal, Señal fuente distribuida, Télécommunication sans fil, Wireless telecommunication, Telecomunicación sin hilo, Télédétection, Remote sensing, Teledetección, Cooperative communications, data gathering in sensor networks, distributed source coding, group testing, multiple access
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Institute of Communications Engineering, National Tsing Hua University, 30013 Hsinchu, Tawain, Province of China
Department of Electrical and Computer Engineering, Comell University, Ithaca, NY 14853, United States
ISSN:
0018-9448
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=20528701
Rights:
Copyright 2008 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.20528701
Database:
PASCAL Archive

Weitere Informationen

Group testing has been used in many applications to efficiently identify rare events in a large population. In this paper, the concept of group testing is generalized to applications with correlated source models to derive scheduling policies for sensors' adopting cooperative transmissions. The tenet of our work is that in a wireless sensor network it is advantageous to allocate the same channel dimensions to all sensor sources that have the same response to a sequence of queries or tests. That is, nodes that have the same data attributes should transmit as a cooperative super-source. Specifically, we consider the case where sensors' data are modeled spatially as a one-dimensional Markov chain. Two strategies are considered: the recursive algorithm and the tree-based algorithm. The recursive scheme allows us to illustrate the performance of group testing for finite populations while the tree-based algorithm is used to derive the achievable scaling performances of the class of group testing strategies as the number of sensors increases. We show that the total number of queries required to gather all sensors' data scales in the order of the joint entropy. A further generalization of this concept provides the basis of deriving efficient data-gathering algorithms for correlated sources.