In this paper, the problem of estimating the link quality in mesh networks has been considered. Such a problem is a major task to develop an efficient network layer, since an accurate knowledge of the link qualities allows routing protocols for efficient use of neighbors as relays in multi-hop communications. In the last years, a number of link-quality aware routing metrics based on the packet delivery ratio have been proposed and analyzed. However, very few works have addressed the problem of estimating the delivery ratio of a wireless link correctly. Therefore, in this paper we carry on a performance comparison between two widely adopted delivery ratio estimators, namely the the Simple Moving Average and the Exponentially Weighted Moving Average, and a recently proposed one based on the neural network paradigm, namely the Simple Unsupervised Neuron Estimator (SUNE), on an IEEE 802.11b mesh network.
Performance Analysis of Link Quality Estimators for an 802.11b Mesh Network / Cacciapuoti, ANGELA SARA; Caleffi, Marcello; Paura, Luigi; Rahman, MD ARAFATUR. - (2010), pp. 1-6. (Intervento presentato al convegno Inf-Q 2010: Primo Workshop del Gruppo di Informatica Quantitativa tenutosi a Pisa nel 7-9 Luglio 20120).
Performance Analysis of Link Quality Estimators for an 802.11b Mesh Network
CACCIAPUOTI, ANGELA SARA;CALEFFI, MARCELLO;PAURA, LUIGI;RAHMAN, MD ARAFATUR
2010
Abstract
In this paper, the problem of estimating the link quality in mesh networks has been considered. Such a problem is a major task to develop an efficient network layer, since an accurate knowledge of the link qualities allows routing protocols for efficient use of neighbors as relays in multi-hop communications. In the last years, a number of link-quality aware routing metrics based on the packet delivery ratio have been proposed and analyzed. However, very few works have addressed the problem of estimating the delivery ratio of a wireless link correctly. Therefore, in this paper we carry on a performance comparison between two widely adopted delivery ratio estimators, namely the the Simple Moving Average and the Exponentially Weighted Moving Average, and a recently proposed one based on the neural network paradigm, namely the Simple Unsupervised Neuron Estimator (SUNE), on an IEEE 802.11b mesh network.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
