@article {Bruneo2012521,
	title = {Evaluating wireless sensor node longevity through Markovian techniques},
	journal = {Computer Networks - Elsevier },
	volume = {56},
	number = {2},
	year = {2012},
	note = {cited By 13},
	pages = {521-532},
	abstract = {<p>Wireless sensor networks are constituted of a large number of tiny sensor nodes randomly distributed over a geographical region. In order to reduce power consumption, nodes undergo active-sleep periods that, on the other hand, limit their ability to send/receive data. The aim of this paper is to analyze the longevity of a battery-powered sensor node. A battery discharge model able to capture both linear and non linear discharge processes is presented. Then, two different models are proposed to investigate the longevity, in terms of reliability, of sensor nodes with active-sleep cycles. The first model, well known in the literature, is based on the Markov reward theory and on the evaluation of the accumulated reward distribution. The second model, based on continuous phase type distributions and Kronecker algebra, represents the main contribution of the present work, since it allows to relax some assumptions of the Markov reward model, thus increasing its applicability to more concrete use cases. In the final part of the paper, the results obtained by applying the two techniques to a case study are compared in order to validate and highlight the benefits of our approach and demonstrate the utility of the proposed model in a quite complex and real scenario. {\textcopyright} 2011 Published by Elsevier B.V.</p>
},
	keywords = {Algebra, Battery discharge, Continuous phase, Discharge process, Electric network synthesis, Energy utilization, Kronecker algebra, Markov reward, Markov reward model, Markov reward models, Markovian, Randomly distributed, Reliability, Reliability theory, Sensor nodes, Sensors, Wireless sensor node},
	issn = {13891286},
	doi = {10.1016/j.comnet.2011.10.003},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84856063119\&partnerID=40\&md5=ecbae538bceec8598d787ee3b936a8e1},
	author = {Dario Bruneo and Salvatore Distefano and Francesco Longo and Antonio Puliafito and Marco Scarpa}
}
