@article {275,
	title = {Energy control in dependable Wireless Sensor Networks: a modeling perspective},
	journal = {PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS. PART O, JOURNAL OF RISK AND RELIABILITY},
	volume = {225},
	year = {2011},
	pages = {424{\textendash}434},
	abstract = {<p>Wireless sensor networks (WSN) are composed of a large number of tiny sensor nodes randomly distributed over a geographical region. In order to reduce power consumption, battery-operated sensors undergo cycles of sleeping-active periods that reduce their ability to send/receive data. Starting from the Markov reward model theory, this paper presents a dependability model to analyse the reliability of a sensor node. Also, a new dependability parameter is introduced, referred to as producibility, which is able to capture the capability of a sensor to accomplish its mission. Two different model solution techniques are proposed, one based on the evaluation of the accumulated reward distribution and the other based on an equivalent model based on non-Markovian stochastic Petri nets. The obtained results are used to investigate the dependability of a whole WSN taking into account the presence of redundant nodes. Topological aspects are taken into account, providing a quantitative comparison among three typical network topologies: star, tree, and mesh. Numerical results are provided in order to highlight the advantages of the proposed technique and to demonstrate the equivalence of the proposed approaches.</p>
},
	keywords = {Energy consumption, Markov reward models, network topology, non-Markovian stochastic Petri nets, producibility, Reliability, Wireless sensor networks},
	doi = {10.1177/1748006X10397845},
	author = {D. Bruneo and A. Puliafito and Marco Scarpa}
}
@booklet {286,
	title = {Artificial Intelligence And Synchronization In Wireless Sensor Networks},
	journal = {JOURNAL OF NETWORKS},
	volume = {4},
	year = {2009},
	pages = {382{\textendash}391},
	abstract = {The basic concept behind a Wireless Sensor Network is to deploy a large number of sensor nodes able to acquire and process data. Most of WSNs applications require sensor nodes to maintain local clocks both to determine the events order and to provide temporal information to measured data. Thus, providing a powerful synchronization system is one of the most important goals to be pursued if an efficient utilization of sensor networks has to be addressed. In order to achieve this goal, applications generally require a synchronization precision close to Milli seconds. This paper proposes a novel synchronization system based on Kohonens Self Organizing Maps (SOMs), able to provide some Artificial Intelligence features to sensor nodes. A SOM is a particular neural network that learns to classify data without any supervision. In each sensor node, a SOM is implemented to evaluate the sensor node time, using a very little amount of storage and computing resources. In a scenario where thousands of sensor nodes are placed, this system evaluates the time of each sensor in a distributed manner, assuming a very little percentage of nodes knowing their actual time, thus ensuring an effective clock synchronization among all the sensors.},
	keywords = {neural networks, synchronization, Wireless sensor networks},
	doi = {10.4304/jnw.4.6.382-391},
	author = {L. Paladina and A. Biundo and Marco Scarpa and A. Puliafito}
}
