Francesco Betti Sorbelli

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— Scalable energy-efficient training protocols are proposed for wireless networks consisting of sensors and a single actor, where the sensors are initially anonymous and unaware of their location. The protocols are based on an intuitive coordinate system imposed onto the deployment area which partitions the sensors into clusters. The protocols are(More)
A scalable energy-efficient training protocol is proposed for massively-deployed sensor networks, where sensors are initially anonymous and unaware of their location. The protocol is based on an intuitive coordinate system imposed onto the deployment area which partitions the anonymous sensors into clusters. The protocol is asynchronous, in the sense that(More)
Scalable energy-efficient training protocols are proposed for networks consisting of Sensors and Actors (SANET), where the sensors are initially anonymous and unaware of their location. The protocols are based on an intuitive coordinate system imposed onto the deployment area which partitions subsets of the sensor population into clusters. The protocols are(More)
In this paper, we study the sensor localization problem using a drone. Our goal is to localize each sensor in the deployment area ensuring a predefined localization precision, i.e., a bound on the position error, whatever is the drone's altitude. We show how to guarantee a-priori the precision localization by satisfying few conditions. Such conditions are(More)
Exploiting features of high density wireless sensor networks represents a challenging issue. In this work, the training of a sensor network which consists of anonymous and asynchronous sensors, randomly and massively distributed in a circular area around a more powerful device , called actor, is considered. The aim is to partition the network area in(More)
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