Istituto di Scienza e Tecnologie dell'Informazione     
Barsi F., Bertossi A. A., Lavault C., Navarra A., Olariu S., Pinotti M. C., Ravelomanana V. Efficient binary schemes for training heterogeneous sensor and actor networks. In: HeterSanet 2008 - International Symposium On Mobile Ad Hoc Networking & Computing. 1st ACM International Workshop on Heterogeneous Sensor and Actor Networks (Hong Kong, China, 26 maggio 2008). Proceedings, pp. 17 - 24. ACM, 2008.
Sensor networks are expected to evolve into long-lived, au- tonomous networked systems whose main mission is to pro- vide in-situ users - called actors - with real-time informa- tion in support of specific goals supportive of their mis- sion. The network is populated with a heterogeneous set of tiny sensors. The free sensors alternate between sleep and awake periods, under program control in response to computational and communication needs. The periodic sen- sors alternate between sleep periods and awake periods of predefined lengths, established at the fabrication time. The architectural model of an actor-centric network used in this work comprises in addition to the tiny sensors a set of mobile actors that organize and manage the sensors in their vicinity. We take the view that the sensors deployed are anonymous and unaware of their geographic location. Importantly, the sensors are not, a priori, organized into a network. It is, indeed, the interaction between the actors and the sensor population that organizes the sensors in a disk around each actor into a short-lived, mission-specific, network that exists for the purpose of serving the actor and that will be disbanded when the interaction terminates. The task of setting up this form of actor-centric network involves a training stage where the sensors acquire dynamic coordi- nates relative to the actor in their vicinity. The main contribution of this work is to propose an energy- efficient training protocol for actor-centric heterogeneous sen- sor networks. Our protocol outperforms all know training protocols in the number of sleep/awake transitions per sensor needed by the training process. Specifically, in the presence of k coronas, no sensor will experience more than ⌈log k⌉ sleep/awake transitions and awake periods.
URL: http://portal.acm.org/toc.cfm?id=1374699&type=proceeding&coll=ACM&dl=ACM&CFID=40926764&CFTOKEN=73093274
Subject Autonomous sensor networks
Free sensors
Periodic sensor
Training protocols
C.2.3 Network Operations
C.2.1 Network Architecture and Design

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