Sensor Networks are required to be properly designed in order to avoid resource waste and optimize their lifetime. Large monitoring applications require proper methodologies and tools supporting the design, when multiple solutions increase the complexity of this task. Indeed, different parameters affect the performance of a solution, as node distribution, sensing coverage, battery usage, etc. A compositional modelling approach can provide early measures, allowing to evaluate and compare different solutions since the design phase. The main contribution of the paper is the definition of a general modelling framework to integrate simple models representing the main components and features of sensor networks. A library for specific sensor devices have been developed, using the Stochastic Activity Network (SAN) formalism. This approach is shown to be compositional since the creation of complex networks can be accomplished by simple subcomponents aggregation. With this approach, obtained models can analyse the dynamic evolution of the overall network, even if complex. First experimental results are also reported and discussed. �� 2013 IEEE.

A compositional modelling approach for large Sensor Networks design

Roberto Nardone;
2013-01-01

Abstract

Sensor Networks are required to be properly designed in order to avoid resource waste and optimize their lifetime. Large monitoring applications require proper methodologies and tools supporting the design, when multiple solutions increase the complexity of this task. Indeed, different parameters affect the performance of a solution, as node distribution, sensing coverage, battery usage, etc. A compositional modelling approach can provide early measures, allowing to evaluate and compare different solutions since the design phase. The main contribution of the paper is the definition of a general modelling framework to integrate simple models representing the main components and features of sensor networks. A library for specific sensor devices have been developed, using the Stochastic Activity Network (SAN) formalism. This approach is shown to be compositional since the creation of complex networks can be accomplished by simple subcomponents aggregation. With this approach, obtained models can analyse the dynamic evolution of the overall network, even if complex. First experimental results are also reported and discussed. �� 2013 IEEE.
Compositional modelling; Modelling framework; Monitoring applications; Multiple solutions; Performance evaluation; Sensor networks design; Stochastic activity network (SAN); Stochastic activity networks; Complex networks; Computer network performance evaluation; Design; Hierarchical systems; Internet; Sensors; Wireless sensor networks; Distributed computer systems
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/47263
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