This paper grounds on the SAPERE project (Self-Aware PERvasive Service Ecosystems), which aims at proposing a multi-agent framework for pervasive computing, based on the idea of making each agent (service, device, human) manifest its existence in the ecosystem by a Live Semantic Annotation (LSA), and of coordinating agent activities by a small and fixed set of so-called eco-laws--sort of chemical-like reactions over patterns of LSAs. System dynamics in SAPERE is complex because of opennes and due to the self-* requirements imposed by the pervasive computing setting: a simulation framework is hence needed for what-if analysis prior to deployment. In this paper we present a prototype simulator we are developing, tested on a crowd steering scenario. Due to the role of chemical-like dynamics, this is based on a variation of an existing SSA (Stochastic Simulation Algorithm), suitable tailored to the specific features of SAPERE, including dynamicity of network topology, pattern-based application of eco-laws, and temporal triggers.

A Simulation Framework for Pervasive Services Ecosystems

M. Viroli;S. Montagna
2011

Abstract

This paper grounds on the SAPERE project (Self-Aware PERvasive Service Ecosystems), which aims at proposing a multi-agent framework for pervasive computing, based on the idea of making each agent (service, device, human) manifest its existence in the ecosystem by a Live Semantic Annotation (LSA), and of coordinating agent activities by a small and fixed set of so-called eco-laws--sort of chemical-like reactions over patterns of LSAs. System dynamics in SAPERE is complex because of opennes and due to the self-* requirements imposed by the pervasive computing setting: a simulation framework is hence needed for what-if analysis prior to deployment. In this paper we present a prototype simulator we are developing, tested on a crowd steering scenario. Due to the role of chemical-like dynamics, this is based on a variation of an existing SSA (Stochastic Simulation Algorithm), suitable tailored to the specific features of SAPERE, including dynamicity of network topology, pattern-based application of eco-laws, and temporal triggers.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11576/2690125
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