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Title: Using stochastic activity networks to study the energy feasibility of automatic weather stations

Automatic Weather Stations (AWSs) are systems equipped with a number of environmental sensors and communication interfaces used to monitor harsh environments, such as glaciers and deserts. Designing such systems is challenging, since designers have to maximize the amount of sampled and transmitted data while considering the energy needs of the system that, in most cases, is powered by rechargeable batteries and exploits energy harvesting, e.g., solar cells and wind turbines. To support designers of AWSs in the definition of the software tasks and of the hardware configuration of the AWS we designed and implemented an energy-aware simulator of such systems. The simulator relies on the Stochastic Activity Networks (SANs) formalism and has been developed using the Möbius tool. In this paper we first show how we used the SAN formalism to model the various components of an AWS, we then report results from an experiment carried out to validate the simulator against a real-world AWS and we finally show some examples of usage of the proposed simulator.
Authors:
 [1] ;  [2] ;  [3]
  1. Dipartimento di Elettronica, Informatica e Bioingegneria, Politecnico di Milano (Italy)
  2. Scuola Superiore Sant’Anna, Pisa (Italy)
  3. Dipartimento di Ingegneria dell’Informazione, University of Pisa (Italy)
Publication Date:
OSTI Identifier:
22391137
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1648; Journal Issue: 1; Conference: ICNAAM-2014: International Conference on Numerical Analysis and Applied Mathematics 2014, Rhodes (Greece), 22-28 Sep 2014; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; COMMUNICATIONS; COMPUTER CODES; DESIGN; GLACIERS; INTERFACES; MONITORS; SENSORS; SIMULATORS; SOLAR CELLS; STOCHASTIC PROCESSES; WEATHER