ETD system

Electronic theses and dissertations repository


Tesi etd-11062013-162021

Thesis type
Tesi di laurea magistrale
Modeling and Simulation of Automatic Weather Stations with mixed energy sources for the design of Sensing and Communication policies
Corso di studi
relatore Prof. Bernardeschi, Cinzia
relatore Ing. Cassano, Luca
relatore Prof. Avvenuti, Marco
relatore Ing. Cesarini, Daniel
Parole chiave
  • mixed energy
  • simulator
  • Mobius
  • energy harvesting
  • wind turbine
  • solar panel
  • MPPT
  • C++
  • AWS
  • policies
  • adaptive policies
  • static policies
  • sensing
  • communication
Data inizio appello
Riassunto analitico
Automatic Weather Stations (AWSs) are widely used in environmental sensing. AWS is an automated type of traditional weather station that allows measurements
from a remote station with the purpose of studying the climatic conditions. These systems are often used in harsh sites where they have to be energy self-sufficient.
Thus, they are equipped with an energy harvesting system, e.g. solar panels and wind turbines, which are used to recharge the batteries. These systems execute an application that provides
sensing and communication rates. The AWS designer has to establish a tradeoff between user demands
of sensing and communication rates and the energy survival of the AWS itself, which is a key factor.

In this thesis we introduce an energetic AWS simulator which helps the AWS designer to assess the feasibility of a given policy,
taking in consideration historical or ad-hoc generated context information such as temperature, solar radiation and wind power.
This simulator gives the designer a powerful and customizable tool that models physical components and the application of an AWS.
Applications are modeled as a suite of independent policies, one for each sensing or transmission device.
Policies are modeled independently on the actual implementation, so they can easily be defined by the AWS designer.

The simulator can be used in many interesting cases but we focused on the choice of the best configuration for an AWS, starting from
the environmental condition, using static policies and the simulation of adaptive application that makes the AWS energy-aware.

The simulator was also validated using real data from an experimental Zagreb AWS making a comparison between simulator results and Zagreb data.