On the Use of Stochastic Activity Networks for an Energy-Aware Simulation of Automatic Weather Stations

On the Use of Stochastic Activity Networks for an Energy-Aware Simulation of Automatic Weather Stations

Luca Cassano (Politecnico di Milano, Italy), Daniel Cesarini (Scuola Superiore Sant'Anna, Italy) and Marco Avvenuti (Università di Pisa, Italy)
DOI: 10.4018/978-1-4666-8823-0.ch006
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Abstract

Automatic Weather Stations (AWSs) are embedded systems equipped with a number of sensors used to monitor harsh environments: glaciers and deserts. AWSs may also be equipped with some communication interfaces in order to enable remote access to data. These systems are generally far from power sources, and thus they are equipped with energy harvesting devices, wind turbines and solar panels, and storage devices, batteries. The design of an AWS represents a challenge, since designers have to maximize the sampled and transmitted data while considering the energy needs. We designed and implemented an energy-aware simulator of AWSs to support designers in the definition of the configuration of the system. The simulator relies on the Stochastic Activity Networks (SANs) formalism and has been developed using the Möbius tool. In this chapter we first show how we used SANs to model the components of an AWS, we then report results from validation experiments carried out by comparing the results of the simulator against a real-world AWS and finally show examples of its usage.
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Introduction

Automatic Weather Stations (AWSs) (Sharan, 2014) are employed as sensing systems in extreme environments, such as desert, Antarctica (Reijmer, 2001), glaciers (Abbate et al., 2013) and deserts (Hobby et al., 2013). An AWS is normally composed of a number of sensors (humidity, wind speed, solar radiation, snow height) connected to a processing unit (CPU and memory). Since AWSs are usually far from mains power sources, they are often powered by batteries and exploit energy harvesting (solar panels or wind turbines, typically) to provide perpetual operation (Priya et al., 2009). AWSs can use communication interfaces, for example WiFi links, Radio bridges, GSM, or satellite links, to implement telemetry (Cesarini et al., 2013).

AWS deployments are built to last, in the order of years. Since after-deployment maintenance is generally difficult, expensive, or even impossible, designing an AWS is a critical task, the requirements being long-lived continuous operation, possibly in any working condition (Box et al., 2004). To address them, designers tend to over-provision energy related sub-systems, although this leads to higher costs and bigger size of the AWS.

Designers should be provided with computer aided design tools to study how hardware choices, e.g., battery and solar panel size, impact on the energy state and evolution of the system (Sabharwal et al., 2013). These class of tools, if used early during the design cycle, can help minimizing AWS design errors. Multiple approaches to analyse energy evolution of wireless systems have been proposed, in particular referred to Wireless Sensor Networks (WSN), which do share some features with AWSs. However, the following important differences hold:

  • AWSs are generally larger and more complex than WSN nodes.

  • The ratio between the energy stored in the battery and the energy spent during operation may be very different (up to two orders of magnitude) between AWSs and WSN nodes.

  • WSN nodes typically have a reduced set of sensors, while an AWS is normally equipped with a broad range of sensors.

  • Even though during the last 10/15 years a large number of research platforms for WSNs have been proposed, only few HW (telos, mica) and SW (TinyOS, Contiki) platforms are currently used in research, with an even lower industrial adoption. Instead, a wider and more heterogeneous panorama of industrial legacy platforms exists for AWSs (Cesarini et al., 2013), motivated by a broader range of different manufacturers, each one delivering it’s own hardware and software.

Considering the exposed differences, we believe that approaches focusing on WSNs cannot be easily adapted to effectively analyse AWSs. Thus, specific tools for AWSs are needed in order to design and install such systems with a higher degree of efficiency and accuracy.

Key Terms in this Chapter

Energy Feasibility Analysis: The analysis aimed at verifying that the designed system meets the previously defined energy requirements.

Automatic Weather Station: A system used in Glaciology or climate science to monitor the weather conditions, often in remote and harsh locations.

Computer Aided Design: The use of software tools to assist the creation, modification, analysis, or optimization of a system.

Embedded Systems: Computing devices embedded within systems whose general goal is different from the simple data elaboration, for example ABS devices in cars.

Stochastic Activity Networks: A stochastic and timed extension of the Petri Nets modeling formalism.

Glaciology: The study of glacier behaviour, in correlation with geology, climate science and global climate evolution.

Telemetry: The technology ensemble needed to transfer remotely acquired sensor data.

Energy Harvesting: A technique and technology to gather energy available from the environment, for example solar or wind energy.

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