Framework for the Simulation of Sensor Networks Aimed at Evaluating In Situ Calibration Algorithms

Abstract

The drastically increasing availability of low-cost sensors for environmental monitoring has fostered a large interest in the literature. One particular challenge for such devices is the fast degradation over time of the quality of their data. Therefore, the instruments require frequent calibrations. Traditionally, this operation is carried out on each sensor in dedicated laboratories. This is not economically sustainable for dense networks of low-cost sensors. An alternative that has been investigated is in situ calibration: exploiting the properties of the sensor network, the instruments are calibrated while staying in the field and preferably without any physical intervention. The literature indicates there is wide variety of in situ calibration strategies depending on the type of sensor network deployed. However, there is a lack for a systematic benchmark of calibration algorithms. In this paper, we propose the first framework for the simulation of sensor networks enabling a systematic comparison of in situ calibration strategies with reproducibility, and scalability. We showcase it on a primary test case applied to several calibration strategies for blind and static sensor networks. The performances of calibration are shown to be tightly related to the deployment of the network itself, the parameters of the algorithm and the metrics used to evaluate the results. We study the impact of the main modelling choices and adjustments of parameters in our framework and highlight their influence on the results of the calibration algorithms. We also show how our framework can be used as a tool for the design of a network of low-cost sensors.

Rendez-Vous Based Drift Diagnosis Algorithm for Sensor Networks Toward In Situ Calibration

“…it does not drift. The instruments of class zero all follow a random gain and offset increase (RGOI) drift model [42]. Gain G(s i , t) and offset O(s i , t) drift of instrument s i are computed at each time step following:…”

Simulating Defects in Environmental Sensor Networks Using Stochastic Sensor Models

“…In order to generate and assess such algorithmic approaches for chemiresistive gas sensors, however, simulation data exploring different sensor network scenarios are necessary. For the specific case of sensor drift, such frameworks have already been investigated to evaluate calibration algorithms, e.g., in [3]. Therefore, in order to study other fault types, we want to present a framework based upon a stochastic sensor simulator [4], which can provide sensor network simulations specifically feasible for a number of different sensor defects.…”