Hindawi Publishing Corporation Journal of Electrical and Computer Engineering Volume 2012, Article ID 762927, 14 pages doi:10.1155/2012/762927 Research Article An Adaptive Amplifier System for Wireless Sensor Network Applications M´ onica Lovay, 1 Gabriela Peretti, 1, 2 Eduardo Romero, 1, 2 and Carlos Marqu´ es 2 1 Mechatronics Research Group, Facultad Regional Villa Mar´ ıa, Universidad Tecnol´ ogica Nacional, Avenida Universidad 450, 5900 Villa Mar´ ıa, Argentina 2 Electronics and Instrumentation Development Group, Facultad de Matem´ atica, Astronom´ ıa y F´ ısica, Universidad Nacional de C´ ordoba, Medina Allende S/N, 5000 C´ ordoba, Argentina Correspondence should be addressed to Gabriela Peretti, gabi peretti@yahoo.com.ar Received 22 December 2011; Revised 13 February 2012; Accepted 27 February 2012 Academic Editor: Jose Silva-Martinez Copyright © 2012 M ´ onica Lovay et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper presents an adaptive amplifier that is part of a sensor node in a wireless sensor network. The system presents a target gain that has to be maintained without direct human intervention despite the presence of faults. In addition, its bandwidth must be as large as possible. The system is composed of a software-based built-in self-test scheme implemented in the node that checks all the available gains in the amplifiers, a reconfigurable amplifier, and a genetic algorithm (GA) for reconfiguring the node resources that runs on a host computer. We adopt a PSoC device from Cypress for the node implementation. The performance evaluation of the scheme presented is made by adopting four different types of fault models in the amplifier gains. The fault simulation results show that GA finds the target gain with low error, maintains the bandwidth above the minimum tolerable bandwidth, and presents a runtime lower than exhaustive search method. 1. Introduction The advances in electronics have enabled the development of low cost, low power, and multifunctional wireless sensor nodes that consist of sensing, data processing, and commu- nication components [1]. These small sensor nodes can be installed in a designated area to form a wireless network for performing specific functions. Usually, a host computer col- lects data from the sensors and carries out different actions depending on the particular purpose of the system. A broad range of applications has been proposed for this kind of systems such as industrial sensor networks, environmental monitoring, home automation, and medical care [2]. The processing and communication units typically found in wireless sensor nodes can be implemented with microcon- trollers (μCs). These offer benefits like low cost and power consumption, ability to perform data processing tasks in the nodes, and usually powerful communication interfaces. In addition, some modern μCs offer a wide pool of configurable digital and analog sections that enhance the node adaptation to a broad range of applications. In a number of applications, the nodes operate in harsh environments, under the action of a several agents that could potentially deteriorate their performances. If the applica- tion is critical, reliable operation of the node can require characteristics of safe operation, adaptation to a changing environment, or ability for compensating degradations in its own circuitry. For achieving this purpose, two related characteristics are necessary: fault detection and circuit self- adaptation. The fault detection characteristic could be constrained by the low power operation of the node, which could make the use of dedicated test circuitry inconvenient for performing built-in self-test. Instead, a software-based self-test (SBST) strategy arises as an effective alternative that can provide in- field testing capabilities with very low area and performance overhead [3]. Particularly suitable for μCs, an SBST strategy utilizes the existing processing core to perform a self-test of the analog and digital components in a node [4, 5]. Providing adaptive characteristics to the node requires configurable hardware sections and a reconfiguration meth- odology. Evolvable hardware (EHW) is a methodology that