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      The Benefits of Soft Sensor and Multi-Rate Control for the Implementation of Wireless Networked Control Systems

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          Abstract

          Recent advances in wireless networking technology and the proliferation of industrial wireless sensors have led to an increasing interest in using wireless networks for closed loop control. The main advantages of Wireless Networked Control Systems (WNCSs) are the reconfigurability, easy commissioning and the possibility of installation in places where cabling is impossible. Despite these advantages, there are two main problems which must be considered for practical implementations of WNCSs. One problem is the sampling period constraint of industrial wireless sensors. This problem is related to the energy cost of the wireless transmission, since the power supply is limited, which precludes the use of these sensors in several closed-loop controls. The other technological concern in WNCS is the energy efficiency of the devices. As the sensors are powered by batteries, the lowest possible consumption is required to extend battery lifetime. As a result, there is a compromise between the sensor sampling period, the sensor battery lifetime and the required control performance for the WNCS. This paper develops a model-based soft sensor to overcome these problems and enable practical implementations of WNCSs. The goal of the soft sensor is generating virtual data allowing an actuation on the process faster than the maximum sampling period available for the wireless sensor. Experimental results have shown the soft sensor is a solution to the sampling period constraint problem of wireless sensors in control applications, enabling the application of industrial wireless sensors in WNCSs. Additionally, our results demonstrated the soft sensor potential for implementing energy efficient WNCS through the battery saving of industrial wireless sensors.

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          A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends

          The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed.
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            Networked Control System: Overview and Research Trends

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              Energy-Efficient Sensing in Wireless Sensor Networks Using Compressed Sensing

              Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                December 2014
                18 December 2014
                : 14
                : 12
                : 24441-24461
                Affiliations
                [1 ] Group of Automation and Integrated Systems, Univ. Estadual Paulista—UNESP, Av. Três de Março 511, Sorocaba 18087-180, Brazil; E-Mail: rkmansano@ 123456yahoo.com.br
                [2 ] Department of Mechanical Engineering, University of São Paulo—USP at São Carlos, Av. Trabalhador São Carlense 400, São Carlos 13566-590, Brazil; E-Mail: ajvporto@ 123456sc.usp.br
                Author notes

                External Editor: Xue Wang

                [* ] Author to whom correspondence should be addressed; E-Mail: epgodoy@ 123456sorocaba.unesp.br ; Tel.: +55-15-3238-3417.
                Article
                sensors-14-24441
                10.3390/s141224441
                4299119
                25529208
                b7130490-db2c-4cde-8ec7-e62995040333
                © 2014 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 01 October 2014
                : 07 November 2014
                : 17 November 2014
                Categories
                Article

                Biomedical engineering
                soft sensor,mathematical model,wireless sensors,energy efficiency
                Biomedical engineering
                soft sensor, mathematical model, wireless sensors, energy efficiency

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