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      Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks

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          Abstract

          Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantages of generality, scalability, and simplicity. Simulation results show that the FLC-QM can provide WSANs with QoS support.

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          MMSPEED: multipath Multi-SPEED protocol for QoS guarantee of reliability and. Timeliness in wireless sensor networks

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            Wireless Sensor/Actuator Network Design for Mobile Control Applications

            Wireless sensor/actuator networks (WSANs) are emerging as a new generation of sensor networks. Serving as the backbone of control applications, WSANs will enable an unprecedented degree of distributed and mobile control. However, the unreliability of wireless communications and the real-time requirements of control applications raise great challenges for WSAN design. With emphasis on the reliability issue, this paper presents an application-level design methodology for WSANs in mobile control applications. The solution is generic in that it is independent of the underlying platforms, environment, control system models, and controller design. To capture the link quality characteristics in terms of packet loss rate, experiments are conducted on a real WSAN system. From the experimental observations, a simple yet efficient method is proposed to deal with unpredictable packet loss on actuator nodes. Trace-based simulations give promising results, which demonstrate the effectiveness of the proposed approach.
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              Prediction-based Dynamic Energy Management in Wireless Sensor Networks

              Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                December 2007
                06 December 2007
                : 7
                : 12
                : 3179-3191
                Affiliations
                [1 ] Faculty of Information Technology, Queensland University of Technology, Brisbane QLD 4001, Australia; E-mail: f.xia@ 123456ieee.org ; y.tian@ 123456qut.edu.au
                [2 ] Precision Engineering Laboratory, Zhejiang University of Technology, Hangzhou 310014, China
                [3 ] State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
                Author notes
                [* ]Author to whom correspondence should be addressed.
                Article
                sensors-07-03179
                10.3390/s7123179
                3841889
                d05d6fb6-7332-4a00-8c18-7c95f9f47760
                © 2007 by MDPI ( http://www.mdpi.org).

                Reproduction is permitted for noncommercial purposes.

                History
                : 30 November 2007
                : 05 December 2007
                Categories
                Full Paper

                Biomedical engineering
                actuator network,fuzzy logic control,wireless sensor,quality of service,adaptive resource management

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