WSDP: Efficient, Yet Reliable, Transmission of Real-Time Sensor Data over Wireless Networks

Honarbacht, A.; Kummert, Anton

doi:10.1007/978-3-540-24606-0_5

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…One can, for example, replace missing samples at the receiver by an estimated value. In [78], a scheme is proposed where the receiver estimates missing values on the basis of Kalman filters. It is demonstrated that by this technique certain signal classes need only 5 out of 100 samples to be able to reconstruct the original signal with good quality, and can be applied at the application layer.…”

Section: Mechanisms To Deal With Channel Errorsmentioning

confidence: 99%

Wireless Technology in Industrial Networks

2005

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Section: Mechanisms To Deal With Channel Errorsmentioning

confidence: 99%

Wireless Technology in Industrial Networks

2005

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“…Enhancements are possible at the application level using Kalman Filters to estimate data when packets are lost [5]. Controlling the transmission power to maintain a preset link gain can dynamically compensate for fading.…”

Section: Performance Under Interferencementioning

confidence: 99%

Performance evaluation of wireless networks for factory automation applications

Anand¹,

Moyne²,

Tilbury³

2009

2009 IEEE International Conference on Automation Science and Engineering

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Recently, there has been interest in applying wireless networking to automation systems to support control, diagnostics and safety functionality in automated manufacturing systems. Multiple standards and technologies are competing for use in these systems. In this paper we study two major wireless standards, IEEE 802.11a,b,g (WiFi), and IEEE 802.15.1-2005 (Bluetooth v1.2) highlighting performance aspects that are relevant to control and automation networks. We demonstrate that these networks can in fact be used for control systems, but discuss some of the factors that must be considered when migrating from a wired to a wireless implementation. For each protocol, we summarize the key facets of the Medium Access Control and Physical layer and present results from performance measurements. Finally, we discuss how these networks can be deployed on the factory floor.

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“…In particular, the use of predictive filtering at information sink nodes in a network has been found useful in overcoming many of the issues related to imprecise sampling and omissions. A variety of methods have been proposed, ranging from the use of state-space dynamic models and Kalman filters (e.g., [26]) through to simpler transfer function dynamic models and ARMA filters (e.g., [14] and the references therein). Although Kalman filtering approaches such as [26] give resilience to unmeasured disturbances, their implementation complexity is high; and as mentioned, a major drawback across all of these methods lies in the assumption that a time-invariant mathematical model of the underlying process and measurement dynamics is available.…”

Section: Packet Loss Compensationmentioning

confidence: 99%

Dependable Control for Wireless Distributed Control Systems

2015

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Abstract:The use of wireless communications for real-time control applications poses several problems related to the comparatively low reliability of the communication channels. This paper is concerned with adaptive and predictive application-level strategies for ameliorating the effects of packet losses and burst errors in industrial sampled-data Distributed Control Systems (DCSs), which are implemented via one or more wireless and/or wired links, possibly spanning multiple hops. The paper describes an adaptive compensator that reconstructs the best estimates (in a least squares sense) of a sequence of one or more missing sensor node data packets in the controller node. At each sample time, the controller node calculates the current control, and a prediction of future controls to apply over a short time horizon; these controls are forwarded to the actuator node every sample time step. A simple design method for a digital Proportional Integral Derivative (PID)-like adaptive controller is also described for use in the controller node. Together these mechanisms give robustness to packet losses around the control loop; in addition, the majority of the computational overhead resides in the controller node. An implementation of the proposed techniques is applied to a case study using a Hardware in the Loop (HIL) test facility, and favorable results (in terms of both performance and computational overheads) are found when compared to an existing robust control method for a DCS experiencing artificially induced burst errors.

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WSDP: Efficient, Yet Reliable, Transmission of Real-Time Sensor Data over Wireless Networks

Cited by 7 publications

References 4 publications

Wireless Technology in Industrial Networks

Wireless Technology in Industrial Networks

Performance evaluation of wireless networks for factory automation applications

Dependable Control for Wireless Distributed Control Systems

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