The convergence of recent advances in sensor manufacturing, wireless communications and digital electronics has produced low-cost wireless sensor networks (WSNs) that can be installed for a fraction of the cost of wired devices (see, for example, [1],[3],[4]). WSNs offer unprecedented flexibility ranging from high density sensing capabilities to deployment in areas where wired devices may be difficult or impossible to deploy (such as inside waterways and high-temperature areas in oil refineries). Augmenting existing process control and monitoring systems with additional WSNs has the potential to expand the capabilities of the existing control technology beyond what is feasible with the wired networked architectures alone. Specifically, deploying additional WSNs throughout the plant and interfacing those devices with the existing control systems permit collecting and broadly disseminating additional real-time information about the state of the plant units which in turn can be used to enhance the performance and robustness of the plant operations. The extra information, together with the increased levels of sensor redundancy achieved with WSNs, also enable achieving proactive fault-tolerance and real-time plant reconfiguration based on anticipated market demand changes. These are appealing goals that coincide with the recent calls over the last few years for expanding the traditional process control and operations paradigm in the direction of smart plant operations [2],[6].

To harness the full potential of WSNs in process control, there is a need to address the fundamental challenges introduced by this technology from a control point of view. One of the main challenges to be addressed when deploying a low-cost WSN for control is that of handling the inherent constraints on network resources, including the limitations on the computation, processing and communication capabilities. Other constraints such as limited power (battery energy) are also important when the WSN is deployed in harsh or inaccessible environments where a continuous power supply is not feasible and the wireless devices have to rely on battery power instead [4]. A tradeoff exists between the achievable control performance and the extent of network resource utilization. Specifically, maximizing the control performance requires continuous (or at least frequent) collection of data and disseminating it broadly to the target control systems. On the other hand, the limited resources of a WSN, together with the difficulty of frequent battery replacement in a plant environment, suggest that sensing and communication should be reduced in order to aggressively conserve resources and extend the lifetime of the network as much as possible. Realizing the potential of WSNs to improve process control requires the development of effective strategies for characterizing and managing this tradeoff.

An effort to address this problem was initiated in [5] where a quasi-decentralized networked control architecture was developed for plants with distributed interconnected units that exchange information over a shared communication network. The main idea is to reduce the exchange of information between the local control systems as much as possible without sacrificing stability of the individual units and the overall plant. To this end, dynamic models of the interconnected units are embedded in the local control system of each unit to provide it with an estimate of the evolution of its neighbors when measurements are not transmitted through the network. The use of a model to recreate the interactions of a given unit with one of its neighbors allows the sensor suite of the neighboring unit to send its data in a discrete fashion since the model can provide an approximation of the unit's dynamics. The state of each model is then updated using the actual state of the corresponding unit provided by its sensor suite at discrete time instances to compensate for possible model uncertainty. Finally, the minimum allowable communication frequency is characterized for the case when the update period is constant and the same for all the units. Under this scenario, all sensor suites are given access to the network and can successfully transmit their data simultaneously.

In addition to controlling the transmission frequencies of individual sensors in the network, another important way of conserving the network's energy resources is to select and activate only a subset of the deployed sensor suites at any given time to communicate with a given unit. Under this restriction, the performance of each unit in the plant becomes dependent not only on the controller design but also on the selection of the scheduling strategy that, at any time, determines the order in which the sensor suites of the neighboring units transmit. The scheduling problem is also important in cases where access constraints in the wireless communications medium limit the number of available channels so that at any one time only some of the sensors and actuators can exchange information, while others must wait.

To address this problem, we present in this work a model-based sensor scheduling approach for power resource management in wireless sensor networks. To illustrate the main ideas, we consider as an example the configuration where only one wireless sensor suite is allowed to transmit its measurement updates to the appropriate units at any one time, while the others remain dormant for some time before the next suite is allowed to transmit its data. The time intervals between the transmissions of the different suites can vary but the update period -- i.e., the time interval between two consecutive transmissions of the same sensor suite -- remains the same for all sensor suites. The objective is to find an optimal sequence for establishing and terminating communication between the sensors suites and the target controllers that maximizes the update period (and thus minimizes the overall network utilization) without jeopardizing the required stability and performance characteristics. To this end, we consider a large-scale distributed plant with interconnected processing units, and initially design a quasi-decentralized control structure where each unit has a local control system with its sensors and actuators connected to the local controller through a dedicated communication network, while the local control systems communicate with one another through a wireless sensor network to minimize the propagation of disturbances and meet the overall plant objectives. Embedded in the local control system of each unit is a set of dynamic models that provide an approximation of the interactions between the given unit and its neighbors in the plant when measurements from the neighboring sensor suites are not transmitted through the network. The state of each model is updated using actual measurements from the corresponding unit when communication is re-established. Since the sensor suites of the different units are forced to transmit their data at different times, the models within the local control system of a given unit will be updated at different times according to the chosen transmission schedule.

Exploiting the periodic nature of the transmission schedule, the networked closed-loop plant is formulated as a switched system where the process states evolve continuously while the model forecasting errors are reset to zero at transmission times. This formulation allows obtaining and explicit characterization of the maximum allowable update period in terms of the sensor transmission schedule, the update times of the different sensor suites, the uncertainty in the models as well as the controller design parameters. The results can be used to compare a set of possible transmission schedules to determine the one that leads to the maximum update period and thus results in minimal network resource utilization. The theoretical results are illustrated using a chemical plant example.

References:

[1] Akyildiz, I. F., W. Su, Y. Sankarasubramaniam, and E. Cayirci, ``Wireless sensor networks: a survey," Computer Networks, 38:102--114, 2002.

[2] Christofides, P. D., J. F. Davis, N. H. El-Farra, K. Harris, and J. N. Gibson, ``Smart plant operations: Vision, progress and challenges," AIChE J., 53: 2734-2741, 2007.

[3] Kumar, P. R., ``New technological vistas for systems and control: The example of wireless networks," IEEE Contr. Syst. Mag., 21:24--37, 2001.

[4] Song, J., A. K. Mok, D. Chen, and M. Nixon, ``Challenges of wireless control in process industry," Proceedings of Workshop on Research Directions for Security and Networking in Critical Real-Time and Embedded Systems, San Jose, CA, 2006.

[5] Sun, Y. and N. H. El-Farra, ``Quasi-decentralized model-based control of networked process systems," Comp. \& Chem. Eng., in press.

[6] Ydstie, E. B., ``New vistas for process control: Integrating physics and communication networks," AIChE J., 48:422--426, 2002.