Event-Driven Sensor Scheduling for Mission-Critical Control Applications

In this paper, we consider a mission-critical control system, where an unstable dynamic plant is monitored by a number of distributed sensors connected to the controller over the wireless fading channels. We focus on the dynamic sensor scheduling to stabilize the unstable dynamic plant. The dynamic sensor scheduling is modeled as a non-convex drift-plus-penalty minimization problem. To improve the scheduling efficiency, the proposed scheme adapts to both the fading channel state as well as the dynamic plant state. To overcome the non-convexity in the minimization problem, we propose a novel transformation technique for the scheduling variables and the objective function (using the Lyapunov drift). Based on that, we can derive a low complexity dynamic sensor scheduling scheme and also obtain a closed-form stability analysis (despite the non-convexity) of the mission-critical control system via a randomized state-independent policy. Compared with various baselines, the proposed scheme has higher power efficiency and superior scalability performance.

[1]  Seksan Laitrakun,et al.  Reliability-Based Splitting Algorithms for Time-Constrained Distributed Detection in Random-Access WSNs , 2014, IEEE Transactions on Signal Processing.

[2]  Ling Shi,et al.  An Opportunistic Sensor Scheduling Solution to Remote State Estimation Over Multiple Channels , 2016, IEEE Transactions on Signal Processing.

[3]  Cunqing Hua,et al.  Co-design of stabilisation and transmission scheduling for wireless control systems , 2017 .

[4]  Ling Shi,et al.  Scheduling Two Gauss–Markov Systems: An Optimal Solution for Remote State Estimation Under Bandwidth Constraint , 2012, IEEE Transactions on Signal Processing.

[5]  Klaus Wehrle,et al.  Finite Blocklength Performance of Cooperative Multi-Terminal Wireless Industrial Networks , 2018, IEEE Transactions on Vehicular Technology.

[6]  Subhrakanti Dey,et al.  Power Allocation for Outage Minimization in State Estimation Over Fading Channels , 2011, IEEE Transactions on Signal Processing.

[7]  Sebastian Trimpe,et al.  Event-Based State Estimation With Variance-Based Triggering , 2012, IEEE Transactions on Automatic Control.

[8]  Mischa Dohler,et al.  Machine-to-Machine (M2M) Communications: Architecture, Performance and Applications , 2015 .

[9]  Pramod K. Varshney,et al.  Sparsity-Promoting Extended Kalman Filtering for Target Tracking in Wireless Sensor Networks , 2012, IEEE Signal Processing Letters.

[10]  Balasubramaniam Natarajan,et al.  Agent based state estimation in smart distribution grid , 2013, 2013 IEEE Latin-America Conference on Communications.

[11]  Vincent K. N. Lau,et al.  Modulation-Free M2M Communications for Mission-Critical Applications , 2018, IEEE Transactions on Signal and Information Processing over Networks.

[12]  Andrea Zanella,et al.  Internet of Things for Smart Cities , 2014, IEEE Internet of Things Journal.

[13]  Michael J. Neely,et al.  Stability and Capacity Regions or Discrete Time Queueing Networks , 2010, ArXiv.

[14]  Jiming Chen,et al.  Optimal Sensor Data Scheduling for Remote Estimation Over a Time-Varying Channel , 2017, IEEE Transactions on Automatic Control.

[15]  Alberto Bemporad,et al.  Decentralized model predictive control of dynamically-coupled linear systems: tracking under packet loss , 2009 .

[16]  Danilo P. Mandic,et al.  An Augmented Extended Kalman Filter Algorithm for Complex-Valued Recurrent Neural Networks , 2006, Neural Computation.

[17]  Hyungsik Ju,et al.  Throughput Maximization in Wireless Powered Communication Networks , 2013, IEEE Trans. Wirel. Commun..

[18]  Ling Shi,et al.  Optimal Periodic Sensor Scheduling With Limited Resources , 2011, IEEE Transactions on Automatic Control.

[19]  Y.-P. Eric Wang,et al.  Analysis of ultra-reliable and low-latency 5G communication for a factory automation use case , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[20]  Jiming Chen,et al.  Design of a Scalable Hybrid MAC Protocol for Heterogeneous M2M Networks , 2014, IEEE Internet of Things Journal.

[21]  Pramod K. Varshney,et al.  Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems , 2013, IEEE Transactions on Signal Processing.

[22]  Anant Sahai,et al.  Design of a low-latency, high-reliability wireless communication system for control applications , 2014, 2014 IEEE International Conference on Communications (ICC).

[23]  Harish Viswanathan,et al.  On resource allocation for machine-to-machine (M2M) communications in cellular networks , 2012, 2012 IEEE Globecom Workshops.

[24]  Xiaoqiang Ren,et al.  Infinite Horizon Optimal Transmission Power Control for Remote State Estimation Over Fading Channels , 2016, IEEE Transactions on Automatic Control.

[25]  Balasubramaniam Natarajan,et al.  Stability of Agent Based Distributed Model Predictive Control Over a Lossy Network , 2015, IEEE Transactions on Signal and Information Processing over Networks.

[26]  José M. F. Moura,et al.  Distributing the Kalman Filter for Large-Scale Systems , 2007, IEEE Transactions on Signal Processing.

[27]  B. Pasik-Duncan,et al.  Adaptive Control , 1996, IEEE Control Systems.

[28]  Robert F. Stengel,et al.  Optimal Control and Estimation , 1994 .

[29]  Andreas Mitschele-Thiel,et al.  Latency Critical IoT Applications in 5G: Perspective on the Design of Radio Interface and Network Architecture , 2017, IEEE Communications Magazine.

[30]  Sneha A. Dalvi,et al.  Internet of Things for Smart Cities , 2017 .