Evaluation of Decentralized Event-Triggered Control Strategies for Cyber-Physical Systems

Energy constraint long-range wireless sensor/ actuator based solutions are theoretically the perfect choice to support the next generation of city-scale cyber-physical systems. Traditional systems adopt periodic control which increases network congestion and actuations while burdens the energy consumption. Recent control theory studies overcome these problems by introducing aperiodic strategies, such as event trigger control. In spite of the potential savings, these strategies assume actuator continuous listening while ignoring the sensing energy costs. In this paper, we fill this gap, by enabling sensing and actuator listening duty-cycling and proposing two innovative MAC protocols for three decentralized event trigger contro l approaches. A laboratory experimental testbed, which emulates a smart water network, was modelled and extended to evaluate the impact of system parameters and the performance of each approach. Experimental results reveal the predominance of the decentralized event-triggered control against the classic periodic control either in terms of communication or actuation by promising significant system lifetime extension

WaterBox: A Testbed for Monitoring and Controlling Smart Water Networks

Copyright 2015 ACM.Smart water distribution networks are a good example of a large scale Cyber-Physical System that requires monitoring for precise data analysis and network control. Due to the critical nature of water distribution, an extensive simulation of decision making and control algorithms are required before their deployment. Although some aspects of water network behaviour can be simulated in software such as hydraulic responses in valve changes, software simulators are unable to include dynamic events such as leakages or bursts in physical models. Furthermore, due to safety concerns, contemporary large-scale testbeds are limited to the monitoring processes or control methods with well established safety guarantees. Sophisticated algorithms for dynamic and optimal water network reconfiguration are not yet widespread. This paper presents a small-scale testbed, WaterBox, which allows the simulation of emerging/advanced monitoring and control algorithms in a fail-safe environment. The flexible hydraulic, hardware, and software infrastructure enables a substantial number of experiments. On-going experiments are related to in-node data processing and decision making, energy optimization, event-driven communication, and automatic control

Energy-based Adaptive Compression in Water Network Control Systems

© 2016 IEEE.Contemporary water distribution networks exploit Internet of Things (IoT) technologies to monitor and control the behavior of water network assets. Smart meters/sensor and actuator nodes have been used to transfer information from the water network to data centers for further analysis. Due to the underground position of water assets, many water companies tend to deploy battery driven nodes which last beyond the 10-year mark. This prohibits the use of high-sample rate sensing therefore limiting the knowledge we can obtain from the recorder data. To alleviate this problem, efficient data compression enables high-rate sampling, whilst reducing significantly the required storage and bandwidth resources without sacrificing the meaningful information content. This paper introduces a novel algorithm which combines the accuracy of standard lossless compression with the efficiency of a compressive sensing framework. Our method balances the tradeoffs of each technique and optimally selects the best compression mode by minimizing reconstruction errors, given the sensor node battery state. To evaluate our algorithm, real high-sample rate water pressure data of over 170 days and 25 sensor nodes of our real world large scale testbed was used. The experimental results reveal that our algorithm can reduce communication around 66% and extend battery life by 46% compared to traditional periodic communication techniques

Evaluation of Decentralized Event-Triggered Control Strategies for Cyber-Physical Systems

Energy constraint long-range wireless sensor/ actuator based solutions are theoretically the perfect choice to support the next generation of city-scale cyber-physical systems. Traditional systems adopt periodic control which increases network congestion and actuations while burdens the energy consumption. Recent control theory studies overcome these problems by introducing aperiodic strategies, such as event trigger control. In spite of the potential savings, these strategies assume actuator continuous listening while ignoring the sensing energy costs. In this paper, we fill this gap, by enabling sensing and actuator listening duty-cycling and proposing two innovative MAC protocols for three decentralized event trigger contro l approaches. A laboratory experimental testbed, which emulates a smart water network, was modelled and extended to evaluate the impact of system parameters and the performance of each approach. Experimental results reveal the predominance of the decentralized event-triggered control against the classic periodic control either in terms of communication or actuation by promising significant system lifetime extension

Reliability or Sustainability: Optimal Data Stream Estimation and Scheduling in Smart Water Networks

As a typical cyber-physical system (CPS), smart water distribution networks require monitoring of underground water pipes with high sample rates for precise data analysis and water network control. Due to poor underground wireless channel quality and long-range communication requirements, high transmission power is typically adopted to communicate high-speed sensor data streams, posing challenges for long-term sustainable monitoring. In this article, we develop the first sustainable water sensing system, exploiting energy harvesting opportunities from water flows. Our system does this by scheduling the transmission of a subset of the data streams, whereas other correlated streams are estimated using autoregressive models based on the sound-velocity propagation of pressure signals inside water networks. To compute the optimal scheduling policy, we formalize a stochastic optimization problem to maximize the estimation reliability while ensuring the system’s sustainable operation under dynamic conditions. We develop data transmission scheduling (DTS), an asymptotically optimal scheme, and FAST-DTS, a lightweight online algorithm that can adapt to arbitrary energy and correlation dynamics. Using more than 170 days of real data from our smart water system deployment and conducting in vitro experiments to our small-scale testbed, our evaluation demonstrates that Fast-DTS significantly outperforms three alternatives, considering data reliability, energy utilization, and sustainable operation

Highly-Distributed Sensor Processing using IoT for Critical Infrastructure Monitoring

Highly-distributed signal processing for critical monitoring infrastructures has been a main research topic over the last decade. Under this context, we show the three phases of the joint "Cyber-physical control system" project; a collaboration between Imperial College London and NEC Corp. Japan. First, the implementation of edge processing with multiple tasks including data mining and communication, developed on a lightweight single core low-powered MCU system is presented. This algorithm has been effectively customized to be implemented on resource-constrained embedded systems. The developed sensor network is coupled with a low-powered wide range LoRa platform for transmission of the minimized payload. The work explores the node-to-node communication limitations and discusses how edge processing can be used for water network control and we present the overview of a Cyber-physical control system which is concerned with the event-triggered control of a water network. Finally, the results of the LoRa communication tests are given

Accurate models of energy harvesting for smart environments

Over the last decade, the energy optimization of resource constrained sensor nodes constitutes a major research topic in smart environments. However, state of the art energy optimization algorithms make strong and unrealistic assumptions of energy models, both in simulations and during the operation of smart systems. For instance, simplistic energy models for energy harvesting leads to inaccurate representation and prediction of the true dynamics of energy. Consequently, systems for smart environments are unable to meet expected performance criteria. In this paper, we propose innovative models to overcome the drawbacks of simplistic energy representations in smart environments. We provide the insights of how to generate precise lightweight energy models. Using the physical properties of solar and flow energy harvesting as case studies, the trade-off between energy harvesting inference and real-time measurement of energy generation is explored. To evaluate our proposed energy models against the simplistic versions, we use real measured data from our environmental micro-climate monitoring deployment in an urban park and a 103% improvement is seen. Additionally, to define the trade-offs between inferred and measured energy generation, experiments are conducted utilizing solar and smart water testbeds

Adaptive Edge Analytics for Distributed Networked Control of Water Systems

© 2016 IEEE.Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas; facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented

Communication schemes for centralized and decentralized event-triggered control systems

Energy constraint long-range wireless sensor/actuator-based solutions are theoretically the perfect choice to support the next generation of city-scale cyber-physical systems. Traditional systems adopt periodic control which increases network congestion and actuations while burdens the energy consumption. Recent control theory studies overcome these problems by introducing aperiodic strategies, such as event-triggered control (ETC). In spite of the potential savings, these strategies assume actuator continuous listening, while ignoring the sensing energy costs. In this paper, we fill this gap, by enabling sensing and actuator listening duty cycling and proposing two innovative medium access control protocols for three decentralized ETC approaches. A laboratory experimental test bed, which emulates a smart water network, was modeled and extended to evaluate the impact of system parameters and the performance of each approach. Experimental results reveal the predominance of the decentralized ETC against the classic periodic control either in terms of communication or actuation by promising significant system lifetime extension