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

Facilitating virtual networking for Design for All in Europe: The HERMES platform

This paper reports on HERMES, the virtual networking platform of the European Design for All eAccessibility Network (EDeAN) that was established in 2002 by the European Commission in order to stimulate European network activities within the area of Design for All, with particular focus on ICT and e-Accessibility. The paper reports on the development and utilisation of the networking platform developed to enable the cooperation of EDeAN members through an accessible interface over the World Wide Web. HERMES is available at http://www.edean.or

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

Advancing experimentation-as-a-service through urban IoT experiments

Smart cities are becoming a vibrant application domain for a number of science fields. As such, service providers and stakeholders are beginning to integrate co-creation aspects into current implementations to shape the future smart city solutions. In this context, holistic solutions are required to test such aspects in real city-scale Internet of Things (IoT) deployments, considering the complex city ecosystems. In this paper, we discuss OrganiCity's implementation of an experimentation-as-a-service (EaaS) framework, presenting a toolset that allows developing, deploying, and evaluating smart city solutions in a one-stop shop manner. This is the first time such an integrated toolset is offered in the context of a large-scale IoT infrastructure, which spans across multiple European cities. We discuss the design and implementation of the toolset, presenting our view on what EaaS should provide, and how it is implemented. We present initial feedback from 25 experimenter teams that have utilized this toolset in the OrganiCity project, along with a discussion on two detailed actual use cases to validate our approach. Learnings from all experiments are discussed as well as architectural considerations for platform scaling. Our feedback from experimenters indicates that EaaS is a viable and useful approach.The authors would like to thank the experimenter teams and volunteers who participated in OrganiCit

A proposal for the development of adaptive spoken interfaces to access the Web

Spoken dialog systems have been proposed as a solution to facilitate a more natural human–machine interaction. In this paper, we propose a framework to model the user׳s intention during the dialog and adapt the dialog model dynamically to the user needs and preferences, thus developing more efficient, adapted, and usable spoken dialog systems. Our framework employs statistical models based on neural networks that take into account the history of the dialog up to the current dialog state in order to predict the user׳s intention and the next system response. We describe our proposal and detail its application in the Let׳s Go spoken dialog system.Work partially supported by Projects MINECO TEC2012-37832- C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/ TIC-1485

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

Real-time Edge Analytics for Cyber Physical Systems using Compression Rates Real-time Edge Analytics for Cyber Physical Systems using Compression Rates

Abstract There is a movement in many practical applications of Cyber-Physical Systems to push processing to the edge. This is particularly important were the CPS is carrying out monitoring and control, where the latency between the decision making and control message reception should be minimal. However, CPS are limited by the capabilities of the typically battery powered low resourced devices. In this paper we present a self-adaptive scheme that both reduces the amount of resources required to store high sample rate data at the edge and at the same time carries out initial data analytics. Using out Smart Water datasets, plus a selection from other real world CPS applications, we show that our algorithm reduces computation by 98%; data volumes by 55%; while requiring only 11KB of memory at runtime (including the compression algorithm). In addition we show that our system supports self-tuning and automatic reconfiguration which means that manual tuning is alleviated and the scheme can be both applied to any kind of raw data automatically and is able self-optimize as the nature of the incoming data changes over time

Next generation cyber-physical water distribution systems

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 and energy waste, minimize maintenance costs etc., by incorporating Information and Communications Technologies (ICT). 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, and control water network assets. However, more than 97% of water network assets are found in remote areas, 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 cyber-physical water distribution systems. In this context, this thesis answers the question: "How can the communication be optimized to achieve sustainable Cyber-Physical Systems (CPS) deployed in such harsh environments exploiting limited resources by combining Information, Control, and Communication theory (I2C)? " In order to efficiently utilize underground wireless sensor and actuator network infrastructures, the concepts of edge data processing, anomaly detection and localization, based on compression, stream analyses and graph theory, are introduced. Furthermore, energy optimization and network sustainability by exploiting data-rate and communication scheduling adaptation, based on Lyapunov optimization, is proposed; while the benefits of aperiodic communication are investigated by accommodating event-triggered control technique into smart water networks. In addition to simulations based on real data, WaterBox and BentoBox evaluation platforms were developed to evaluate the proposed algorithms and prove the benefits of event-triggered control and Low Power Wide Area (LPWA) communication technologies against the state-of-the-art solutions. Through theoretical analysis, simulations, and real testbed experiments, the proposed algorithms and systems are shown to outperform contemporary solutions by achieving communication and actuation optimization, data reliability enhancement, while ensuring the sustainable operation of smart water networks. The work presented in this thesis should be of interest to researchers in the emerging areas Cyber-Physical Systems (CPS), Internet of Things (IoT), and Information and Communications Technology (ICT) for smart sustainable cites.Open Acces