Energy is a vital resource for human activities and lifestyle, powering important everyday infrastructures and services. Currently, pollutant and non-renewable sources, such as fossil fuels, remain the main source of worldwide consumed energy. The environmental impact of their exploitation has boosted research and investments in alternative, clean and renewable sources, including photovoltaic and wind-based systems. As a whole, buildings are one of the major energy consumption sectors. Hence, improving energy efficiency in buildings will result in economical and environmental gains. In the case of households, home energy management systems are mainly used for monitoring real-time consumption and to schedule appliance operations so that the energy bill could be minimised, or according to another specific criterion. This work aims to survey the most recent literature on home energy management systems, providing an aggregated and unified perspective in the context of residential buildings. In addition, an updated literature list regarding commonly managed household appliances and scheduling objectives are included. Physical and operational constraints, and how they are addressed by home energy management systems along with security issues are also discussed.

A new methodology for tuning the scaling factors, or gains, of fuzzy proportional-integral-derivative controllers, by taking explicitly into account the closed-loop system performance is proposed in this study. The solution is obtained by solving a nonlinear constrained optimization problem, considering a set of constraints on the scaling factors of the Mamdani-type fuzzy system, and on the plant's inputs and outputs. Two distinct approaches are presented, which are associated with the optimization being carried out offline or in real time. The offline tuning scheme assumes the system dynamics described by a nonlinear model, while for the real-time implementation, the plant's dynamics is locally approximated by a linear model, with the underlying parameters recursively updated. In order to cope with rather stringent sampling time requirements, the constrained online optimization problem is implemented based on the grid computing paradigm. Given the adaptive nature of the real-time scheme, time-varying dynamics and unknown disturbances can be accommodated in such a way that the closed-loop performance is effectively maximized, while avoiding wind-up phenomena induced by the integrator term. The proposed tuning methodologies are assessed on a benchmark three-tank system and compared against a conventional-based tuning approach. Results from experiments illustrate the feasibility of the proposed approaches and also all the relevance in optimal control systems based on Mamdani-type fuzzy controllers.

Cyber-physical systems are nowadays an important component of most industrial infrastructures, and materialise the integration of control systems with advanced information technologies. Commonly, they aggregate distinct communication platforms and networked devices or nodes with different capabilities and goals. The resulting increase in complexity has inevitably brought into play new challenges, namely on the physical world and cyber space. In these systems, the development and gaining of state awareness and how to deal with the inherent vulnerabilities of the overall system are essential and also challenging. In this work, the problem of resilience enhancement in cyber-physical systems is addressed based on a hierarchical multi-agent framework that is implemented over a distributed middleware, in which each agent carries out specific tasks. Physical and cyber vulnerabilities are taken into account, and state and context awarenesses of the whole system targeted. The proposed framework ensures a minimum level of acceptable performance in case of physical disturbances and malicious attacks, as demonstrated by experiments on a IPv6-based test-bed.

Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

Wireless sensor networks (WSNs) have become an important area of research because of their inherent characteristics, such as flexibility, low operational and maintenance costs, and scalability. When dealing with system monitoring in industrial environments, WSNs can be used for detecting and classifying transitory events or be integrated into networked control systems. As such, it is essential that the collected data is reliable, ensuring the quality of received information. A particular case of loss of reliability stems from outliers in raw data collected from the environment through built-in transducers or external transmitters attached to analog-to-digital converter ports. To avoid sending inaccurate data to the base station, it is required to implement a real-time data analysis to be launched at sensor nodes, which takes into account the nodes' natural computing and storage limitations. This brief proposes an outlier detection and accommodation methodology relying on univariate statistics in the form of Shewhart control charts, and formalized through a distributed hierarchical computational entities topology. The proposed scheme is evaluated on a real monitoring scenario implemented in a major oil refinery plant. Results from in situ experiments demonstrate the feasibility and relevance of the proposed approach.