Rule-based technical management for the dependable operation of networked building automation systems

The Internet of Things (IoT) paradigm is gradually finding its way in virtually every industry; however, beyond adding more sensors and measuring and controlling previously inaccessible domains, it is also about transforming ‘legacy’ approaches to control systems, such as those used in Building Management Systems (BMS), by leveraging on the advantages brought by Cyber Physical Systems (CPS). The purpose of this paper is to address several issues gradually emerging in the process of applying the CPS and IoT paradigms to revolutionise BMS. The results of this on-going research aim to help avoid potential pitfalls and provide a sound platform for taking advantage of the benefits brought by this technology in a feasible, effective and controlled manner. More specifically, the paper will address i) the changing meaning of interoperability in the context of the explosion in the number of IoT devices, ii) the need for guidance in adopting sustainable CPS and IoT platforms supporting BMS, based on appropriate non-functional and viable systems principles, iii) emerging issues in the BMS ‘cloudification’ endeavour and iv) the lack of data sources’ correlation resulting in sub-optimal data quality and detail in using Big Data technologies to enable effective analytics for prompt BMS decision-making.

Building Management Systems (BMS) are advancing using wireless Internet of Things (IoT) technologies to develop more intelligent and efficient structures. Systematic innovation is essential to leverage the advantages of IoT while addressing its constraints. The objective of this study is to utilize the ORBIT innovation framework, which includes Opportunity Scanning, Research & Development (R&D), Building Collaborative Ecosystems, Implementation, and Tracking Performance, to analyze current developments in wireless IoT-enabled Building Management Systems (BMS). Approaches: A literature study of Scopus-indexed sources was performed, concentrating on energy efficiency, security, artificial intelligence (AI) integration, and sustainability in IoT-enhanced building management systems (BMS). Literature insights were aligned with the stages of the ORBIT framework to comprehend the innovation process. Findings: A wireless IoT-based Building Management System (BMS) can decrease energy consumption in buildings by approximately 20–30%, cut operational expenses, and enhance occupant comfort. The incorporation of IoT improves security via real-time monitoring, although it also presents cybersecurity and privacy problems. AI-powered control and analytics facilitate intelligent HVAC systems and predictive maintenance, enhancing performance optimization. These advancements promote sustainability by reducing greenhouse gas emissions and facilitating environmental objectives. The ORBIT framework analysis concludes that effective IoT-BMS innovation adheres to a cycle of recognizing opportunities (e.g., energy savings), allocating resources to focused R&D (sensors, protocols, AI algorithms), cultivating partnerships (IT companies, facility managers, stakeholders), conducting pilot implementations, and persistently monitoring results for enhancement. Implementing this systematic methodology can expedite BMS innovation; but persistent issues in cybersecurity, interoperability, and substantial initial expenses necessitate continuous focus. Future study ought to investigate standardized frameworks and coordinated techniques to tackle these problems and completely achieve sustainable, intelligent building management.

Building Automation System (BAS) is a computer-based control system that is widely installed in office buildings and laboratories for monitoring and controlling mechanical/electrical equipment. With the advancements in Cyber-Physical System (CPS) and Internet of Things (IoTs), BAS is in the process of becoming more intelligent by merging computing resources and network communication with physical control. Along with potential benefits, it also brings tremendous risks of security breaches and safety violations, especially when it comes to Programmable Logic Controllers (PLCs). In this paper, we systematically analyze biocontainment laboratory control models based on real case scenarios from Biosecurity Research Institute (BRI) at Kansas State University. We present a vision for a new secure Real-Time Operating System (RTOS) architecture, which leverages various technologies, including microkernel structure, Trusted Platform Module (TPM), proxy-based policy enforcement, and formal verification. The secure RTOS architecture is designed specifically to work with embedded controllers which are widely used in BAS and other CPS to achieve a highly secure and trustworthy control system.

Building Management Systems (BMS) is a set of software and hardware tools that enables the monitoring and the control of building's mechanical and electrical equipment. These systems take advantage of Internet of Things (IoT) and Information Technology (IT) domains to efficiently enhance the buildings management. Blockchain technology is a new emerging technology that has attracted many researchers this last years. Basically, it has many applications in the field of cryptocur-rency, finance and insurances fields. However, these last years, blockchain has known other applications beyond cryptocurrency domain, such as the IoT. In this paper, we shed the light on one specific application of blockchain technology to assist the design of efficient BMS. We propose an hierarchical architecture composed of IoT, fog and cloud layers where the blockchain is integrated on the different layers to enhance the security of the whole BMS systems. We show the main advantages of applying blockchain technology in BMS systems and how our architecture outperforms the existing architectures in terms of low latency, security and privacy.

In the last period, Internet of Things (IoT) is experiencing an increasing development, and the tendency of hosting applications and especially data, in cloud computing systems is growing. Currently, most of the Building Management System (BMS) analyse directly data or transfer it to the cloud.In this context, the proposed solution aims to read indoor thermal climate data from cloud computing platform and based on them operate a hydraulic motor. The input data could be generated by an in-house sensor monitoring solution or by a foreign BMS system that transmits open source data to cloud.In this study some conditions are imposed to the input data and as a result, control current is generated and transmitted in order to command proportional electro-hydraulic valve that drive a hydraulic motor. The electro-hydraulic valve is controlled with the help of an amplifier, by a data acquisition board (DAQ) that runs LabView application. With the help of the developed software, the motor speed and the direction of rotation can be controlled.In last years, the number of requests for architectures projects with moveable elements has increased because they multiply the possible use of the buildings. With the information generated by the sensors that measure the indoor climate parameters, the hydraulic motor could be used in such a construction. Examples of atypical buildings with special functionalities that the architects would propose and that needs high torque actuation are presented in this paper.

Buildings are subject to faults in their heating, ventilation and air-conditioning (HVAC) systems that can lead to excessive energy wastage, poor indoor climate, equipment failures and high maintenance costs. Field studies have shown that employing fault detection, diagnosis and prognosis (FDDP) tools followed up with equipment services and corrections can help achieve up to 40% of energy savings within the HVAC system and improve indoor climate, increase equipment lifecycle and reduce maintenance costs. The increasing adoption of building automation systems (BAS), Internet of Things (IoT) and other smart technologies in recent years have allowed large amounts of data to be continuously collected from building systems. This data-rich environment, along with the surge in data analytics and machine learning tools, has made cost-effective data-driven FDDP strategies possible. Compared to purely physics-based methods, data-driven methods require less explicit knowledge of the underlying physical system, thus are often easier to develop, and can learn certain intricate relationships that exist among data. Within the reported data-driven FDDP methods, there exists a few research gaps: 1) data imputation methods that leverage mutual information from correlated measurements to defy poor data quality from BAS have not been utilized efficiently; 2) there lacks a systematic and scalable fault diagnosis framework that incorporates probabilistic temporal relationships to track fault evolution; 3) existing fault diagnosis strategies typically focus on traditional rule-based control strategies and their scalability for advanced control strategies such as Guideline 36 have not been explored yet; 4) active threats information such as cyber-attacks, are typically not incorporated in an FDDP framework; 5) fault prognosis strategies to preemptively identify gradual faults for predictive maintenance have rarely been studied. This research attempts to address the above-mentioned research gaps through the following: Data Imputation: reported data imputation methods that are suitable for handling and repairing multi-source BAS data are evaluated. Data collected from a medium-sized, mixed-use institution building situated in Stockholm, Sweden and a small commercial building simulated in a laboratory setup is used to evaluate five different data imputation methods. Results demonstrate that incorporating time-lagged cross-correlations within the k-Nearest Neighbor (kNN) method helps to significantly improve the imputation accuracy and minimize the impact of repaired data on data-driven algorithms. Dynamic Bayesian Network (DBN)-based Framework for Cyber-Physical Fault Diagnosis: a DBN framework with discretized conditional probabilities parameters to represent the temporal relationships among building measurements is developed. Both domain knowledge and machine learning methods are used to develop the DBN structure and parameter model. The developed framework is evaluated for both traditional rule-based and Guideline 36 controls using datasets from a real building, a laboratory building, and a virtual testbed. Results show that the developed DBN framework is effective in diagnosing and isolating faults in systems even with different control strategies. The framework also successfully distinguishes whether system abnormalities originate from cyber-attacks or naturally occurring physical faults. Potential future direction to improve fault isolation using modified DBN topological structure is also reported in this study. DBN-based Framework for Fault Prognosis: an extension of the DBN framework in conjunction with Robust Multivariate Temporal (RMT) variate selection is proposed for fault prognosis. The RMT variate selection is used to extract localized temporal features from high dimensional datasets to determine the best inputs for training forecasting models. The expected fault-free behavior of multiple target variates, selected using domain knowledge, is forecasted using incoming data. The prediction errors generated from the forecasting phase are used as evidences in the DBN inference to estimate future fault probabilities. Gradual faults simulated in the virtual testbed are used to evaluate the prognosis framework. Results show that the developed framework is effective in prognosing gradual faults by leveraging the trending growth on the prediction errors. The research presented in this thesis contributes to the overall objective of developing a robust and cost-effective DBN-based framework for fault diagnosis and prognosis of building HVAC systems. Potential solutions to other existing challenges of implementing data-driven FDDP strategies, such as obtaining high-quality datasets, handling and repairing missing data, establishing a baseline model for detecting abnormalities despite other disturbances such as weather and internal conditions changes, and extracting temporal features from timeseries data are also examined.

The indoor safe area of a building is important to the health and well-being of people. All buildings have some sort of mechanical, electrical, and plumbing services such as coolant, pump, and water heater to provide a comfortable environment inside the building. These services must be managed in some way to ensure a comfortable environment. Building Management System (BMS) provides a comfortable working environment reliably. In this article, we propose effective Internet of Things (IoT) applications, which are used to monitor everyday home environments with inexpensive ubiquitous sensor systems. The parameters for using smart sensors and transmitting data over the internet provide a self-monitoring mechanism to improve unit performance in the monitoring phase. The structure of the monitoring system is based on a combination of widely used distributed sensors, data aggregation, and information systems for inference and situational awareness. The results are encouraging considering the reliability of the sensing information transmission through the proposed unified network architecture using Amazon Web Services (AWS) Cloud services for smart predictive systems.

This study aims to design, evaluate, test, and assess a prototype Internet of Things (IoT)-based building management system for air conditioning (AC) control in a campus building. The research background stems from the need for a system that can manage temperature more effectively, aligning with the demands of sustainability in energy management. The methodology employed in this study involves the development of a system using an ESP32 microcontroller, integrated with an Infrared (IR) transmitter and an LDR sensor. Testing was conducted to evaluate the performance of the IR transmitter in detecting signals at various distances (1 to 5 meters) and directions (straight, left, and right), as well as to measure the accuracy of the LDR sensor at a distance of 2 meters. The test results revealed that the IR transmitter achieved optimal detection performance at a distance of 1 meter, with significant degradation at greater distances. Conversely, the LDR sensor demonstrated 100% accuracy at a distance of 2 meters. The study concludes that the IoT-based AC control system designed not only enhances temperature management efficiency but also improves user comfort, with the potential for broader application in building management systems in the future.

With smart and connected communities emerging as an important facet of the Internet of Things (IoT) paradigm, next generation Building Automation Systems (BAS) become evident as one of their prime offerings. The performance of BAS is highly dependent on how well a building does on the fronts of energy efficiency and indoor Quality of Experience (QoE) provided to the users. Higher QoE or comfort expectations impose heavy demands on overall energy consumption of buildings. The inherent conflict in the objectives, namely, maximizing of QoE for users while minimizing the energy costs incurred in the process, is recognised as a complex problem and hence the intrinsic details in its mathematical formulation need attention. This paper presents a survey of energy cost and QoE optimization strategies proposed in recent years with emphasis on the objectives and modelling of the multi-objective optimization problem. A brief summary of the tools and techniques used to solve the optimization problem is also presented. A few research directions are also provided based on the findings of the survey.

This chapter presents an overview of energy-harvesting (EH) methods that can be used for future Internet of Things (IoT) applications. Wireless sensors in applications such as healthcare systems, automobiles, and building automation systems can be powered by deriving electric energy from the surroundings such as solar energy, mechanical vibration energy, heat energy, and wind energy. Traditionally, these wireless sensors are powered by batteries. But, battery-powered wireless sensors have some critical problems like lower life span and higher battery replacement cost. In some harsh environmental applications, even replacement of batteries at regular intervals of time is very difficult. There comes the need for EH systems. Wireless sensor networks are the basic units of majority of IoT applications. This chapter describes various EH systems that can be used in future IoT applications.

The purpose of this paper was to present an assessment of a series of instructionalactivities that were developed and implemented to improve the basic knowledge level of studentsentering technical systems management undergraduate level classes. Many of the students enrollingin the technical systems management classes do not have the same level of fundamentalunderstanding of the basic principles and knowledge level in technical systems management. Aseries of instructional activities were developed to provide students opportunities to learn thefundamental knowledge as they star and progress through the classes. These instructional activitieswere primarily web-based and offered as supplemental instructional activities that did not requireformal instructional time. The activities were self-paced and student-centered. These activitiesincluded instructional topics such as tool and components identification, safety practices,fundamental scientific principles and applications of instructional practices. All activities arecompleted outside of the formal class and laboratory teaching times. The primary goal of theseactivities was to provide an opportunity for those students who do not have the expected entry levelof fundamental knowledge to develop a baseline of competence.

One of the main concerns about implementing networked automation systems is ensuring its security against cyber attacks. In this paper, we consider networked automation systems abstracted as Discrete-Event Systems (DES), and consider cyber attacks where a malicious agent eavesdrops a network communication channel with the objective to gather information about the system behavior. It is important to remark that network security strategies used in Information Technology (IT) cannot be straightforwardly used in industrial networks, since the control of automation systems usually requires small communication delays, which limits the size of the data that can be transmitted in the communication network. In this paper, we introduce a new cryptographic scheme based on events, called event-based cryptography, where an event is defined as any change in the binary signals transmitted in the channel. We also present a necessary and sufficient condition that the event-based encryption function must satisfy to be used in the cryptographic scheme proposed in this paper, and present a class of encryption functions that can be modeled by Mealy automata. We also present procedures for the implementation of the event-based encryption function, and illustrate all results with a practical example.

This chapter explores how smart cities can enhance building management through technologies like the Internet of Things (IoT) and advanced predictive models, focusing on energy efficiency and air quality. The escalating reliance on technology as the primary solution to contemporary and future challenges has highlighted Internet of Things (IoT), digitalization, and machine learning, among others, as new methodologies for assessing management in smart cities. Moreover, in the realm of defining innovative building management systems, pressing issues such as climate change and pandemic episodes like COVID-19 underscore the need to prioritize energy efficiency and air quality. This imperative has led to the emergence of digital twins, a technology integrating 3D models with real-time data, enabling a comprehensive understanding of building dynamics. In addition, automated prediction models leveraging advanced statistical and machine learning techniques contribute significantly to enhancing climatization control, energy efficiency, and air quality management. These predictive models analyze historical data, enabling accurate forecasts to assess future behavior, which is crucial for effective maintenance planning. The application of linear and non-linear regression models, alongside techniques like Support Vector Machines and neural networks, further refines predictions. Additionally, real-time monitoring and decision algorithms optimize information transmission during incidents, ensuring a rapid response to environmental factors or anomalies, thereby mitigating risks and maximizing operational efficiency.

In order to put into practice the objectives proposed by the policy of energy transition towards systems and buildings increasingly electrified, smart and sustainable, the massive digitization of environments and systems in both residential and commercial plays a key role, in view of the 'internet of things'. This paper aims to demonstrate the possibility of creating a reliable and robust building automation and monitoring system, maintaining, at the same time, a character of cost-effectiveness essential for a massive spread of these technologies. A case study has been realized by taking as reference a typical home in which smart devices have been placed, aimed at equipment controlling and widespread energy consumption monitoring, both electrical and thermal. The main environmental parameters influencing energy processes are also monitored, such as: position of the windows (open/closed), temperature and humidity of the individual areas air, levels of illuminance and the presence of people in the rooms and in the entire building. These elements were then integrated into the open-source SCADA (or better personal hub) "Home Assistant" and made available on a very simple dashboard, locally accessible through any web browser. As we will see, the complete low-cost smartization of the house brings a considerable advantage on energy savings, as well as an obvious greater comfort for users.

This study investigates how South African hotels incorporate smart technologies into Environmental Management Accounting (EMA) practices to enhance environmental accountability and sustainable management. Owing to the high consumption of energy, water, and other natural resources, the hospitality industry is under increasing pressure to reduce its environmental footprints. The EMA offers a mechanism for hotels to embed sustainability into their decision making. However, the integration of EMA with digital technologies remains underexplored in South Africa, an emerging economy. A qualitative multiple-case study design was employed, involving 27 semi-structured interviews with key hotel stakeholders across 3–5-star hotels in the KwaZulu-Natal and Gauteng provinces. Thematic analysis guided the identification of patterns and themes. The findings revealed that digital tools, such as Internet of Things (IoT) sensors, building management systems, and real-time dashboards, are increasingly being used to monitor environmental performance. When integrated with operational and financial workflows, these tools enhance decision making, reduce costs, and promote a sustainable culture. However, limited interoperability with legacy systems, high implementation costs, and insufficient technical expertise have been identified as the key barriers. Smart technologies have the potential to transform the EMA in the hotel sector, but their success hinges on organisational alignment, skill development, and supportive governance structures.