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IoT Architecture for Preventive Energy Conservation of Smart Buildings
Abstract
With the advent of the Internet of Things, we can connect almost any device with each other and share information easily. This data can be further used in estimation, predictions, quantification, and molded into useful information for the synthesis of better responsive devices which we in modern terms quote as “Smart”. Buildings which are forefront of any economy setup are also influenced by this innovative idea and it is capable of integrating its traditional chores with a revamped appearance. However, this transition demands feasible and pragmatic solutions which not only aim at bringing the required change, but also frame the structure to accustom the work in technology for instrumental and worthwhile service. In this chapter, we scrutinize the various architectures and frameworks currently involved with the smart building approach and also examine their utility, outcomes, and prospects.
... [1]. Smart homes feature advanced security, remote monitoring, a smart environment, health tracking, and smart home appliances [2]. Wireless home automation system technologies include GSM, Bluetooth, ZigBee, wi-fi based, etc. [3]. ...
... Voice activated assistants [4], sensors [5], programmable controllers [6] and wireless home automation system [3] together all makes the automation system for smart homes. Energy management, security management, entertainment management, cooling management, and other automation system applications are used in smart homes [2]. Interoperability, which will allow for simple integration with the current internet of various smart gadgets is also a challenge [7]. ...
... Energy-efficient smart homes refer to homes that use technology to reduce energy expenditure and improve energy performance while providing comfort and convenience to its occupants [2]. House energy management system also plays an critical contribution in saving energy at intelligent house [44]. ...
An intelligent house automation system is a technology-driven solution that allows homeowners to control and automate various home devices and systems using a central hub using smartphone, voice command and IoT. The system typically includes equipment such as smart thermostats, smart lighting, smart locks, security systems, and entertainment systems. With the increasing prevalence of IoT, smart homes have become increasingly popular, and automation systems have emerged as a key technology for controlling and managing various home appliances and systems. This note explores an exhaustive detail of the present state of automation systems for smart homes, and aims to identify areas for future research and development along with comfort, energy efficiency, privacy, and accessibility that a smart home automation system offers users can enhance their life as a whole. The current paper also highlights the components of smart home automation system but also put a spotlight on application and challenges associated with it.
... Leading the charge in this transformation is the introduction of the IoT into architectural design, which holds the potential to make buildings dynamic entities that can accurately adjust to changing conditions and human demands instead of being static designs [1]. There are various potentials to improve energy efficiency, sustainability, and occupant comfort in this evolving paradigm where architecture and cutting-edge technology come together [2]. It also presents a variety of challenges and uncharted territory in the context of sustainable design, which is crucial to consider in an era where environmental concerns and the demand for eco-friendly, energy-efficient solutions are critical. ...
The application of Internet of Things (IoT) technology is a significant step toward improving the sustainability and responsiveness of the built environment. The current work introduces the Adaptive and Sustainable IoT Integration Model (ASIIM), a novel framework designed to enhance the dynamic sustainability and adaptation of IoT in architecture in order to maximize its potential. Through a comprehensive review of the literature, this study analyzes the current state of IoT applications in architectural professions, highlighting the key benefits of IoT in improving building performance, occupant comfort, and energy efficiency. The ASIIM framework emerges as a comprehensive approach that encompasses key strategies for combining user interface systems, sustainability measures, IoT-enabled adaptive features, and fundamental design principles in order to promote a more responsive and sustainable architecture design. The paper identifies key obstacles to IoT integration, such as interoperability, data protection, and device sustainability, and offers collaborative ways to overcome them. The findings demonstrate the transformative potential of IoT in architecture, suggesting a future in which buildings will become dynamic systems that can adapt to the needs of both the surrounding environment and their occupants rather than static structures. This study contributes to the expanding body of knowledge on sustainable architecture by offering insights and a theoretical foundation for further study and practice in the integration of IoT technology in architectural design.
... transportation management utilize the IoT sensors in the network. The role of the sensors is to gather information on a real-time basis, and gathered information is used for feeding the subsystems of the network, like data processors and communication channels [3][4][5]. Thus, the operation of the ITS requires enormous data for modelling the data-driven techniques for enhancing the efficiency of the process. Ships, aeroplanes, track trucks, or public transportation services are improved through better traffic management plans using the gathered information from real-time data. ...
Demand-based public bus service meets the need of passengers with less money, time, and resources by reducing the number of private vehicles on the road. In contrast, dynamic real-time demand-based routing faces challenges like elevated travel time due to the requested assignment based on the paths and vehicle availability. Hence, this research introduces a novel framework named Passenger Influence Bus Service-Intelligent Public Transport System (PIBS-IPTS) for efficient routing of available vehicles based on the demand of passengers. For this, optimal paths are elected from the known routes of the general vehicle through the Cuckoo Search (CS) optimization algorithm. Then efficient route prediction is employed by the Artificial Neural Network (ANN) for passenger flow. Here, the unavailability of the passenger request, such as source location or Destination locations, or the unavailability of both locations is updated while employing the path generation process. The path generation process ensures the reduction of request drops generated by the passenger, which elevates the usage of the general bus service. Here, for the optimal selection of routes from the identified routing paths, a multi-objective function based on traffic density, route condition, and route mobility is employed for the selection of a near-optimal global solution. The method's performance is analyzed using MAE, RMSE, and MAPE and obtained the best values of 0.69, 0.72, and 0.74, respectively.
... Operators working in the Central Control Room (CCR) in industry must have adequate knowledge and competencies about DCS and SCADA. They are responsible for monitoring and controlling instruments at the Field Devices level, such as sensors and actuators, through computers at the Remote Stations level (Ani et al., 2019); (Khanna et al., 2019). This competency is very important for operators to be able to perform their tasks well. ...
The DCS SCADA course is one of the latest courses for the 2022 curriculum at Politeknik ATI Makassar. This course has case study-based learning from the industrial world and involves very expensive instrumentation devices for Real Plant, so a Teaching Module with factory i/o was created. This research aims to develop a DCS SCADA Teaching Module based on the Water Level Control case using Labview and Factory I/O. The virtual plant uses a water tank with instruments consisting of two control valves, namely the fill valve and the discharge valve, and two transducers, namely the level meter and the flow meter. To connect the factory I/O to NI Labview, the open protocol Modbus Ethernet is used. Based on the testing results, the calibration for the level measurement has an accuracy of 99.59% and a precision of 0.69% with the interpolation y = 0.301x -0.536 for a 100% correlation. For the system response with SP = 150 without disturbance, the PID parameters should be set to Kp = 25, Ti = 0.1, and Td = 0 with a system response consisting of Rise Time = 396 seconds, no Overshoot, Settling Time = 430 seconds, and Steady state error = 0.566 cm. For the system response with SP = 150 with a DV = 50% disturbance, the PID parameters should be set to Kp = 25, Ti = 0.1, and Td = 0 with a system response consisting of Rise Time = 382 seconds, no Overshoot, Settling Time = 462 seconds, and Steady state error = 1.469 cm.
This study presents a proposed approach that is focused on two aspects of smart buildings (SBs), which are near or real-time measuring and monitoring of energy consumption and energy generated locally. The design proposed is a smart plug, which is the employment of a smart monitoring system that allows users to monitor various ordinary electrical appliances (i.e., common lighting, air-conditioning, fans, and other electronic devices). For remote interaction with the smart plug, a simple end-user interface accessible through any smartphone is devised via Wi-Fi. Via internet connection, the sensors provide a direct connection to the users. A smart microgrid controller based on a Raspberry Pi board is used in the proposed system to control and act as the central part of the system while managing the operation of the system in real-time. The smart plug provides real-time data acquisition and data logging through a cloud database. As a result of the data obtained by real-time monitoring, building systems may be monitored, predictive maintenance can be performed, and problems and abnormalities can be identified. This system is intended to provide easy control of appliances and to allow adjustment of the electricity usage accordingly for conservation and saving of electricity. The proposed approach is also simple and convenient for building occupants to employ.
Big Data is a term used to refer to massive amounts of data, which is complex and obtained from a variety of sources. This type of data is large-scale, growing exponentially with time, and adds a substantial value if analyzed properly. It can be characterized using the 5 Vs of Big Data. Deriving meaningful insights and correlations to enable intelligent decisions is done via the process of Big Data Analytics (BDA). Big Data Analytics can thus help an enterprise cut costs, increase efficiency, focus on local preferences and improve sales. Many industries such as healthcare, finance, advertising, banking and security benefit from BDA. The emergence of big data has made it disadvantageous for corporations to store such large amounts of data in silos. Cloud computing provided a stable and established alternative, enabling remote access to data, faster crunching of large volumes of raw data and reliable storage of the data and the analysis. Therefore it emerges as the perfect complement for big data. Despite the benefits, there exist some security concerns that are not uncommon in applications of cloud computing.This chapter begins with an introduction of Big Data, including its advantages, followed by Big Data analytics, its relationship with Big Data and its benefits and barriers. We also analyze challenges in Big Data such as data storage, computational complexities and information security. This chapter discusses the relationship between Big Data analytics and the Cloud, including cloud services used in BDA. We also discuss future research areas such as high-speed processing accompanied with elevated performance and exalted throughput in BDA.KeywordsBig data analyticsCloudEdge computingBig data securityIntelligenceHadoopMapReduceHDFS
Today, the issue of energy efficiency is a major one in global politics. The external environment, particularly the wind speed and outside air temperature, determines the thermal burden the cold outside air places on a building's interior. The heat load of a building is influenced by several factors, including the wall’s heat transfer coefficients (W/mK), the coating material (W/mK), the inside temperature (°C), and the outside temperature (°C), and the temperature of external surface (°C). In this investigation, we undertake a comprehensive assessment, evaluation, and comparing the performance of two unique artificial approaches (BSA and COA) utilized for anticipating heat loss in green buildings; the optimum way is then identified depending on the R2 and RMSE criteria. The outcomes demonstrate that BSA and COA have R2 values of (0.97038 and 0.90158) and (0.9919 and 0.94239) in the training and testing phases. Additionally, the RMSE values for BSA and COA in the training and testing stages are (0.02541 and 0.08616) and (0.01336 and 0.06662), correspondingly. Also, the estimated MAEs (0.019055 and 0.0097193) denote a low level of training error for both methods. Regarding R2, RMSE and MAE values, the COA predicts energy loss more accurately.
Indoor environment has significant impacts on human health as people spend 90% of their time indoors. The COVID-19 pandemic and the increased public health awareness have further elevated the urgency for cultivating and maintaining a healthy indoor environment. The advancement in emerging digital twin technologies including building information modeling (BIM), Internet of Things (IoT), data analytics, and smart control have led to new opportunities for building design and operation. Despite the numerous studies on developing methods for creating digital twins and enabling new functionalities and services in smart building management, very few have focused on the health of indoor environment. There is a critical need for understanding and envisaging how digital twin paradigms can be geared towards healthy indoor environment. Therefore, this study reviews the techniques for developing digital twins and discusses how the techniques can be customized to contribute to public health. Specifically, the current applications of BIM, IoT sensing, data analytics, and smart building control technologies for building digital twins are reviewed, and the knowledge gaps and limitations are discussed to guide future research for improving environmental and occupant health. Moreover, this paper elaborates a vision for future research on integrated digital twins for a healthy indoor environment with special considerations of the above four emerging techniques and issues. This review contributes to the body of knowledge by advocating for the consideration of health in digital twin modeling and smart building services and presenting the research roadmap for digital twin-enabled healthy indoor environment.
This paper presents a mixed integer non-linear programming (MINLP) model to optimize, in a centralized fashion, the operation of multiple buildings in a microgrid. The proposed model aims to minimize the total cost of the energy imported from the main grid at the interconnection point, managing the power demand and generation of buildings, while operational constraints of the electrical grid are guaranteed. This approach considers the management of heating, ventilation and air conditioning (HVAC) units, lighting appliances, photovoltaic generation (PV) and energy storage system (ESS) of each building. Comfortable indoor conditions for the occupants are kept by a set of mathematical constraints. Additionally, a strategy that simplifies the original model is presented, based on a set of linearization techniques and equivalent representations, obtained through a pre-processing stage executed in EnergyPlus software. This strategy allows approximating the proposed model into a mixed integer linear programming (MILP) formulation that can be solved using commercial solvers. The proposed model was tested in a 13-bus microgrid for different deterministic cases of study with non-manageable loads and smart buildings. A large-size test case is also considered. Finally, a rolling horizon (RH) strategy is proposed with the aim of addressing the uncertainty of the data, as well as reducing the amount of forecasting data required.
Opportunistic networks (OppNets) are networks that can be used in situations of sparse network connectivity. Strategies for implementing secure transmission of messages in OppNets offer complex and distinctive problems that are wholly different from traditional networks using TCP/IP protocols for data transmission. In this paper, a security protocol named FuzzyPT is proposed which combats black hole attacks in OppNets. The defense mechanism is based on the information extracted from messages in the buffer, potential threat (PT) messages, and fuzzy logic. Fuzziness aids the system in being adaptive by modeling the character of nodes as either benign or malicious. It provides the network with the ability to judge the character of nodes based on relationships between different parameters instead of basing this decision on fixed conditions. FuzzyPT is observed to reduce erroneous decision-making and decrease the number of false positives and false negatives in the system. An evolutionary game theoretic model is applied, which analyzes the decision-making ability of relays in choosing varied strategies while forwarding messages. The evolutionary stable state is computed for the protocol, which verifies the proposition that a majority of the relays will cooperate by forwarding messages. The efficiency of the protocol is judged on various grounds such as delivery probability, network overhead ratio, packets dropped, and transmission latency. It is observed that the proposed game theoretic protocol outperforms an existing acknowledge and evolutionary game theory–based security protocol in terms of all the aforementioned performance characteristics.
Internet of Things (IoT) is the new digital realm
that is taking the world by storm. With intense connectivity
requirements and standards, IoT devices need to communicate
with other devices through a common protocol. For these reason
the interoperability is the most defining and crucial element in
the Internet of Things. This paper has delved in the literature
on IoT journals and article to identify the pertinent issues that
require to be addressed if success of the system is to be realized.
A primary break down of the IoT system has been done to
identify the key issues upon which a list of challenges have been
addressed with a help of literature. It has been discovered that
as new devices are being developed at an unprecedented pace,
their interoperability is becoming an issue. The nature of the
challenges identifies requires that the stakeholders develop a
standard that will guide them in their device development to
ultimately to overcome the interoperability in the IoT system.
Background & Objective: The paper presents a cloud based environment parameter monitoring system. Wireless Sensor Area Network is developed using ZigBee and NodeMCU. The wireless sensor nodes are developed with capabilities of remote data acquisition through the Internet of Things. The system architecture is designed to address various requirements of the environmental parameter monitoring. The system comprises of nodes which are placed at different locations. These nodes are designed with Arduino Uno an open source platform and sensors for environmental parameter monitoring. Conclusion: The data from the sensors are collected by Arduino Uno and uploaded to cloud through NodeMCU.
