Showing posts with label IoT. Show all posts
Showing posts with label IoT. Show all posts

Cyber-Physical System Approach To Smart Grids

    What Are Smart Grids?

    Smart grids are electric grids that use sophisticated monitoring, control, and communication technology to deliver dependable and secure electricity, increase system efficiency, and give flexibility to prosumers. 

    The advent of the smart grid era, as well as advancements in contemporary infrastructures for metering, communication, and energy storage, has transformed the power grid. 

    Smart grids are created using complicated physical networks and cyber technologies, allowing smart grids to be used in the Internet of Energy (IoE)

    IoE is the cloud in which intelligent sources of power production and loads of power consumption are incorporated. 

    Sensors are installed into modern electric power systems to give measurements. 

    Sensor measurements, as well as the sophisticated uses of numerous sensors, need the employment of a Cyber-Physical System (CPS)

    CPS is a system type that combines physical processes, computing, and networking. 

    The smart grid CPS model aids in modelling and simulation for evaluating system performance and characteristics. 

    The smart grid's CPS model must be resilient, allow for future additions, and be compatible with web service technologies. 

    The physical layers of the smart grid include electricity generating sources and loads such as smart buildings, the cyber-physical integration is formed by sensors used for measurements, and the cyber layer is formed by data storage and processing utilizing IOE. 

    The smart grid CPS model aids in the integration of intelligent devices and related information and communication technologies for resilient and dependable smart grid operation. 

    The energy management system is critical in the smart grid paradigm for increasing system efficiency and reliability. 

    This article describes a CPS model for smart grids as well as the obstacles related with its development. 

    Furthermore, this article discusses the smart grid's energy management system paradigm.

    What Is The History Of Smart Grids?

    The complex interactive network was the first study carried out by the Electric Power Research Institute (EPRI) in 1998 for constructing a fully automated dependable grid, which is the first smart grid prototype. 

    Following Intelli-suggestion Grid's in 2002, the smart grid idea was largely adopted for the future development of electricity networks. 

    In 2005, the European Smart-Grids Technology Platform was established, and in 2006, a study containing ideas and a framework for the European smart grid was released. 

    The US Department of Energy revised this research in 2007 to promote a stable and sustainable energy supply in the report "The Smart Grid." Many nations' key task is to establish a smart city with socioeconomic and environmental advantages, as well as to improve power consumption in a smarter manner to preserve energy. 

    In recent years, there has been a rise in demand for electric energy, with load patterns growing more complicated, posing a challenge to the power grid network. 

    To overcome these issues, power engineers and academics introduced the CPS strategy for the power system network. 

    The Cyber-Physical Energy Systems (CPES) are a mix of a cyber-physical system and a power system network. 

    The measurements from the sensors are used to make the choice to execute the control in the distributed network. 

    The integration of cyber and physical components in the system is the most difficult problem in CPES. 

    For a unified functioning of cyber and physical layer components in the CPES, all events occurring and choices made must be communicated between cyber and physical layer components, boosting the system's potential to resolve difficulties. 

    The development of smart sensors and their integration into the electric grid guarantees that original and reliable data is available at control centers. 

    The use of genuine, trustworthy data improves the precision with which issues are solved and control is applied in a variety of applications. 

    The current electric grid incorporates a greater quantity of renewable energy, which is not properly managed owing to the intermittent availability of renewable energy. 

    This difficulty inspires the notion of a smart grid with upgraded infrastructure for communication and computing in the traditional grid. 

    The current smart grid apps lack an underlying framework, resulting in isolation and making integration and future growth difficult. 

    The smart grid may be constructed more effectively with the aid of a CPES reference model. 

    The CPES reference model must handle large-scale and long-term smart grid scenarios. 

    The CPES model must be a general model that represents the features of the smart grid scenario using smart grid technology and standards. 

    The primary goal of power engineers is to create an efficient algorithm that can operate in real time in the grid. 

    Another challenge for power engineers is the extraordinary amount of data collected by the Phasor Measurement Unit (PMU), which must be gathered and processed according to the requirements. 

    The coordination of dispersed resources is critical to the grid's real-time functioning. 

    The grid's communication network must be upgraded in order to manage the grid's coordinated functioning. 

    The infrastructure consists of a communication network and middleware, which includes software for data processing and control deployment. 

    The diagram below depicts the structure of a typical electricity network. 

    This article provides an overview of CPS as well as a summary of research in cyber-physical energy systems. 

    The purpose of this article is to assist power engineers and researchers in understanding the CPS approach to power grids. 

    What are the various definitions of a  SMART GRID across geographies?

    Smart grid (SG) is a phrase that has many meanings. 

    The definition of SG is the integration and enablement of information and communications technology (ICT) and advanced technologies with power networks in order to make the power system efficient, affordable, and sustainable. 

    In the United States, SG refers to the process of converting the electric industry from a centralized, producer-controlled network to a consumer-controlled network. 

    SG in Europe refers to the involvement and integration of all European societies

    SG refers to a physical network-based method to ensuring security, dependability, and sustainability in China. 

    The need of SG in IEEE Grid Vision 2050 is to run and manage the whole power system, which includes all current and future technologies. 

    The need for flexible, portable, safe, and secure power supply utilization through SG necessitates a rethinking of the interplay between physical power systems, cyber systems, and users. 

    The issues with SGs include the intermittent nature of renewable generating, which impacts power quality and system stability. 

    Peak demand plays an important role in electricity consumption; lowering peak demand improves supply capacity without adding additional generating units. 

    Power losses in SGs may be reduced by eliminating long-distance transmission lines owing to the use of dispersed generating. 

    The use of smart meters, modern sensors, and ICT aids in the improvement of SG efficiency. 

    What are the characteristics of a smart grid?

    To accomplish all of the aforementioned benefits, the SGs must have the following characteristics: 

    • Distributed control 

    • Load forecasting 

    • Renewable generation forecasting 

    • Peak demand reduction

     • Energy storage system 

    The CPS paradigm, which employs a systematic approach to solving difficulties and challenges, is the answer to these concerns and challenges. 


    The term CPS was established in 2006 by the National Science Foundation to characterize a complex, interdisciplinary, next-generation system that integrates embedded technology in the physical environment. 

    CPS is defined in the United States as the integration of embedded systems and physical components, in Europe as the communication between cloud and human, and in China as intelligence in sensing, processing, and control. 

    CPS has made considerable progress in recent years, but it still has a long way to go before reaching its full potential. 

    Sensing, analyzing, synthesizing, modelling, and control are rapidly evolving in engineering and research. 

    CPSs combine engineering and computer science to address concerns and challenges. 

    The following are the technical obstacles in bringing the two sectors together: 

    • Design: 

    1. Design is essential for achieving continuous integration, communication, and computation. 
    2. To satisfy the system requirements, standard architectures and design tools are necessary. 
    3. Architectures and approaches should provide data confidentiality, integrity, availability, and asset protection. 

    • Science and engineering: 

    1. The integration of cyber and physical components necessitates rapid sensing, processing, and control, all of which must be precise. 
    2. To make judgments and govern activities, enormous amounts of data must be processed quickly and efficiently. 
    3. Traditional centralized control does not have the requisite speed, hence dispersed control is essential. 
    4. The essential components are data sensing, data processing, and control. 


    The physical layer comprises of the components required for CPS monitoring and control. 

    Equipment in the physical layer include generators, transformers, loads, and measurement devices. 

    The multi-input multi-output (MIMO) CPS model is represented as, 

    αΊ‹(t) = Ax(t) + Bu(t) (1.1) 

    y(t) = Cx(t) (1.2) 

    where, A is the state matrix, B is the input matrix, C is the output matrix, 

    x(t) is the state vector, u(t) is the input vector, y(t) is the output vector. 

    The control is achieved using input vector which is given by, 

    u(t) = Kx(t) (1.3) 

    where, K is the connection between cyber layer control and physical layer sensors. 


    To accomplish CPS features, SG integrates physical components of the power grid network with the cyber layer. 

    Real and virtual systems are combined in SG, where events in physical systems are relayed as input to CPS control centers and simulated to assess physical system performance. 

    Communication channels enable dynamic collaboration between physical and cyber systems. 

    Parallel processing and distributed data aid in decision making via CPS layers. 

    The CPS will adapt, organize, and learn on its own, allowing it to react to faults, attacks, and emergencies in SG, making it secure and trustworthy. 

    The problems in the CPS part of the SG are that the system is time-critical, the components work together to ensure stability, voltage and frequency control, and quick reaction to uncertainties and disturbances. 

    CPS is used in SG to eliminate duplication and increase SG stability. 

    The primary functions of CPS in Singapore are as follows: 

    • Dependability 

    • Reliability 

    • Predictability 

    • Sustainability 

    • Security 

    • Interoperability 

    Many studies are being conducted to address the concerns related to SG and CPS. 

    The combination of SG and CPS is referred to as CPES. 

    What is the Architecture of a CPS Smart Grid?

    The physical component of the power system network, which varies from other object-oriented software, demands safety and dependability. 

    To enable the system to function in uncertain and unexpected settings, the CPS design must be particular for integrating cyber and physical components. 

    In these conditions, the software-based components perform well. 

    Because software platforms lack timing capabilities, redesigning computer architectures in light of power system features is essential. 

    It requires standards and frameworks in which physical, communication, and computing components interface on their own standards and are interfaced together. 

    Communication technology is required for efficient and effective interaction between physical and cyber layer components. 

    Space and time are two dimensions of communication that must be addressed. 

    They relate to the distance and time required for data transmission. 

    The several levels of communication are determined by the network size, and they are as follows: home area network, neighborhood area network, metropolitan area network, and wide area network. 

    Time delay, error pockets, and queue delays are all important real-time issues. 

    Modeling and Simulation: The modelling tool must be able to handle network standards, interoperability, hybrid modelling, and large-scale operation. 

    The SG of the future might be huge or small in size, with scattered sources and energy needs, and it must be run reliably and in a user-friendly way, requiring risk analysis, risk management, security, uncertainty analysis, and coordination. 

    How Is Cyber Security Enforced In a CPS Smart Grid?

    CPS must be secure, and any random failure or attack would be detrimental to the system. 

    Because of the employment of cyber components such as PMU and advanced metering infrastructure (AMI), the system is vulnerable to attack. 

    The SG must be designed to identify and mitigate cyber-attacks using an intrusion detection system (IDS). 

    IDS is classified into two types: 

    1. host-based IDS 
    2. and network-based IDS. 

    How does Distributed Computation occur on a CPS Smart Grid?

    In SG, a vast number of smart meters and sensors are positioned at different levels, each of which must process massive amounts of data sequentially. 

    Fault detection, power control and reconfiguration, management, and restoration in power networks are all time-sensitive, posing a problem in SG. 

    Data mining techniques ideal for dealing with massive amounts of data are the answer to these issues. 

    In SG, new computational techniques like as grid and cloud computing platforms are employed to execute sub- and local calculations. 

    Distributed Intelligence: The SGs use a multi-agent system to do large-scale computing (MAS). 

    An agent is a control entity that may communicate and interact with other components to achieve local/global objectives. 

    In MAS, a set of agents is used in a dispersed network to concentrate on different applications. 

    In SG, automation must be present at both the micro and macro operational levels in order to make requirements-based decisions. 

    SG is dependent on global optimization and local control, where global optimization has multiple goals and local control has one. 

    Because centralized optimization is ineffective for SG, distributed optimization solutions are necessary. 

    In the merging of global optimization with local control, MAS is employed to achieve global coordination. 

    How is Distributed Control achieved in a Smart Grid?

    As the number of components in SG increases, the system gets more complicated due to the presence of several levels of control and hierarchy. 

    Control goals are multi-objective, having global and local needs that may vary based on the operating conditions. 

    The control must generate data from physical components in order to assess and regulate the system's components. 

    Control in SG is built on the physical layer, the cyber layer, and the planning and operations layer. 


    What Is CPES?

    Power system analysis in contemporary systems is performed using computer models and is a current research area. 

    When computers were used for the power grid, new software to mimic the transmission and distribution system was created. 

    By building additional software, this system was upgraded to compute more complicated networks and to calculate quicker. 

    The study in the creation of new software aided in the construction of a distributed model of a power network, parallel processing, and system analysis. 

    Some current power system simulators, such as Siemens PSSE, are only used for transmission systems, while others, such as GridLab-D, are only used for distribution systems. 

    Recently, considerable research has been conducted in the field of co-simulation. 

    Many cloud-based software applications are utilized to simulate the network and run simulations. 

    Co-simulation combines continuous system and discrete event simulation to model and understand CPES behavior. 

    Diverse strategies, such as the common information model, have been used to integrate various systems. 

    These approaches are used to manage energy in distributed systems and subsystems. 

    Many researchers have used the CPS for power grid design to examine the reliability and security of the power system. 

    The study focuses on the difficulties in CPS modelling, design, and simulation. 

    CPS is utilized in a variety of applications, including management, smart buildings, cloud computing, surveillance, scheduling, monitoring, and transportation systems. 

    16 Any outage or blackout in the power system has a significant influence on the economy and society, making the functioning of the power grid important. 

    The CPES model is an interconnected structure meant to facilitate communication among stakeholders. 

    This model is not a CPES standard, but it contains information on numerous technologies and standards for the smart grid, such as the National Institute of Standards and Technology (NIST) and IEC. 

    This model may be used to create new technologies, standards, or algorithms, as well as to assess the operation of smart grids. 

    Future standards and technologies may be grown from current ones using the CPES paradigm. 

    The following reference models have been established and discussed in the literature: 

    • Open Systems Interconnection Reference Model. 

    • Agent Systems Reference Model. 

    • National Institute of Standards and Technology Reference Model. 


    Many issues in monitoring and controlling power networks have arisen in recent years as a result of technological advancements and the usage of dispersed energy sources. 

    Many PMUs have been installed to collect real-time data and transmit it to the control center. 

    PMUs collect data at a high sample rate. 

    To accomplish this high sample rate, a Wide Area Network (WAN) is employed to develop Wide Area Monitoring and Control (WAMC). 

    WAMC has several applications in power grids, including state estimation, contingency analysis, optimum power flow analysis, economic dispatch, and autonomous generation control. 

    These acquired data are used to operate systems using control algorithms, however all data must be monitored synchronously to prevent mistakes, which are done in the underlying framework. 

    As a result, the underlying infrastructure is a critical component for power system applications. 

    Applications may be both functional and nonfunctional. 

    The functional applications synchronize and coordinate data flow across dispersed network resources. 

    Scalability (support for a large number of PMUs and a communication network), latency and predictability (time sensitive), and reconfigurability are nonfunctional applications (addition or removal of components, nodes, or modifications in control algorithms). 


    The AMI devices in the SG paradigm are used for two-way communication between the utility and the user, allowing for demand control by shifting peak loads. 

    It is an optimum management system for monitoring and managing power production, consumption, and storage in SGs. 

    The network's communication infrastructure is utilized to gather data on load demand, generation, and forecasts from all sensors in order to enable remote monitoring and control for different operating modes, which is monitored in control centers. 

    The SGEMS not only delivers efficient generation use, but also energy storage and system management services. 


    The SGEMS center features a central controller that provides monitoring and control capabilities to the utility and customer based on communication. 

    The smart meter serves as a conduit for communication between the utility and the customer. 

    The data is collected by smart meters and sent to the control center, which receives the control signal to optimize demand management depending on generation. 

    Electric vehicles (EV) use SG electricity while simultaneously providing power back to SG in an emergency and acting as energy storage. 

    Because the distributed generation in the SG is integrated to accomplish generation management, the SG does not need to depend on electricity from the central grid. 

    Because renewable energy is inherently intermittent, energy storage systems play a critical role in maintaining power quality, efficiency, and dependability. 

    What are the FUNCTIONS OF SGEMS?

     The SGEMS must be adaptable in order to manage and regulate the SG and participate in the market while conserving energy and meeting load demand. 

    Control services are accessible to utilities and customers, who may choose services and preferences through a human-machine interface. 

    The following are the primary roles and descriptions of the SGEMS: 

    Monitoring: Provides access to data on energy production and demand Displays operating mode and status 

    Logging: Collect and preserve data on DER production, demand from loads, and energy storage system. 

    Control: There are two forms of control: direct control applied on equipment and remote control where consumers watch load patterns and control. 

    Alarm: An alarm is raised at the SGEMS centre with data on system anomalies discovered. 

    Management: Management improves the optimization and effective use of energy in Singapore. 

    It offers services including DER management and storage management. 


    A smart control center, smart meter, communication and networking system, energy storage, distributed generation, and other smart devices comprise the SGEMS infrastructure. 

    SGEMS can access, monitor, regulate, and optimize the performance of multiple distributed generations, loads, and other devices via these infrastructures. 

    SGEMS facilitates load and generation integration via two-way communication. 


    The SGEMS are based on hardware communication, such as powerline communication and human-machine interface. 

    Researchers are working on new WAN communication networks. 

    The SGEMS communication network must fulfil the IEEE 802.15.4 WAN specifications. 

    The ability to include Bluetooth technology in communication may be employed in SGEMS as well. 

    To accomplish system functioning, the major components of SGEMS are the processor for applications, communication, user, sensor, and load interface. 


    Smart meters are used to assess consumer energy use and production, as well as power generation, and they employ two-way communication to send data to and receive signals from the control center. 

    Smart meters' primary features include detecting energy use, two-way communication, transmitting data and receiving instructions, smart load shedding transition in the event of a failure, and data collecting. 


    The Smart Energy Management System (EMS) center is the brain of the whole smart grid and is responsible for implementing the energy management system in SG. 

    The smart EMS center's primary functions are as follows: receiving messages sent by smart meters and control panels, automated demand response, human-machine interface, online monitoring, scalability, integrating distributed resources and energy storage, forecasting renewable generation, and optimal control. 

    What is the scope for Renewable Sources in IN SMART GRIDS?

    Since the 1990s, the use of renewable energy has increased significantly in a variety of industrial, commercial, and residential settings. 

    Only 31.1% of all energy output in the globe is produced by renewable energy. 

    The research in the area of EMS for a renewable energy system is progressing rapidly. 

    The necessity to reduce emissions in energy generation paves the way for the development of sustainable approaches that make use of renewable energy sources. 


    Solar energy is the most environmentally friendly and inexhaustible renewable energy source. 

    Solar energy is used in a variety of ways, including solar heaters, solar PV, and so on. 

    Because of their ease of installation, solar heaters are often employed in home applications. 

    Solar PV and solar concentrators are used to generate electricity. 

    Electricity generating need large-scale investment to meet bulk power requirements. 

    Solar energy is used in two ways: 

    1. solar thermal, which converts sunlight into thermal energy and generates electricity, 
    2. and solar PV, which generates electricity directly from sunlight. 

    Solar energy is widely employed because of its copious supply of sunshine and minimal maintenance. 

    Because solar energy is only accessible during the day, energy storage is required. 

    Charge controllers are required to safeguard energy storage devices from overcharging and discharging. 

    Wind energy is another renewable energy source that is used on a big and small scale. 

    Wind speeds of 2-15 m/s can be used to generate electricity. 


     In this part, important difficulties and possibilities in the SG's CPS are discussed in terms of ecosystems, big data, cloud computing, and the Internet of Things. 

    • Ecosystem Perspective: 

    • SG growth is inextricably linked to the environment and social system. 
    • The flora and fauna, as well as climate changes, are examples of nature, environment, and ecosystems that are influenced by SG improvement. 

    • Big Data: 

    • Big data is utilized in data collection and analysis. 
    • Volume, velocity, veracity, variance, and value are the five key features of big data. 

    • Cloud Computing: 

    • In SG, dispersed resources in real-time management must be met on time. 
    • Cloud computing is a model in which services like compute, networking, and storage serve as resources. 
    • It provides the benefits of self-service, resource sharing, flexibility, and it boosts security and overcomes privacy problems. 

    • Internet of Things (IoT): 

    • The Internet of Things (IoT) is the expansion of Internet services caused by the spread of RFID, sensors, smart devices, and "things" on the Internet. 
    • IoT is rapidly expanding, with 50 billion devices expected to be connected to the internet by 2020. 
    • The progress of IoT results in the advancement of IoE. 


     The smart grid environment with EMS plays an important role in the efficient use of power and demand response. 

    The smart SGEMS with wireless networks and smart sensing element technology raises the bar for SG standards. 

    Because of its ease of use and simplicity, SGEMS has been more popular in recent years. 

    The present smart grid infrastructure, which includes two-way communication, metering, and monitoring devices, lays the groundwork for smart SGEMS applications. 

    The extensive use of SGEMS in the future may change the manner of electricity use and renewable energy utilization inside the power network. 

    Due to geographical and climatic conditions, alternative energy may be the dominant contributor in renewable energy applications, whereas wind and biomass contribute comparably little. 

    The use of renewable energy reveals that energy savings from transmission energy losses and conventional installation may be realized. 

    The design of a CPS is more difficult than developing physical and cyber components one by one. 

    For CPSs, the required behavior of machine parts must be specified in terms of their impact on the physical environment. 

    As a result, modelling requires a unifying framework that allows for consistency and a low-overhead style.

    ~ Jai Krishna Ponnappan.

    Find Jai on Twitter | LinkedIn | Instagram

    You may also read more about Green Technologies and Renewable Energy Systems here.

    References And Further Reading:

    1. Amin, M. Minimizing Failure While Maintaining Efficiency of Complex Inter-active Networks and Systems: EPRI and US Department of Defense Complex Interactive Networks/Systems Initiative; First Annual Report, 2000. 

    2. Haase, P. Intelli Grid: A Smart Network of Power. EPRI J. 2005, 27, 17–25. 

    3. Profiling and Mapping of Intelligent Grid R & D Programs. EPRI 2006.

    4. European Smart-grids Technology Platform: Vision and Strategy for Europe’s Electricity Networks of the Future. Directorate-General for Research Sustainable Energy Systems, 2006. 

    5. Davis, C. et al. Scada Cyber Security Testbed Development. In NAPS. IEEE, 2006; pp 483–488. 

    6. Schneider, K. et al. Assessment of Interactions Between Power and Telecommunications Infrastructures. IEEE TPWRS 2006. 

    7. Sun, Y. et al. Verifying Noninterference in a Cyber-physical System the Advanced Electric Power Grid. In QSIC; IEEE 2007; pp 363–369. 

    8. Karnouskos, S. Cyber-physical Systems in the Smartgrid. In INDIN. IEEE, 2011; pp 20–23. 

    9. Giani, A. et al. The Viking Project: An Initiative on Resilient Control of Power Networks. In ISRCS. IEEE, 2009; pp 31–35. 

    10. Mo, Y. et al. Cyber–physical Security of a Smart Grid Infrastructure. Proc. IEEE 2012, 100 (1), 195–209. 

    11. Susuki, Y. et al. A Hybrid System Approach to the Analysis and Design of Power Grid Dynamic Performance. Proc. IEEE 2012. 

    12. Saber, A.; Venayagamoorthy, G. Efficient Utilization of Renewable Energy Sources by Gridable Vehicles in Cyber-physical Energy Systems. Syst. J. IEEE 2010, 4 (3), 285–294. 

    13. Zhu, Q. et al. Robust and Resilient Control Design for Cyber-physical Systems with an Application to Power Systems. In CDC-ECC, 2011. 

    14. Hadjsaid, N. et al. Modeling Cyber and Physical Interdependencies-application in ICT and Power Grids. In IEEE/PES PSCE, 2009; pp 1–6. 

    15. Zhao, J. et al. Cyber Physical Power Systems: Architecture, Implementation Techniques and Challenges. Dianli Xitong Zidonghua (Autom. Electric Power Syst.) 2010, 34 (16), 1–7. 

    16. NIST Special Publication 1108R2. NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 2.0, 2012; NIST Framework Release 2-0 corr.pdf 

    17. Liu, Y.; Ning, P.; Reiter, M. False Data Injection Attacks against State Estimation in  Electric Power Grids. ACM TISSEC 2011. 

    AI - Smart Hotels And Smart Hotel Rooms.

    In a competitive tourist sector, high-tech and artificial intelligence are being used by luxury hotels to deliver the greatest experience for their visitors and grow their market share.

    The experience economy, as it is known in the hospitality management business, is shaping artificial intelligence in hotels.

    An experience is created by three major players: a product, a service, and a consumer.

    The artifacts presented in the marketplaces are known as products.

    Services are the concrete and intangible benefits of a single product, or a collection of goods, as marketed by frontline staff via a procedure.

    The end user of these items or services is the client.

    Customers are looking for items and services that will meet their requirements.

    Hoteliers, on the other hand, must develop extraordinary events that transform manufactured goods and services into real experiences for their consumers in order to emotionally connect with them.

    In this approach, experiences become a fungible activity in the market with the goal of retaining clients.

    Robotics, data analysis, voice activation, face recognition, virtual and augmented reality, chatbots, and the internet of things are all examples of artificial intelligence in the luxury hotel business (IoT).

    Smart rooms are created for hotel guests by providing automated technology that naturally solves their typical demands.

    Guests may utilize IoT to control the lights, curtains, speakers, and television in their rooms through a connected tablet.

    • When a person is awake and moving about, a nightlight system may detect this.
    • Wellness gadgets that deliver sensory experiences are available in certain rooms for disabled visitors.
    • Smart rooms may capture personal information from customers and keep it in customer profiles in order to give better service during subsequent visits.

    In terms of smart room technology, the Hilton and Marriott worldwide luxury hotel companies are industry leaders.

    One of Hilton's initial goals is to provide guests the ability to operate their room's features using their smartphone.

    • Guests may customize their stay according to their preferences utilizing familiar technologies in this manner.
    • Lights, TVs, the temperature, and the entertainment (streaming) service are all adjustable in typical Hilton smart rooms (Ting 2017).
    • A second goal is to provide services via mobile phone apps.
    • During their stay, guests may put their own preferences.
    • They may, for example, choose digital artwork or images from the room's display.
    • Voice activation services are presently being developed for Hilton smart bedrooms (Burge 2017).

    Marriott's smart rooms were created in collaboration with Legrand's Eliot technology and Samsung's Artik guest experience platform.

    Marriott has deployed cloud-based hotel IoT technologies (Ting 2017).

    Two prototype rooms for testing new smart systems have come from this partnership.

    The first is a room with smart showers, mirrors, art frames, and speakers that is totally networked.

    • Guests may use voice commands to operate the lighting, air conditioning, curtains, paintings, and television.
    • A touchscreen shower is available, allowing visitors to write on the smart glass of the shower.
    • Shower notes may be turned into papers and sent to a specific address (Business Traveler 2018).
    • The quantity of oxygen in this Marriott room is controlled by sensors that monitor the number of people in the suite.
    • These sensors also help visitors wake up in the middle of the night by displaying the time to get out of bed and lighting the path to the restroom (Ting 2017).
    • A loyalty account allows guests to select their particular preferences ahead to arrival.

    A second, lower-tech area is linked through tablet and just has the Amazon Dot voice-controlled smart speaker.

    • The television remote may be used to change the room's characteristics.
    • The benefit of this room is that it has very few implementation requirements (Ting 2017).
    • Hoteliers point to a number of benefits of smart rooms in addition to convenience and customization.
    • Smart rooms help to protect the environment by lowering energy consumption expenses.
    • They may also save money on wages by reducing the amount of time housekeeping and management spend with visitors.

    Smart rooms have their own set of constraints.

    It may be tough to grasp certain smart technology.

    • For starters, the learning curve for overnight visitors is rather short.
    • Second, the infrastructure and technology required for these rooms continues to be prohibitively costly.
    • Even if there are long-term cost and energy benefits, the initial investment expenses are significant.

    Finally, there's the issue of data security.

    Hotels must continue to evolve to meet the needs of new generations of paying customers.

    Technology is deeply interwoven in the everyday behaviors of millennials and post-millennials.

    Their smart phones, video games, and tablets are transforming the meaning of experience in a virtual world.

    Luxury tourism already includes high-priced goods and services that are supported by cutting-edge technology.

    The quality of future hotel smart room experiences will be influenced by visitor income levels and personal technological capabilities, creating new competitive marketplaces.

    Customers expect high-tech comfort and service from hotels.

    Hotel operators gain from smart rooms as well, since they serve as a source of large data.

    Companies are rapidly collecting, storing, and using all accessible information on their customers in order to provide unique goods and services.

    This technique aids businesses in creating twenty-first-century markets in which technology is as important as hotel guests and management.

    ~ Jai Krishna Ponnappan

    Find Jai on Twitter | LinkedIn | Instagram

    You may also want to read more about Artificial Intelligence here.

    See also: 

    Smart Cities and Homes.

    References & Further Reading:

    Burge, Julia. 2017. “Hilton Announces ‘Connected Room,’ The First Mobile-Centric Hotel Room, To Begin Rollout in 2018.” Hilton Press Center, December 7, 2017.

    Business Traveler. 2018. “Smart Rooms.” Business Traveler (Asia-Pacific Edition), 11.

    Imbardelli, A. Patrick. 2019. “Smart Guestrooms Can Transform Hotel Brands.” Hotel Management 234, no. 3 (March): 40.

    Pine, B. Joseph, II, and James H. Gilmore. 1998. “Welcome to the Experience Economy.” Harvard Business Review 76, no. 4 (July–August): 97–105.

    Swaminathan, Sundar. 2017. Oracle Hospitality Hotel 2025 Industry Report. Palm Beach Gardens, FL: International Luxury Hotel Association.

    Ting, Deanna. 2017. “Hilton and Marriott Turn to the Internet of Things to Transform the Hotel Room Experience.” Skift, November 14, 2017.

    AI - Smart Homes And Smart Cities.


    Projects to develop the infrastructure for smart cities and houses are involving public authorities, professionals, businessmen, and residents all around the world.

    These smart cities and houses make use of information and communication technology (ICT) to enhance quality of life, local and regional economies, urban planning and transportation, and government.

    Urban informatics is a new area that gathers data, analyzes patterns and trends, and utilizes the information to implement new ICT in smart cities.

    Data may be gathered from a number of different sources.

    Surveillance cameras, smart cards, internet of things sensor networks, smart phones, RFID tags, and smart meters are just a few examples.

    In real time, any kind of data may be captured.

    Passenger occupancy and flow may be used to obtain data on mass transit utilization.

    Road sensors can count cars on the road or in parking lots.

    They may also use urban machine vision technologies to determine individual wait times for local government services.

    From public thoroughfares and sidewalks, license plate numbers and people's faces may be identified and documented.

    Tickets may be issued, and statistics on crime can be gathered.

    The information gathered in this manner may be compared to other big datasets on neighborhood income, racial and ethnic mix, utility reliability statistics, and air and water quality indices.

    Artificial intelligence (AI) may be used to build or improve city infrastructure.

    Stop signal frequencies at crossings are adjusted and optimized based on data acquired regarding traffic movements.

    This is known as intelligent traffic signaling, and it has been found to cut travel and wait times, as well as fuel consumption, significantly.

    Smart parking structures assist cars in quickly locating available parking spaces.

    Law enforcement is using license plate identification and face recognition technologies to locate suspects and witnesses at crime scenes.

    Shotspotter, a business that triangulates the position of gunshots using a sensor network placed in special streetlights, tracked and informed police agencies to over 75,000 bullets fired in 2018.

    Information on traffic and pedestrian deaths is also being mined via big data initiatives.

    Vision Zero is a global highway safety initiative that aspires to decrease road fatalities to zero.

    Data analysis using algorithms has resulted in road safety efforts as well as road redesign that has saved lives.

    Cities have also been able to respond more swiftly to severe weather occurrences because to ubiquitous sensor technology.

    In Seattle, for example, conventional radar data is combined with RainWatch, a network of rain gauges.

    Residents get warnings from the system, and maintenance staff are alerted to possible problem places.

    Transport interconnection enabling completely autonomous autos is one long-term aim for smart cities.

    At best, today's autonomous cars can monitor their surroundings to make judgments and avoid crashes with other vehicles and numerous road hazards.

    However, cars that connect with one another in several directions are likely to create fully autonomous driving systems.

    Collisions are not only averted, but also prevented in these systems.

    Smart cities are often mentioned in conjunction with smart economy initiatives and foreign investment development by planners.

    Data-driven entrepreneurial innovation, as well as productivity analyses and evaluation, might be indicators of sensible economic initiatives.

    Some smart towns want to emulate Silicon Valley's success.

    Neom, Saudi Arabia, is one such project.

    It is a proposed megacity city that is expected to cost half a trillion dollars to build.

    Artificial intelligence is seen as the new oil in the city's ambitions, despite sponsorship by Saudi Aramco, the state-owned petroleum giant.

    Everything will be controlled by interconnected computer equipment and future artificial intelligence decision-making, from home technology to transportation networks and electronic medical record distribution.

    One of Saudi Arabia's most significant cultural activities—monitoring the density and pace of pilgrims around the Kaaba in Mecca—has already been entrusted to AI vision technologies.

    The AI is intended to avert a disaster on the scale of the 2015 Mina Stampede, which claimed the lives of 2,000 pilgrims.

    The use of highly data-driven and targeted public services is another trademark of smart city programs.

    Information-driven agencies are frequently referred to as "smart" or "e-government" when they work together.

    Open data projects to encourage openness and shared engagement in local decision-making might be part of smart governance.

    Local governments will collaborate with contractors to develop smart utility networks for the provision of electricity, telecommunications, and the internet.

    Waste bins are linked to the global positioning system and cloud servers, alerting vehicles when garbage is ready for pickup, allowing for smart waste management and recycling initiatives in Barcelona.

    Lamp poles have been converted into community wi-fi hotspots or mesh networks in certain areas to provide pedestrians with dynamic lighting safety.

    Forest City in Malaysia, Eko Atlantic in Nigeria, Hope City in Ghana, Kigamboni New City in Tanzania, and Diamniadio Lake City in Senegal are among the high-tech centres proposed or under development.

    Artificial intelligence is predicted to be the brain of the smart city in the future.

    Artificial intelligence will personalize city experiences to match the demands of specific inhabitants or tourists.

    Through customized glasses or heads-up displays, augmented systems may give virtual signs or navigational information.

    Based on previous use and location data, intelligent smartphone agents are already capable of predicting user movements.

    Artificial intelligence technologies are used in smart homes in a similar way.

    Google Home and other smart hubs now integrate with over 5,000 different types of smart gadgets sold by 400 firms to create intelligent environments in people's homes.

    Amazon Echo is Google Home's main rival.

    These kinds of technologies can regulate heating, ventilation, and air conditioning, as well as lighting and security, as well as household products like smart pet feeders.

    In the early 2000s, game-changing developments in home robotics led to widespread consumer acceptance of iRobot's Roomba vacuum cleaner.

    Obsolescence, proprietary protocols, fragmented platforms and interoperability issues, and unequal technological standards have all plagued such systems in the past.

    Machine learning is being pushed forward by smart houses.

    Smart technology' analytical and predictive capabilities are generally regarded as the backbone of one of the most rapidly developing and disruptive commercial sectors: home automation.

    To function properly, the smarter connected home of the future needs collect fresh data on a regular basis in order to develop.

    Smart houses continually monitor the interior environment and use aggregated past data to establish settings and functionalities in buildings with smart components installed.

    Smart houses may one day anticipate their owners' requirements, such as automatically changing blinds as the sun and clouds move across the sky.

    A smart house may produce a cup of coffee at precisely the correct time, order Chinese takeout, or play music based on the resident's mood as detected by emotion detectors.

    Pervasive, sophisticated technologies are used in smart city and household AI systems.

    The benefits of smart cities are many.

    Smart cities pique people's curiosity because of its promise for increased efficiency and convenience.

    It's enticing to live in a city that anticipates and easily fulfills personal wants.

    Smart cities, however, are not without their detractors.

    Smart havens, if left uncontrolled, have the ability to cause major privacy invasion via continuous video recording and microphones.

    Google contractors might listen to recordings of exchanges with users of its famous Google Assistant artificial intelligence system, according to reports in 2019.

    The influence of smart cities and households on the environment is yet unknown.

    Biodiversity considerations are often ignored in smart city ideas.

    Critical habitat is routinely destroyed in order to create space for the new cities that tech entrepreneurs and government officials desire.

    Conventional fossil-fuel transportation methods continue to reign supreme in smart cities.

    The future viability of smart homes is likewise up in the air.

    A recent research in Finland found that improved metering and consumption monitoring did not successfully cut smart home power use.

    In reality, numerous smart cities that were built from the ground up are now almost completely empty.

    Many years after their initial construction, China's so-called ghost cities, such as Ordos Kangbashi, have attained occupancy levels of one-third of all housing units.

    Despite direct, automated vacuum waste collection tubes in individual apartments and building elevators timed to the arrival of residents' automobiles, Songdo, Korea, an early "city in a box," has not lived up to promises.

    Smart cities are often portrayed as impersonal, elitist, and costly, which is the polar opposite of what the creators intended.

    Songdo exemplifies the smart city trend in many aspects, with its underpinning structure of ubiquitous computing technologies that power everything from transportation systems to social networking channels.

    The unrivaled integration and synchronization of services is made possible by the coordination of all devices.

    As a result, by turning the city into an electronic panopticon or surveillance state for observing and controlling residents, the city simultaneously weakens the protective advantages of anonymity in public settings.

    Authorities studying smart city infrastructures are now fully aware of the computational biases of proactive and predictive policing.

    ~ Jai Krishna Ponnappan

    Find Jai on Twitter | LinkedIn | Instagram

    You may also want to read more about Artificial Intelligence here.

    See also: 

    Biometric Privacy and Security; Biometric Technology; Driverless Cars and Trucks; Intelligent Transportation; Smart Hotel Rooms.

    References & Further Reading:

    Albino, Vito, Umberto Berardi, and Rosa Maria Dangelico. 2015. “Smart Cities: Definitions, Dimensions, Performance, and Initiatives.” Journal of Urban Technology 22, no. 1: 3–21.

    Batty, Michael, et al. 2012. “Smart Cities of the Future.” European Physical Journal Special Topics 214, no. 1: 481–518.

    Friedman, Avi. 2018. Smart Homes and Communities. Mulgrave, Victoria, Australia: Images Publishing.

    Miller, Michael. 2015. The Internet of Things: How Smart TVs, Smart Cars, Smart Homes, and Smart Cities Are Changing the World. Indianapolis: Que.

    Shepard, Mark. 2011. Sentient City: Ubiquitous Computing, Architecture, and the Future of Urban Space. New York: Architectural League of New York.

    Townsend, Antony. 2013. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. New York: W. W. Norton & Company.

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