Smart Office Research Articles

Review of Smart Office Assistant

AI is playing major role in every field now a days and have become widely accepted in our society than traditional methods. The Smart Office Assistant is an intelligent system made to automate office environment in which employee attendance tracking and visitor management in offices can be done. Using web cam face recognition is done for people entering the office, for employees greeting is done by calling the name also their check in time and attendance is marked. For unrecognized faces it acts as a smart receptionist, which can handle appointment bookings and provide general office information for unfamiliar visitors through a chatbot interface. The Smart Office Assistant is for offices of all sizes and of various fields. Automating the office enhances the overall workplace experience, ensuring secure and efficient interactions for both employees and visitors. It takes more time for employees to manually sign in. Also visitors can easily book meetings or get information without anyone’s help using this technology. It helps to reduce administrative workload and staff can focus on more important tasks. To conduct this literature review of papers, we narrowed down the following areas relating AI-driven Smart Office Assistants; face recognition for attendance, Chatbot systems for visitor, integration of Artificial Intelligence in offices. The following databases were used: IEEE Xplore and Google Scholar; keywords included “AI office automation,” “face recognition attendance,” and “chatbot visitor management.”

관공서 스마트 오피스 구축에 관한 연구 : 사용자 조사 및 국내·외 사례조사 분석을 중심으로

In recent years, the social trend of emphasizing work-life balance (Work-Life Balance) and the introduction of the 52-hour workweek system have highlighted the need for smart offices focused on work efficiency. Public institutions are also actively pursuing the implementation of smart offices. This paper aims to provide guidelines for establishing smart offices in public institutions, based on field surveys and user studies of a public office currently undergoing relocation planning. Through a spatial usage survey of Public Office A, which is in the process of relocation planning, and user surveys and interviews with staff, this study identifies office space improvement needs and requirements. Furthermore, by analyzing domestic and international case studies of smart office implementations in public institutions, this paper offers practical directions for the establishment of smart offices in public institutions. Based on the surveys and interviews conducted, the study addresses the specific requirements of each department, ensures a clear understanding of and engagement with spatial needs, and incorporates insights from successful international examples. This paper highlights the importance of applying innovative spatial designs that can improve the current organizational culture, facilitating active communication and collaboration.

Smart Materials for Modern Facades: An AI-Powered Selection Process

Abstract According to the mid-21st century’s technological advancements, artificial intelligence has gained popularity as a transformative tool due to its immense integration in the evolving field of architecture. Evidently, it is being used in the building skin design to enhance the energy efficiency and to reduce the continuously rising energy waste and consumption problem in office buildings. Despite AI’s immense advancements, it lacks sufficient data with regards to the turf of smart skin design in office building for energy optimisation. Hence, this research aims to develop a framework for the optimal integration of artificial intelligence in the selection process of smart materials for energy efficient design for office buildings’ skin. To reach this aim, a mixed-method approach using quantitative and qualitative analysis will be implemented. First, theoretical research utilising archives and case study analysis is conducted to determine the various applications of AI in architecture, the components of a smart office building skin focusing on smart materials, the definition of energy efficiency and its measurement tools and the miscellaneous AI tools that are inaugurated to design an office building skin. As a result, a framework will be created by integrating the optimal AI tools in the selection process of smart materials for energy efficient smart skin design. Second, the applied studies are performed using empirical and experimental analysis to test and support the framework through case study analysis and comparison to test the framework obtained from the literature review aiming to cover the gap identified in the selection process of the materials.

Advanced smart HVAC system utilizing borehole thermal energy storage: Detailed analysis of a Uppsala case study focused on the deep green cooling innovation

This article presents and thoroughly examines an innovative, practical, cost-effective, and energy-efficient smart heating, ventilation, and air conditioning (HVAC) system. The fundamental component of this concept is a state-of-the-art method called Deep Green Cooling technology, which uses deep drilling to utilize the ground's heating and cooling potential directly without the need for machinery or heat pumps. This method satisfies demands with the least energy use, environmental impact, and operational costs. In order to effectively oversee and regulate energy production, storage, and utilization, the system consists of an intelligent control unit with many smart controllers and valves. Renewable energy deployment is made easier, and the intelligent automation unit is more compatible with the help of a high-temperature cooling resource with a high supply temperature of 16 °C. The technical, environmental, and financial aspects of the suggested smart office building system in the southern region of Uppsala, Sweden, are evaluated using TRNSYS software. According to the results, boreholes provide more than 28.5 % of the building's energy requirements by utilizing the ground's ability to generate affordable, dependable seasonal thermal energy. The district heating network satisfies the remaining demand, amounting to 787.2 MWh, highlighting the benefits of combining conventional and renewable energy sources for increased supply security and dependability. The borehole thermal energy storage system meets the building's entire cooling need, underscoring the importance of high-temperature cooling systems. The most expensive part of the system is the borehole thermal energy storage, which accounts for over half of the total investment. The system has an appropriate payback period of ten years, proving its long-term profitability and cost-effectiveness, thanks to removing the machinery and heat pump. With 3138 MWh of ground-source heating and cooling, the system saves 17,962 USD by reducing CO2 emissions by about 143.7 t, sufficient to grow 16.3 ha of trees throughout the payback period.

Leveraging the Integration of IoT Devices for Energy Optimization in Office Blocks

The integration of Internet of Things (IoT) devices in office buildings has emerged as a promising solution for enhancing energy efficiency and optimizing resource utilization. In the context of modern workplaces, the adoption of smart technologies offers a transformative approach towards achieving energy optimization goals. Internet connected smart devices are ubiquitous and hence opens new opportunities for optimising energy consumption within office blocks. This research paper explores the integration of IoT devices within an office environment to improve energy efficiency and sustainability. The research paper aims to review a simple and low-cost smart workplace model that employs IoT technology, cloud storage, and data analytics for real-time monitoring and analysis of energy consumption patterns within an office environment. The study examines the implementation of an integration of IoT sensors within an office environment enabling HVAC and lighting systems to dynamically adjust energy use based on real-time conditions such as occupancy levels, ambient temperature, and daylight availability. Through the identification of trends, anomalies, and opportunities for optimization, the study seeks to qualify adaptive control strategies that can effectually reduce energy waste and improve general operational productivity in office buildings. The findings from this research underwrite to the growing field of smart offices by demonstrating the potential of integrating IoT devices for energy optimization in modern office blocks.

IoT resource discovery based on multi faected attribute enriched CoAP: smart office seating discovery

IoT resource discovery based on multi faected attribute enriched CoAP: smart office seating discovery

Valuation of Internet of Energy (IoE) Platforms in Smart Cities: A Hybrid Multi-Criteria Decision Making Approach

A smart city is a place where people live using computing, sensing, and networking technology in a variety of ways, including smart buildings, smart homes, smart transportation, smart workplaces, smart offices, smart industries, and smart agriculture. These smart technologies need to connect so; we need to use the Internet of Things (IoT) that has applications in many areas of our smart lives. IoT has been used due to the growing usage of renewable energy sources and the challenges of network management. Internet of Energy (IoE) integrates the IoT and smart grid capabilities. The basic role of smart grids or IoE is an essential element in creating smart cities. In this work, we present some IoE platforms that represent the alternatives and their criteria. The proposed study treats the uncertainty of multi-criteria decision-making (MCDM) modified by single value triangular neutrosophic number (SVTrN) by choosing the optimal IoE platform based on the criteria importance through inter-criteria correlation (CRITIC) method and multi-attributive ideal-real comparative analysis (MAIRCA). The proposed model proved its effectiveness by applying it to an experimental case. The results show that the DCC (A3) platform is the best one.

BEET: Blockchain Enabled Energy Trading for E-Mobility Oriented Electric Vehicles

Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings.

Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning

Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning

Development of a Facenet Enhanced Secured Smart Office System

A secured smart office system is the one that is capable of recognizing and granting access to authorized persons only and manage the office appliances autonomously. The goals are access control, security and automation. Over the years, several studies have been carried out to meet these needs using RFID cards, access codes and biometrics resulting in weak security with long computational period. Switching of electrical appliances and smoke detection in case of fire outbreak were used but real time electrical appliances management that could prevent fire outbreak is yet to be achieved. This research focused its attention on the design and implementation of a smart office system that meet these needs. The system was developed using a raspberry pi 4 board. Ultrasonic sensor, camera, servo motor, relay, current and voltage sensors were interfaced with the raspberry pi for image capturing, opening the door, switching and power monitoring respectively. The system captures the image of an approaching person and process it for recognition using FaceNet; an open source model for face recognition. Information was transmitted via SIM800L GSM module as SMS to the administrator. The system shuts down the office electrical network once the supply voltage exceeds 220v ac or less than 161v ac, thus preventing any chance of fire outbreak due to irregular power supply. The accuracy of image recognition model was 93.13%. This research has shown a simple way of implementing an autonomous smart office system that is capable of providing adequate security, efficiency and convenience in offices.

IMPLEMENTATION OF INNOVATIVE TECHNOLOGIES IN THE EDUCATIONAL PROCESS: CREATING AN INTELLIGENT COMPUTER SCIENCE CLASSROOM AT THE UNIVERSITY

The purpose of this paper is to highlight the issues related to the creation of "smart" computer science classrooms based on microelectronics in higher education institutions. A "smart" computer science classroom is a fully automated educational environment that operates in three modes: "standard", "automatic" and "automatic energy saving". The smart classroom can be controlled using smartphones, personal computers, and remote controls. The "smart" cabinet, designed for computer science, is equipped with a number of sensors, electronic parts and indicators based on ArduinoUNO, MEGA and ESP8266- 12E WiFi modules. Built-in sensors and indicators of the smart office measure the microclimate in the office and classroom, and display information for demonstrations and laboratory work. The office consists of three modules: "Information", "Executive" and "Demonstration", which are controlled by an ATMEL microcontroller. The Demonstration module is designed to easily and quickly connect various sensors and components to solderless boards. It uses the open Arduino programming platform. Smart sensors can be used in computer science classrooms to monitor variable environmental parameters both indoors and outdoors (such as temperature, humidity, pressure, light level, carbon dioxide and other gases in the atmosphere). In addition, you can remotely control peripheral devices such as TVs, projectors, lamps, electrical outlets, and curtains. All three modules are integrated into a wireless local area network using a star topology for radio communication between the modules. The main components of the system are executive modules that have access to the Internet, hardware, technology, and software tools. The use of the technical base in a higher education institution involves the creation of "smart" computer science classrooms for higher education institutions.

A machine learning based framework for IoT devices identification using web traffic.

Identification of the Internet of Things (IoT) devices has become an essential part of network management to secure the privacy of smart homes and offices. With its wide adoption in the current era, IoT has facilitated the modern age in many ways. However, such proliferation also has associated privacy and data security risks. In the case of smart homes and smart offices, unknown IoT devices increase vulnerabilities and chances of data theft. It is essential to identify the connected devices for secure communication. It is very difficult to maintain the list of rules when the number of connected devices increases and human involvement is necessary to check whether any intruder device has approached the network. Therefore, it is required to automate device identification using machine learning methods. In this article, we propose an accuracy boosting model (ABM) using machine learning models of random forest and extreme gradient boosting. Featuring engineering techniques are employed along with cross-validation to accurately identify IoT devices such as lights, smoke detectors, thermostat, motion sensors, baby monitors, socket, TV, security cameras, and watches. The proposed ensemble model utilizes random forest (RF) and extreme gradient boosting (XGB) as base learners with adaptive boosting. The proposed ensemble model is tested with extensive experiments involving the IoT Device Identification dataset from a public repository. Experimental results indicate a higher accuracy of 91%, precision of 93%, recall of 93%, and F1 score of 93%.

Assessment of the effectiveness of the introduction of technology “Smart Home” in buildings with built-in office premises

The development of the idea of “Smart Office” technologies within the “Smart Home” received during the COVID-19 pandemic. Comfortable staying at home during the pandemic, upon its completion, prompted the idea of promoting the idea of creating more advanced, motivating and productive workplaces. The article shows the relevance of using “Smart Home” technologies for buildings with built-in office premises. Their positive and negative sides, the most promising options for solutions for use in office premises are considered. The article considers such technologies as “Smart Intercom”, “Smart Lighting”, “Smart video surveillance”, “Smart Climate Control”, their operating principles. The economic efficiency of using thermostats applicable to the designed building is considered. The calculation of the main indicators of economic efficiency was performed: net present value (NPV), internal rate of return (IRR), profitability index (PI) and payback period. The result of the calculation is graphically shown.

Industry 4.0 technologies and Lean Office: perspectives to Smart Office

Industry 4.0 technologies and Lean Office: perspectives to Smart Office

Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices.

Simultaneous Data Dissemination Among WiFi and ZigBee Devices

Recent advances in Cross-Technology Communication (CTC) have opened a new door for cooperation among heterogeneous IoT devices to support ubiquitous applications, such as smart homes and smart offices. However, existing work mainly focuses on physical layer performance improvements. In this paper, we explore how to leverage the latest CTC techniques for network layer performance improvements. Specifically, we introduce Waves, which leverages WiFi to ZigBee CTC and WiFi access point’s adaptive transmit power control techniques for reliable and fast data dissemination in low-duty-cycle ZigBee networks. We extensively evaluate our design under various settings. Evaluation results show that Waves can provide reliable data dissemination and is 33.5 times faster than the state-of-the-art protocol in terms of dissemination time.

Network Traffic Attention Features Fusion for Unauthorized IoT Devices Detection

The demand for using IoT devices is increasing because of its usefulness. IoT devices are present in all areas of life. They make life easier and more comfortable. Moreover, many companies and households are tending to increase in the use of IoT devices to turn on/off home, school, office into smart home, smart school, smart office and so on. Although IoT devices are very useful, they have major security problems because they are not interested, leading to a large number of security holes and creating opportunities for hackers to attack the systems. In this paper, we develop an attention features fusion (AFF) method for the purpose of classifying and detecting IoT devices that are not in the pre-registered white list. Our recommended technique finds the features with the best classifier ability, then converts them into images and uses ensemble learning based on the baseline models which are deep learning models to detect and classify. Our proposed solution helps to detect strange devices that are exchanging data in the IoT network, reducing the risk of the system being attacked by hackers. According to the experimental results, our method achieves very good results when we detect ten IoT devices with up to 97.85% accuracy

Biometric face authentication system for secure smart office environments

<span>The use of the smart office concept in security has increased significantly recently. One of the areas of concern is the use of facial finger biometric technology for authentication systems, for example, authentication to enter a particular room in an office. This paper aims to describe a new prototype for office door automation and security that combines facial biometric technology and NodeMCU. Hopefully, this system will help increase the safety and comfort of office employees with easy installation and low cost. This system automatically controls (opens or closes) the door based on the biometrics of the user’s face registered in the database on the NodeMCU microcontroller. The main system comprises a NodeMCU microcontroller, face sensors, and a door lock system.</span>

High speed 60 Gbps RGB laser based-FSOC link by incorporating hybrid PDM-MIMO scheme for indoor applications

Abstract Visible Light Communication (VLC) systems enhanced by red, green and blue (RGB) lasers are at the forefront of indoor technology, offering dynamic lighting, high-speed data transfer, and energy efficiency. This innovative combination not only revolutionizes connectivity and illumination but also ensures privacy and security, making it a game-changer for smart homes, offices, and various indoor applications. In our research, we introduce a polarization division multiplexing and Multiple Input Multiple Output based (PDM-MIMO) system that carries 60 Gbps of data over a transmission range of 500 m in free space Channels. The utilization of the cost-effective on-off key (OOK) modulation format is attributed to its affordability in our transmission scheme. For parallel data transmission, three laser diodes in RGB were utilized. To enhance both the transmission range and reduce the Bit Error Rate (BER), MIMO scheme is employed. Our study presents simulation outcomes, conducted using OptiSystemTM software, that focus on evaluating the bit error rates for the proposed PDM-MIMO link. Our findings demonstrate successful 60 Gbps data transmission over 350 m in FSO with an acceptable BER, reinforced by clear eye diagrams. Introducing MIMO expands the range to 500 m while improving BER, paving the way for real-time experimentation and research advancement.

Channel Assignment Method for Maximizing Throughput in the Internet of Things System

The growth of the number of interconnected wireless devices such as the Internet of Things (IoT) is continuously increasing across various sectors, including smart buildings, smart offices, smart cities, and others. According to estimates, by the year 2030, there will be at least 50 billion devices interconnected through networks. The escalating number of uncontrolled wireless devices can lead to various issues such as interference, collisions, and data loss, resulting in an overall decline in network system performance. This study aims to propose a scenario as an alternative solution to optimize the overall network performance in the system by assigning channels to each interconnected wireless pair to reduce the impact of interference. This research indicates that the proposed method successfully enhances the system throughput performance by 39.75% compared to the condition where all wireless pairs operate on the same channel, thereby outperforming several other comparative methods.