Search

This paper presents a novel, sensor-enabled centralised monitoring system for fault detection in streetlights. Unlike traditional methods that depend on citizen reports or manual inspections, this system utilizes Internet of Things (IoT) technology to identify faults automatically and efficiently. The system comprises monitoring devices equipped with sensors and transceivers attached directly to streetlights. These devices collect data on light source functionality and potential wiring faults. This data is then wirelessly transmitted to a central transmitter, which relays it to a cloud platform. A machine learning model, trained with an accuracy of 98.8%, analyzes the data and predicts the streetlight's operational status. The predicted status and detailed fault information are displayed on a dedicated webpage, allowing stakeholders to monitor remotely. This eliminates manual searches, expedites repairs due to precise fault knowledge, and empowers linemen for faster resolution. Overall, this cost-effective system significantly improves streetlight maintenance efficiency, enhancing public safety and contributing to a well-lit urban environment. [ABSTRACT FROM AUTHOR]

A large-scale street lights send data to the cloud platform, causing load imbalance among nodes of the server in high concurrency, resulting in communication failure. In view of this problem, based on microservices, this study proposes a dynamic weight load balancing algorithm based on the smart street lamp cloud platform. The algorithm calculates the respective weight coefficients and the initial weights of each node according to the hardware performance of each server during initialization, and dynamically adjusts the server weights according to the idle rate of CPU(Central Processing Unit) and network bandwidth during the request process to realize load optimization. By setting the minimum threshold and comparing it with the calculated remaining load rate, the weight of the server that reaches the upper limit of the load is set to 0 to prevent the server from being overloaded. The test results show that the proposed algorithm has a better load balancing effect than the minimum number of connections algorithm and the smooth weighted round robin algorithm in the experimental environment. Compared with the dynamic weight algorithm, the average response time and the actual number of concurrent connections of the proposed method are also improved. [ABSTRACT FROM AUTHOR]

The industrial internet of things (IIoT) encompasses smart devices, manufacturing systems, humans, and networks for automated productive outcomes. The placement of devices and networks is vulnerable to distributed denial of service (DDOS) attacks that degrade the productivity and efficiency of IIoT. In this article, we propose a checkpoint‐intrigued adversary mitigation scheme (CIAMS) for improving the security features and recommendations of the detection systems. Features that use the recommendation to provide relevant information maintain their security level. A DDoS attack is dealt with at the outset, resulting in increased productivity. The IIoT's smart devices are less productive and efficient because of this DDoS attack. This CIAMS method is designed to address vulnerability and the ability to survive the features checkpoints. The proposed scheme substantiates the security breach and lag in the checkpoint systems against DDOS attacks. The checkpoints' vulnerability level and surviving features are assessed using a classified learning approach. In this assessment, the degrading features are reimbursed by improving the security functions, control, and access methods. Periodic checkpoint replacement and mutual security measures are used for mitigating the prolonging DDOS impact in the network. The proposed scheme's performance is verified using false positives, service distribution, lag, an efficiency score. Improvements have been made to the industrial environment's service delivery and efficiency. By reducing false positives by 10.35%, the proposed scheme improves service distribution ratio and efficiency score by 11.68% and 12.55% for different devices. [ABSTRACT FROM AUTHOR]

With the emergence of smart home appliances, traditional power sockets are becoming less compatible with modern living styles. Furthermore, modern commercialized sockets are expensive and unaffordable. This project presents the development of a low-cost Wi-Fi smart home socket using internet of things (IoT) technology that is user-friendly for smartphone users to control home appliances. Smart home socket devices can turn on and off power outlets automatically from any location if they are linked to the internet and providing the user with more convenience and energy savings. This project uses a node microcontroller unit (NodeMCU) Wi-Fi module (ESP8266) as the main microcontroller unit to connect to a cloud platform. It also uses a mobile phone application to send instructions to the microcontroller for turning on and off household appliances remotely through a smart socket. The switching mechanism is monitored and controlled through the Blynk platform. A 4-channel relay module is used to transition DC current loads to AC current loads in order to activate switching processes. According to the study's findings, the Wi-Fi smart home socket system is able to save on excessive usage of electrical appliances while also increasing electrical appliance safety. [ABSTRACT FROM AUTHOR]

In recent years, the demand for efficient and sustainable agricultural practices has been leveraged, leading to smart farming practices. These practices aim to enhance agricultural processes and productivity and minimize resource waste. One of the crucial challenges faced by farmers is the uneven distribution of soil humidity and pH across their agricultural lands. Further, the irregularity in soil moisture content and pH can lead to poor crop performance, water wastage, and increased resource utilization. In this work, an Internet of Things-based smart sensor node was developed, which consists of humidity and pH sensors to ensure the efficient management of water and soil conditions across an entire farm. Also, an array of humidity and pH sensors were placed across the farm, and these units worked independently as they have their own controller and battery unit. The developed device was integrated with a solar cell, which charged the battery. Further, the data acquired from these sensors were wirelessly transmitted to the base station, and it gathered the information of each unit, including their humidity levels, pH values, signal strength, and energy supply. This information was processed at the base station, and a graphical overview of the farm with the acquired information was represented, which provides farmers with real-view insight to identify areas with poor humidity and pH conditions. These data were transmitted to an IoT cloud, offering the farmer the ability to monitor their farm from a remote location. In cases where humidity levels dropped drastically and remained unchecked for more than two hours, the system triggered an alert. This mechanism makes sure that farmers are notified of potential issues, allowing them to prevent crop damage and optimize their resource usage. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes an IoT‐enabled soil monitoring system using wireless sensor network for automatic irrigation in agricultural applications, especially for lemongrass plants, where an automated control system is required for irrigation applications. This can solve the problem of the water crisis, which is faced by the farmers during the cultivation of the crop in the field. This controls the water supply in the irrigation process using an IoT communication system. A system architecture for soil monitoring and controlling irrigation using IoT technique is designed where the different sensors and actuators like humidity, soil moisture, temperature, pump, and so forth are connected with a node microcontrol unit and message queuing telemetry transport (MQTT) protocol for enhancing communication capabilities. This wireless sensor network gives feedback to the system. This provides automation by the on/off pump system during drip irrigation. The sensor data are displayed on a PC or mobile phone through wireless communication and an IoT cloud platform. An experimental testing setup is developed and the experimental performance of a soil monitoring system using IoT technique for automatic drip irrigation has been carried out and soil moisture data are also stored in a cloud server for analytics. The performance shows that the MQTT protocol sends data within 48 s to the IoT cloud so that the data can be acquired in a faster manner. This shows that this kind of soil monitoring system is suitable for automatic drip irrigation, which enhances the farming process and overcomes the water crises in the agricultural system by reducing the wastage of water. [ABSTRACT FROM AUTHOR]

With environmental issues arising from the excessive use of fossil fuels, clean energy has gained widespread attention, particularly the application of lithium-ion batteries. Lithium-ion batteries are integrated into various industrial products, which necessitates higher safety requirements. Narrowband Internet of Things (NB-IoT) is an LPWA (Low Power Wide Area Network) technology that provides IoT devices with low-power, low-cost, long-endurance, and wide-coverage wireless connectivity. This study addresses the shortcomings of existing lithium-ion battery pack detection systems and proposes a lithium-ion battery monitoring system based on NB-IoT-ZigBee technology. The system operates in a master-slave mode, with the subordinate module collecting and fusing multi-source sensor data, while the master control module uploads the data to local monitoring centers and cloud platforms via TCP and NB-IoT. Experimental validation demonstrates that the design functions effectively, accomplishing the monitoring and protection of lithium-ion battery packs in energy storage power stations. [ABSTRACT FROM AUTHOR]

The growing number of vehicles in a densely populated smart city results in a significant lack of parking space. During the implementation of systems for visibility of parking space vacancies for drivers, the bulk of the systems are focused on expensive dedicated sensor devices, requiring high installation costs. The emergence of a relatively inexpensive internet of things (IoT) system allows embedded cameras to track parking spaces' utilisation. However, parking space positions' manual specification before drivers can use such devices after implementation is important even for camera-captured images. Hence in this paper, IoT assisted intelligent parking system (IoT-AIPS) with cloud platform has been proposed to reduce vehicle parking waiting time and enhance accurate vehicle position prediction. The proposed method utilizes the machine learning method to classify topologies in the parking space based on stationary location. [ABSTRACT FROM AUTHOR]

Reputation evaluation is an effective measure for maintaining secure Internet of Things (IoT) ecosystems, but there are still several challenges when applied in IoT-enabled pumped storage power stations (PSPSs), such as the limited resources of intelligent inspection devices and the threat of single-point and collusion attacks. To address these challenges, in this paper we present ReIPS, a secure cloud-based reputation evaluation system designed to manage intelligent inspection devices' reputations in IoT-enabled PSPSs. Our ReIPS incorporates a resource-rich cloud platform to collect various reputation evaluation indexes and perform complex evaluation operations. To resist single-point attacks, we present a novel reputation evaluation model that combines backpropagation neural networks (BPNNs) with a point reputation-weighted directed network model (PR-WDNM). The BPNNs objectively evaluate device point reputations, which are further integrated into PR-WDNM to detect malicious devices and obtain corrective global reputations. To resist collusion attacks, we introduce a knowledge graph-based collusion device identification method that calculates behavioral and semantic similarities to accurately identify collusion devices. Simulation results show that our ReIPS outperforms existing systems regarding reputation evaluation performance, particularly in single-point and collusion attack scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Solar energy, as a prominent clean energy source, is increasingly favored by nations worldwide. However, managing numerous photovoltaic (PV) power generation units via wired connections presents a considerable challenge. The advent of the Internet of Things (IoT) and cloud service technologies has facilitated the creation of an efficient and convenient PV grid-connected management system. This paper investigates IoT technology and PV grid-connected systems, integrating wireless sensor network technology, cloud computing service platforms and distributed PV grid-connected systems. We propose a Zigbee wireless network featuring ad hoc network functionality and Narrow Band Internet of Things (NB-IoT) smart gateway with multi-protocol and multi-device support. This system enables the collection and uploading of PV grid-connected system data to cloud service platforms, addressing daily operation and maintenance as well as intelligent management of distributed PV power stations. Furthermore, it expedites the aggregation and analysis of PV power generation data, streamlining power marketing across various regions. [ABSTRACT FROM AUTHOR]

In order to improve the test efficiency of concrete strength and ensure measured data reliability, we present a novel intelligent rebound hammer system which is based on the Internet of Things (IoT) and speech recognition technology. The system uses a STM32F103C8T6 microcontroller as the Main Control Unit (MCU), and one BC26 module as the communication unit, combined with a LD3320 voice recognition module and TOF050H laser ranging sensor to achieve the function of phonetic transcription and laser ranging. Without the need for traditional multi-person collaboration and burdensome data transfer, the system can collect the data of rebound value and location information and send them to the remote cloud information management system automatically in real time. The test results show that the system has high measuring accuracy, good data transmission stability and convenient operation, which could provide guidance for other types of non-destructive testing equipment designs. [ABSTRACT FROM AUTHOR]

Worldwide, buildings consume about 40 percent of the overall energy resources and contribute to an average of 30 percent of the global carbon emission. Hence, technologies for improving the energy efficiency of buildings play an essential role in the global fight against climate change. The CELSIUS project aims to improve the energy efficiency and indoor climate of medium to large sized commercial and public buildings by developing an integrated system solution that consists of (1) an IoT-enabled and cloud-based platform for monitoring and diagnostics of building energy performance and indoor climate quality, (2) a middleware software platform for cost-effective large-scale deployment of wireless sensors and gateways, and (3) an IoT network management platform for cost-efficient life-cycle maintenance of sensors and gateways. The integrated system solution will be deployed and demonstrated in a 6000 m2 building in Aarhus, Denmark, and an 18,000 m2 building in Kuala Lumpur, Malaysia. By choosing buildings located in different climate zones on different continents allows the developed system solution to be tested under realistic conditions for the international export market. [ABSTRACT FROM AUTHOR]

Traditional embedded prediction system is susceptible to the influence of field conditions with its own hardware. The current photovoltaic power generation system has a short monitoring distance, high power consumption, low data acquisition accuracy, and poor stability of the embedded prediction system. A photovoltaic power station monitoring system is designed with the internet of things cloud platform as the main framework and LoRa as the main communication technology. By deploying services in the cloud and using GA-Elman neural network model, this system can reduce the cost of field hardware and improve the overall prediction accuracy. This system is tested to monitor the power-on capacity of 19. 9 kW photovoltaic matrix in Wudang Lake photovoltaic power station in Hubei. Experiments show that the system can run reliably for a long time, and monitor every data of photovoltaic power station in real time, with high accuracy and scalability in power prediction. [ABSTRACT FROM AUTHOR]

Rising electricity bills as a result of climate variability and new home electrical and electronic appliances are becoming a major source of concern for most end users. Consumers are typically unaware of their household electricity consumption patterns and the costs associated with them, making proper planning and budgeting difficult. Monitoring and controlling energy consumption on appliances can reduce energy costs for end-users. The Internet of Things (IoT) has the potential to provide remote monitoring and control of devices via automated monitoring and control. In this study, we propose an IoT-enabled system for monitoring and controlling energy consumption in homes to save money and make electricity more affordable to low-income people. Autonomous sensor nodes attached to power outlets collect and route power consumption data to the GSM-enabled gateway. Data aggregation is performed by the gateway for data received from all end-nodes within its coverage area. The gateway sends the data to the cloud, where real-time data analytics is performed. We also developed an algorithm that enables users to not only understand their usage patterns but also remotely control their appliances at any time. Mobile phones are used to remotely turn on and off household appliances via a machine-to-machine interface, as well as to provide real-time visualization of household energy consumption patterns. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the problems that the traditional single-chip microcomputer experiment can only see the experimental results through the hardware circuit board in a fixed place, and the experimental data cannot be reproduced and analyzed, a set of innovative single-chip microcomputer experiment system based on the Internet of Things is designed in the proposed study. The system consists of Internet of Things experimental platform, experimental server and experimental board. The experimental platform uses STM32F205 chip to process and display the data of the experimental board, and uses NB-IoT technology to upload the data. The experimental server is based on B/S architecture and built on Alibaba Cloud to realize the forwarding processing and reproduction of experimental data. The experimental board uses MSP430G2553 chip and sensor chip to realize data collection and communication. The system realizes the remote display of data by designing the function processing, communication protocol and interface of each part, so that students can analyze the historical data at any time, to better find out the problems in the experiment, and teachers can give guidance more conveniently. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the integration of new-generation information and communication technology (ICT) in the manufacturing industry, many advanced manufacturing modes have been proposed. Cloud manufacturing (CMfg) is one example which integrates manufacturing resources and the provision of on-demand services using new technologies, such as Internet of Things (IoT), Big Data and cloud computing. CMfg has experienced rapid development from its initial theory and technology to its application and has attracted considerable attention since its introduction in 2010. Many scholars in China have conducted significant research on CMfg. Therefore, it is significant to review the achievements from Chinese scholars to examine the current progress of CMfg research in China. This paper presents the definitions of CMfg found in the literature and summarizes the developments regarding eight dimensions of CMfg. Several successful implementations of CMfg in China are discussed and compared. Furthermore, the strengths, weaknesses, opportunities and threats (SWOT) method is used to analyze the potential impacts of CMfg on the future development of the China manufacturing industry. [ABSTRACT FROM AUTHOR]

Accurate and reliable fault detecting plays a key role in application of grid‐connected wave power generation systems. This study presents a novel IoT‐based approach to condition monitoring of the wave power generation system, which has faster operating rate and lower hardware requirement. The compressed sensing (CS) method is adopted to compress the data, which aims to reduce the data uploaded to cloud platform; and then, the extreme learning machine (ELM) algorithm is used to achieve the condition monitoring of wave power generation system in cloud platform. In order to validate the effectiveness of the proposed method, the IoT‐based wave power generation condition monitoring system test platform is established. The experiment results illustrate the high efficiency and reliability of proposed method. The proposed method has a potential of practical applications. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has started to appear everywhere in many shapes and forms. But security is one of the crucial topics that could trip up the growth of the IoT. Following security principles used in enterprise computing can help clear that issue. Already there are more connected devices than people on the planet, according to leading researchers in this area. By 2020, there will be 50 billion connected devices, outnumbering people by more than 6 to 1. Most of these devices will be controllable over the Internet, and they will increasingly be responsible for collecting and transmitting sensitive data. Today consumers might own an app that collects information on their exercise routine. In a few years, those same people might have an Internet-enabled medical device that continually delivers data to their doctor. In the wrong hands, data from home management systems could be used to assess user's whereabouts. Likewise, businesses could be vulnerable when they connect things like HVAC, irrigation, or commercial appliances. [ABSTRACT FROM AUTHOR]

The integration of Cloud Computing into Internet-of-Things (IoT) could solving heterogeneity problems with its devices, protocols and technologies. IoT Cloud-based platforms also offer reliability, scalability, and security features in terms of reduced costs. For building an IoT Cloud platform we proposed a three-layered model and we emphasized the functions of the Fog/Edge and Cloud Application levels, leveraging on Cloud Computing. Also, we described several platforms existing on IT market and we presented several pros and cons arguments for using IoT Cloud platforms in business, public administration and healthcare. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to promote the integrated application of the Internet of Things and cloud platform computing technology in intelligent manufacturing system, a cloud platform of cutting database was established in this paper. With the help of multi-sensor and distributed storage technology, mathematics model and data mining algorithm, functions of fast accessing and storing cutting data, intelligent modeling and analysis, efficient mining and optimizing processing parameters can be realized in the system, solving the key problems of analyzing cutting force and controlling surface quality in turning difficult-to-cut materials. Data equipment and users in the system were connected as a whole through wireless network, which can provide intelligent service of data querying, modeling, and optimizing through the cloud server. The study made up for the drawbacks in current intelligent database system such as oversimplified platform architecture, low level of intelligence, and low utilization of historical data, providing an efficient method of managing and applying big data in cutting process for the production system. [ABSTRACT FROM AUTHOR]

Nowadays, governments and companies are looking for solutions to increase the collection level of various waste types by using new technologies and devices such as smart sensors, Internet of Things (IoT), cloud platforms etc. In order to fulfil this need, this paper presents solutions provided by a research project involving the design, development and implementation of fully automated waste collection systems with an increased usage degree, productivity and storage capacity. The paper will focus on the main results of this research project in turning the automated waste collection system into a smart system so that it can be easily integrated in any smart city infrastructure. For this purpose, the Internet of Things platform for the automated waste collection system provided by the project will allow real time monitoring and communication with central systems. Details about each module are sent to the central systems: various modules' statuses (working, blocked, needs repairs or maintenance etc.); equipment status; storage systems status (allowing full reports for all waste types); the amount of waste for each module, allowing optimal discharging; route optimization for waste discharging etc. To do that, we describe here an IoT cloud solution integrating device connection, data processing, analytics and management. [ABSTRACT FROM AUTHOR]

With the development of network technology, the Internet portals have been constantly changing. However, such portals cannot meet the requirements of Internet of Everything (IoE) communication between people and things or between things themselves. Inspired by the ideal of container, Mobile Cross-platform Application Development Framework (MCADF) and Platform as a Service (PaaS), we designed a "Cloud + Container" portal platform for IoE. The cloud provides application development, testing, deployment, operation, management, and other functions. It is also responsible for data storage, management and analysis. The terminal of user container is used to carry and manage mass applications and IoE data, and provide user access entry. In this paper, we first introduce the background of IoE and related problems, then give the detailed design of the platform. Finally, we evaluate the performance of the platform comparing with other platforms. [ABSTRACT FROM AUTHOR]

With the expansion of smart meters, like the Advanced Metering Infrastructure (AMI), and the Internet of Things (IoT), each smart city is equipped with various kinds of electronic devices. Therefore, equipment and technologies enable us to be smarter and make various aspects of smart cities more accessible and applicable. The goal of the current paper is to provide an inclusive review on the concept of the smart city besides their different applications, benefits, and advantages. In addition, most of the possible IoT technologies are introduced, and their capabilities to merge into and apply to the different parts of smart cities are discussed. The potential application of smart cities with respect to technology development in the future provides another valuable discussion in this paper. Meanwhile, some practical experiences all across the world and the key barriers to its implementation are thoroughly expressed. [ABSTRACT FROM AUTHOR]

In view of the monotonicity and the lack of professional experiment for the Internet of Things, a combination of wireless sensor networlc and the Internet of Things integrated training platform is designed. The platform takes a three layers modular architecture design, i.e., perception layer, transport layer and control layer. The perception layer which is composed of sensor networlc fusion processes multi sensor data and sends to the gateway node, the gateway node builds a two-way connection with the cloud platform by using TCP/IP to complete the two-way communication of data chain and realized the cloud platform data display and remote control function. Finally, the cloud platform, APP, SMS and the terminal control and display are realized. It has played an important role on the network of laboratory construction. [ABSTRACT FROM AUTHOR]

The Energy Internet paradigm is the evolution of the Internet of Things concept in the power system. Microgrids (MGs), as the essential element in an Energy Internet, are expected to be controlled in a corporative and flexible manner. This paper proposes a novel decentralized robust control strategy for multi-agent systems (MASs) governed MGs in future Energy Internet. The proposed controller is based on a consensus algorithm applied with the connected distributed generators (DGs) in the MGs in the energy internet paradigm. The proposed controller's objectives are the frequency/voltage regulation and proportional reactive/active power-sharing for the hybrid DGs connected MGs. A proposed two-level communication system is implemented to explain the data exchange between the MG system and the cloud server. The local communication level utilizes the transmission control protocol (TCP)/ internet protocol (IP) and the message queuing telemetry transport (MQTT) is used as the protocol for the global communication level. The proposed control strategy has been verified using a hypothetical hybrid DGs connected MG such as photovoltaic or wind turbines in MATLAB Simulink environment. Several scenarios based on the system load types are implemented using residential buildings and small commercial outlets. The simulation results have verified the feasibility and effectiveness of the introduced strategy for the MGs' various operating conditions. [ABSTRACT FROM AUTHOR]

There will be a dearth of electrical energy in the world in the future due to exponential increase in electrical energy demand of rapidly growing world population. With the development of Internet of Things (IoT), more smart appliances will be integrated into homes in smart cities that actively participate in the electricity market by demand response programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, the energy management strategy using a price-based demand response program is developed for IoT-enabled residential buildings. We propose a new EMS for smart homes for IoT-enabled residential building smart devices by scheduling to minimize cost of electricity, alleviate peak-to-average ratio, correct power factor, automatic protective appliances, and maximize user comfort. In this method, every home appliance is interfaced with an IoT entity (a data acquisition module) with a specific IP address, which results in a wide wireless system of devices. There are two components of the proposed system: software and hardware. The hardware is composed of a base station unit (BSU) and many terminal units (TUs). The software comprises Wi-Fi network programming as well as system protocol. In this study, a message queue telemetry transportation (MQTT) broker was installed on the boards of BSU and TU. In this paper, we present a low-cost platform for the monitoring and helping decision making about different areas in a neighboring community for efficient management and maintenance, using information and communication technologies. The findings of the experiments demonstrated the feasibility and viability of the proposed method for energy management in various modes. The proposed method increases effective energy utilization, which in turn increases the sustainability of IoT-enabled homes in smart cities. The proposed strategy automatically responds to power factor correction, to protective home appliances, and to price-based demand response programs to combat the major problem of the demand response programs, which is the limitation of consumer's knowledge to respond upon receiving demand response signals. The schedule controller proposed in this paper achieved an energy saving of 6.347 kWh real power per day, this paper achieved saving 7.282 kWh apparent power per day, and the proposed algorithm in our paper saved $2.3228388 per day. [ABSTRACT FROM AUTHOR]