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Smart grid represents an advanced and interconnected system that incorporates modern technologies to enhance efficiency, reliability and sustainability. In comparison to the conventional grid, the smart grid (SG) uses many cutting-edge technologies. This research introduces a fog computing encryption-based model for privacy preservation in the smart grid model. By using different advanced technologies, our model addresses the balance between privacy, security, effectiveness and functionality. The model facilitates efficient communication and function inquiry while mitigating challenges posed by massive Internet of Things (IoT) systems in the smart grid environment. Specifically, it tackles the secure data consolidation challenge by employing encryption-based techniques for transmitting private data from smart meters to fog devices. These devices consolidate the data before updating to cloud. Conventional data consolidation approaches for SGs have high computation and communication costs and suffer from high storage requirement. The proposed model resolves these issues; algorithms for data consolidation and extraction of data at fog device and cloud respectively to obtain the secure communication has also been included. The performance of the developed mechanism has been computed against existing data consolidation mechanisms GCEDA (Grouping of Clusters for Efficient Data Aggregation), SPPDA (Secure Privacy-Preserving Data Aggregation) and LPDA (Lightweight Privacy-preserving Data Aggregation) for numerous performance parameters. And the results proves that the performance of developed mechanism with respect to bytes of storage, communication cost and ratio of transmission is better than the existing ones. [Display omitted] • Encryption-based data consolidation strategy for 5G in fog computing is presented. • Data consolidation and data extraction algorithm at fog devices and cloud servers. • A comparative analysis based on storage, communication and transmission cost is done. [ABSTRACT FROM AUTHOR]

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device's continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%. [ABSTRACT FROM AUTHOR]

This document provides an overview of the advancements in energy harvesting technologies and their integration into the Internet of Things (IoT) and wireless sensor networks. It includes 31 papers covering various energy harvesting devices, optimization techniques, and potential applications. The research aims to improve the efficiency and sustainability of wireless sensor networks while minimizing energy consumption. The articles explore different methods of energy harvesting, such as solar, thermal, vibrational, and radio frequency energy, and discuss their application in various fields, including wearable devices, solar energy harvesting, and IoT-enabled smart meters. Overall, this collection offers a comprehensive overview of the current research and advancements in energy harvesting for sensor networks. [Extracted from the article]

Global water use has grown steadily in the past decade, and climate change will further worsen the levels of water stress. Another global issue lies in the ageing of water infrastructures, registering conspicuous water losses (40% in the Italian scenario). A great opportunity is represented by the exploitation of new technologies relating to IoT, namely smart meters, which can enable the prediction of water consumption, detect leaks, and customise the service. This paper assesses the economic and environmental impact of smart meters adoption for water consumption, by taking the Italian scenario as a reference context, through the development of an analytical model which considers the benefits and costs associated with their adoption, with the ultimate objective of evaluating the convenience of the investment. Data to feed the model was collected through secondary sources, literature reviews, and interviews with utility companies' employees. Results provide corroborating evidence of the positive impact of smart water meter adoption, both in economic and environmental terms, in particular by increasing the roll-out number, given the higher amount of data available and economies of scale to be exploited. The present study contributes to the academic literature by providing a comprehensive model that considers economic and environmental aspects of smart water adoption, which allows practitioners to have an insightful understanding of the involved variables in such investments. [ABSTRACT FROM AUTHOR]

This study's object is an energy efficiency of residential sector. The work is aimed at solving the task to improve the energy efficiency of the housing sector by devising technical solutions for monitoring and managing energy consumption and microclimate parameters of buildings. The proposed proactive management system for residential buildings consists of multi-sensors measuring CO2, temperature and humidity, smart meters of heat and electricity consumption, and smart plugs. The equipment is combined into single system through an integration controller with remote user access through an interactive web interface. A feature of the technical solution is the ability to collect, process, visualize, and archive data on the consumption of energy, as well as on the key parameters of the microclimate of residential premises. The advantages of the system are its flexibility due to the possibility of integrating additional devices during operation, as well as the use of standard communication protocols, which enables the interchangeability of component elements. The implementation and testing were carried out under the conditions of a real pilot site. The use of the system in practice confirmed the efficiency and stability of the operation, making it possible to obtain data on the parameters of energy consumption and microclimate and devising recommendations for reducing energy consumption at the pilot site. It was established that the microclimate meets the requirements of the standards (air temperature is about 22 °С while relative humidity does not exceed 60 %). Decrease in energy consumption can be achieved by reducing the temperature of the heat carrier in the absence of residents, as well as by considering the influence of weather conditions. During periods of residents activity, an excess of the permissible level of CO2 was recorded, therefore, automatic ventilation systems should be provided in the apartments. [ABSTRACT FROM AUTHOR]

The Internet of Things is an ecosystem that connects billions of smart devices, meters, and sensors. These devices and sensors collect and share data for use and evaluation by organizations in different industry sectors. Humans may use the IoT to live and work more intelligently and gain total control over their lives. Consequently, IoT can be used to connect devices and integrate them with new digital technologies for customers. On the other hand, smart utility companies in the electric, gas, and water sectors need to deliver services more efficiently and analyze their operations in a way that can help optimize performance, detect growing problems in real time, and initiate fixes to avoid unplanned service interruptions. Building actual smart metering networks is costly and time-consuming. Therefore, in this paper, a new Smart Utilities Traffic Scheduling Algorithm (SUTSA) is proposed. To minimize the system complexity, the model is based on narrowband power line communication, in which a wired hidden network sends data across power lines. A simulation is performed using OPNET Modeler 14.5 to evaluate the proposed model. The results proved that the proposed model is highly scalable and achieves full network-bandwidth utilization in different situations based on different application requirements. [ABSTRACT FROM AUTHOR]

Science and technology, with their miraculous advancements, have fascinated human life to such an extent that imagining a world without these innovations is hardly possible. While technology is on the rise, an increase in immoral activities should also be noted. From a technical perspective, "power theft" is a non-ignorable crime that is highly prevalent and directly affects the economy of a nation. Today's life is unimaginable without electricity, almost all things, equipment, and appliances used daily rely on electric power to run smoothly. Solar energy is an alternative but has limitations, such as environmental changes and high initial costs. From rural to urban areas and from domestic to industrial sectors, the use of electricity has increased, but so has power theft. Detecting and eradicating such crimes with the help of the developing scientific field is the "need of the hour." With these views, the paper was conceived and designed. The paper provides a complete and comprehensive tool to stop power theft, which is extremely simple to understand and easy to implement. [ABSTRACT FROM AUTHOR]

The Wi-SUN FAN (Wireless Smart Ubiquitous Network Field Area Network) standard is attracting great interest in various applications such as smart meters, smart cities and Internet of Things (IoT) devices due to the attractive features that the standard offers, such as multihop and mesh topologies, a relatively high data rate, frequency hopping, and interoperability between manufacturers. However, the process of connecting nodes in Wi-SUN FAN networks, which includes discovering, joining, and forming the network, has been shown to be slow, especially in multihop environments, which has motivated research and experimentation to analyze this process. In the existing literature, to measure network formation time, some authors have performed experiments with up to 100 devices, which is a costly and time-consuming methodology. Others have used simulation tools that are difficult to replicate, because little information is available about the methodology used or because they are proprietary. Despite these efforts, there is still a lack of information to adequately assess the formation time of Wi-SUN FAN networks, since the experimental tests reported in the literature are expensive and time-consuming. Therefore, alternatives such as computer simulation have been explored to speed up performance analysis in different scenarios. With this perspective, this paper is focused on the implementation of the Wi-SUN FAN network formation process using the Contiki-NG open source operating system and the Cooja simultor, where a functionality was added that makes it possible to efficiently analyze the network performance, thereby facilitating the implementation of new techniques to reduce network training time. The simulation tool was integrated into Contiki-NG and has been used to estimate the network formation times in various indoor environments. The correspondence between the experimental and numerical results obtained shows that our proposal is efficient to study the formation process of this type of networks. [ABSTRACT FROM AUTHOR]

In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy. [ABSTRACT FROM AUTHOR]

A reconfigurable patch loaded via a switching radio frequency (RF) diode has been designed, investigated, and fabricated. The fabricated microstrip is able to adjust among several frequencies. A spectrum analyzer is used to test and measure different modes of operations. The proposed antenna is offered a new solution of uncomplicated and incomplex design via eliminating the biasing circuit while maintaining the finest performance. Two fast-switching diodes have been inserted as a new electrical reconfiguration technique. Thus, the suggested antenna is skilled to function in different bands. An ultra-small size antenna of 31×21 mm2 has offered various operating frequencies such as S-band at 2.4 GHz, C-band at 4.97 GHz, 5.06 GHz, 5.18 GHz, 5.1 GHz, and 7.94 GHz, X-band at 10.73 GHz, 10.91 GHz, and 11.9 GHz, Ku-band at 13.52 GHz, 14.78 GHz, 14.97 GHz, 15.63 GHz, 16.4 GHz, 17.3 GHz, and 17.48 GHz, and K-band at 19 GHz. The manufactured reconfigurable patch antenna is proficient for several wireless technologies for instance industrial scientific medical band (ISM): Bluetooth, wirelessfidelity (Wi-Fi), ZigBee, internet of things (IoT), WiMAX, and smart power meters. Also, long term evolution (LTE), 5G applications, fixed wireless systems (FWS), and satellite communication applications. [ABSTRACT FROM AUTHOR]

The IoT-based Smart Energy Meter, powered by the ESP32-C3 microcontroller, is an innovative solution for monitoring and controlling electricity consumption for both residential and commercial utilization. This smart energy meter enables users to actively monitor their energy usage, thereby reducing costs and promoting eco-friendly practices. It utilizes non-intrusive sensors ZMPT101B and ZMCT103C to acquire real-time data on power consumption, which is then transmitted over Wi-Fi to a cloud-based server, accessible through an intuitive web or mobile interface. Additionally, the system offers power-saving recommendations, robust security measures, and the capacity for scalable expansion, making it an invaluable tool for optimizing energy consumption and contributing to a sustainable future. [ABSTRACT FROM AUTHOR]

In this manuscript, we propose a conformal and flexible meander dipole rectenna array for omnidirectionally harvesting ambient RF power for application in Internet of Things (IoT) water meters. The array unit consists of an antenna for RF power harvesting and a Schottky diode for converting the harvested RF power into DC power. The impedance between the antenna and diode is directly conjugated and matched using a meander structure and coupling loop. Traditional matching networks introduce additional losses, while direct conjugate matching maximizes power transmission efficiency and reduces energy losses. The elimination of the matching network simplifies the design of the rectenna, reducing the number of components and the overall size and weight. The rectenna unit is suitable for low-power ambient energy harvesting and operates at 2.45 GHz. The measured RF to DC conversion efficiency of the rectenna unit reaches 50% at 0 dBm. The rectenna array is formed by connecting eight antenna units in parallel, and units are affixed to the four surfaces of the water meter case to achieve omnidirectional RF environmental power harvesting. The output DC power of the array can be up to 1.3 mW at 100 µw/cm2 received power density. An energy management circuit (BQ25504) is designed to store, distribute, and manage the harvesting of RF power to power the IoT water meter efficiently. Measured results demonstrated that the proposed rectenna array exhibited excellent adaptability and application potential in IoT scenarios. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is the amalgamation of hardware, like sensors and trackers, which monitor several parameters of the environment or physical objects, and software that processes all the data gathered by hardware. Globally, the IoT market is anticipated to reach 53.8 billion USD by 2025. This enhancing demand is due to its innate ability to automate, which drives several industries to adopt IoT. In addition, minimum memory cost, processing, and storage with an increase in Big Data (BD), cloud, and conjunction of industrial networks and the internet are the added factors for the increase in IoT development. Due to this significance, IoT has applications in numerous areas like medical management, farming, wearable technology, smart energy meters, smart cities, etc. The applications are not limited to the examples mentioned above. Considering this, existing studies have considered different applications and attempted to execute them. As different applications have been focused on by these studies, the present review intends to provide a compilation of potential applications of IoT as considered by conventional research between 2018 and 2022. The study also intends to explore the advantages and disadvantages of different IoT applications (deliberated by conventional studies) through tabular analysis. Further, this review emphasizes IoT’s major key challenges and countermeasures to resolve its security issues. Finally, the study affords recommendations that will assist all IoT experts in bringing IoT products with enhanced security into the market. [ABSTRACT FROM AUTHOR]

A smart grid is an electricity network that uses advanced technologies to facilitate the exchange of information and electricity between utility companies and customers. Although most of the technologies involved in such grids have reached maturity, smart meters—as connected devices—introduce new security challenges. To overcome this significant obstacle to grid modernization, safeguarding privacy has emerged as a paramount concern. In this paper, we begin by evaluating the security levels of recently proposed authentication methods for smart meters. Subsequently, we introduce an enhanced protocol named PPSG, designed for smart grids, which incorporates physical unclonable functions (PUF) and an elliptic curve cryptography (ECC) module to address the vulnerabilities identified in previous approaches. Our security analysis, utilizing a real-or-random (RoR) model, demonstrates that PPSG effectively mitigates the weaknesses found in prior methods. To assess the practicality of PPSG, we conduct simulations using an Arduino UNO board, measuring computation, communication, and energy costs. Our results, including a processing time of 153 ms, a communication cost of 1376 bits, and an energy consumption of 13.468 mJ, align with the requirements of resource-constrained devices within smart grids. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management system (EMS) in smart grid based on Internet of Things (IoT) configuration using hybrid approach. The proposed approach is joint execution of Multi-fidelity meta-optimization and Turbulent Flow of water based optimization (TFWO), hence it is known as M2FWO technique. The main objective of this proposed EMS is to better manage power with the resources of Smart Grid by constantly monitoring data from the IoT-based communication framework. Here, each home device is connected to data acquisition module is utilized to facilitate the demand response (DR) growth for energy management system in smart grid. The framework collects DR from every smart home device and then transmits the data to the centralized server. The transmitting data is enabled by M2FWO method. The smart grid IoT framework enhances the feasibility of these networks makes better use of obtainable resources. The proposed system is in charge to satisfy the total supply with energy requirement. Finally, the proposed model is stimulated on MATLAB/Simulink site, then the efficiency is examined with existing methods. [ABSTRACT FROM AUTHOR]

This article summarizes and organizes recent research findings in information and communication technology security developments integrated with smart grids. A vital component of a smart grid is a smart meter. It is relevant because it can collect, process, and transport customer's data over the Internet. Whereas developments in smart grid and smart meter technologies have given new productivity gains, they have also presented new security concerns. Security is essential in defending both the smart grid and smart meter from cyber-attack. Guaranteeing safety is one of the most challenging aspects of designing and deploying a smart metering infrastructure. This study presents a thorough investigation of the integrity of smart metering technologies from multiple different viewpoints. It focuses on threats, countermeasures, and estimations. This article makes four contributions: First, all potential vulnerabilities in smart metering components are described and examined. Second, it assesses the impact of attacks that use these weaknesses to boost the performance of each part and the whole smart meter structure. Thirdly, potential countermeasures to defend smart meters are discussed. lastly, it discusses the unresolved issues surrounding smart meter security and future research areas. This evaluation is distinctive due to its exhaustive treatment of security weaknesses and attacks on smart meter components. In conclusion, the future vision is described. [ABSTRACT FROM AUTHOR]

— Reading traditional meters is always time-consuming and expensive. Using smart meters solves most of the problems existing in the traditional meter network. Smart meters are an advanced form of traditional electro-mechanical devices that can measure energy consumption in real-time and communicate through one or more wired or wireless networks. These devices can communicate from long distances and get changed, making them an easy target for attacks. This paper studies the security mechanisms in smart meters networks and suggests some security solutions in such networks. We have developed software for managing the information of smart meters and controlling them remotely. In this paper, we present the implemented security mechanisms in the developed smart meter management software. The proposed solutions for enhancing the security of this software include implementing the authentication system, enabling user management, and defining different access levels to prevent users from connecting without proper authentication and access control in the developed software. Moreover, hashing the password with a random salt technique is implemented for securing the database. Furthermore, we have secured the software platform to prevent web attacks such as Clickjacking and CSRF attacks.

As we reach the limits of our current technologies and the number of connected devices grows, scientists put more efforts to estimate and reduce the ecological damage of the Internet of Things. Unfortunately, recent literature related to Life Cycle Assessment and eco design of IoT systems suffers from a major inconvenience so far: it does not put sensor data in the focus of attention. This paper aims to point out explicitly the essential role of this aspect for modeling reference flows, and demonstrate its relevance for appropriate environmental assessment and practical eco design. Also, it aims to illustrate that such modeling process must happen in a comprehensive way. For this, our work relies on a case study addressing smart metering, and we proceed as follows: based on available documentation and inspired by certain aspects of different technologies, we deduce the maximal capacities of key electronic components and construct an unfavorable data flow scenario, to get a rough idea of the reference flow and the long-term impact of our system during its use phase. Results from this procedure are later contrasted with results obtained from a packet traffic analysis, in which local and internet data flow are examined carefully. At the end, we verify the importance of sensor data theoretically and empirically, and we conclude that the reference flow and the impact contributors of a system could be affected not only by the local data transit but also by the complex interactions between edge devices and cloud resources. All our findings are discussed to nourish the state-of-the-art around the environmental impact of using full IoT systems and their sustainable design. [ABSTRACT FROM AUTHOR]

Energy theft is a significant problem that needs to be addressed for effective energy management in smart cities. Smart meters are highly utilized in smart cities that help in monitoring the energy utilization level and provide information to the users. However, it is not able to detect energy theft or over-usage. Therefore, we have proposed a multi-objective diagnosing structure named an Energy Theft Prevention System (ETPS) to detect energy theft. The proposed system utilizes a combination of machine learning techniques Gated Recurrent Unit (GRU), Grey Wolf Optimization (GWO), Deep Recurrent Convolutional Neural Network (DDRCNN), and Long Short-Term Memory (LSTM). The statistical validation has been performed using the simple moving average (SMA) method. The results obtained from the simulation have been compared with the existing technique in terms of delivery ratio, throughput, delay, overhead, energy conversation, and network lifetime. The result shows that the proposed system is more effective than existing systems. [ABSTRACT FROM AUTHOR]

Smart metering IoT applications are among the most energy-critical in the current panorama. Metering sensors are battery-powered and are expected to have a lifetime exceeding ten years. In order to achieve such long operation duration, a generic header compression mechanism named Static Context Header Compression (SCHC) has been introduced and accepted as a standard by the Internet Engineering Task Force (IETF). This paper aims to demonstrate the energy savings enabled by the use of SCHC on a cellular IoT network by the means of real-life implementation and measurements. Experiments are conducted in a controlled environment for different scenarios and considering multiple parameters such as message size and radio conditions. Measurements show the high impact of this header compression mechanism, particularly when the radio conditions are bad and repetitions are used to improve the reliability of the transmission: a reduction of up to 40% in energy consumption is observed. Using SCHC over the non-IP transport mode (NIDD) of NB-IoT compared to the legacy IP mode also enables significant energy savings and allows the latency to be reduced while maintaining the interoperability provided by the IP layer. [ABSTRACT FROM AUTHOR]

Demand response (DR) has been studied widely in the smart grid literature, however, there is still a significant gap in approaches that address security, privacy, and robustness of settlement processes simultaneously. The need for security and robustness emerges as a vital property, as Internet of Things (IoT) devices become part of the smart grid; in the form of smart meters, home energy management systems (HEMSs), intelligent transformers, and so on. In this paper, we use energy blockchain to secure energy transactions among customers and the utility. In addition, we formulate a mixed-strategy stochastic game model to address uncertainties in DR contributions of agents and achieve optimal demand response decisions. This model utilizes the processing hardware of customers for block mining, stores customer DR agreements as distributed ledgers, and offers a smart contract and consensus algorithm for energy transaction validation. We use a real dataset of residential demand profiles and photovoltaic (PV) generation to validate the performance of the proposed scheme. The results show the impact of electric vehicle (EV) discharging and customer demand reduction on increasing the probability of successful block mining and improving customer profits. Moreover, the results demonstrate the security and robustness of our consensus algorithm for detecting malicious activities. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has recently become one of the fast‐growing technologies in the world. Integrating existing systems with smart grids still represents a significant challenge arising from the massive cost of upgrading the infrastructure. This paper provides an IoT infrastructure solution based on a newly designed low‐cost microcontroller‐based IoT remote terminal unit (RTU) to integrate new, old, and conventional sites of existing grids with smart grids. The IoT RTU supports Wi‐Fi communication and includes digital/analog inputs and outputs besides the advantage of serial data transmission standard RS‐485 as an industrial field communication (IFC). The Modbus communication protocol is utilized as a serial industrial protocol that can be interfaced over the RS‐485 standard. The advantage of having an IFC protocol integrated into the IoT infrastructure enables to link any supported device and publish the required data to the cloud, besides the ability to add smart metering technologies, which allows Advanced Metering Infrastructure (AMI) in these areas. IoT‐enabled Supervisory Control and Data Acquisition (SCADA) system is integrated with the proposed IoT infrastructure for its advantages of remote monitoring and control. The proposed IoT infrastructure is characterized by proper data security and low power consumption. The IoT infrastructure is simulated with an existing real application for proof of concept, and the results are discussed for different case studies. [ABSTRACT FROM AUTHOR]

Smart meters are among the fundamental components of smart grids due to their features such as high-accuracy energy measurement, remote reading, and control. They are also utilized to reduce the rate of illegal electrical energy use in some distribution areas. This paper presents the development of a Wi-Fi smart meter and designing a smart plug-in device for customer-side energy management. In this way, a method that does not require additional infrastructure and extra investment for remotely reading the energy consumption in environments with common Wi-Fi networks, such as large shopping malls, business centers, community buildings, villa cities, and university campuses has been proposed. The Wi-Fi smart meter and smart energy metering module (SEMM) support various user-friendly applications in accordance with the infrastructure of the smart grid and smart building applications. The SEMM is capable of measuring, monitoring, and remotely managing each electrical device. Thanks to the developed mobile application, customers can monitor their Wi-Fi smart meter data and also the consumption of each electrical device through the SEMMs as well as remotely manage these devices. The application examples presented in this study are also aimed at increasing awareness of energy saving for customers. [ABSTRACT FROM AUTHOR]

The demand response program is an important feature of the smart grid. It attempts to reduce peak demand, improve the smart grid efficiency, and ensure system reliability. Implementing demand-response programs in residential and commercial buildings requires the use of smart meters and smart plugs. In this paper, we propose an architecture for a home-energy-management system based on the fog-computing paradigm, an Internet-of-Things-enabled smart plug, and a smart meter. The smart plug measures in real-time the root mean square (RMS) value of the current, frequency, power factor, active power, and reactive power. These readings are subsequently transmitted to the smart meter through the Zigbee network. Tiny machine learning algorithms are used at the smart meter to identify appliances automatically. The smart meter and smart plug were prototyped by using Raspberry Pi and Arduino, respectively. The smart plug's accuracy was quantified by comparing it to laboratory measurements. To assess the speed and precision of the small machine learning algorithm, a publicly accessible dataset was utilized. The obtained results indicate that the accuracy of both the smart meter and the smart plug exceeds 97% and 99%, respectively. The execution of the trained decision tree and support vector machine algorithms was verified on the Raspberry Pi 3 Model B Rev 1.2, operating at a clock speed of 600 MHz. The measured latency for the decision tree classifier's inference was 1.59 microseconds. In a practical situation, the time-of-use-based demand-response program can reduce the power cost by about 30%. [ABSTRACT FROM AUTHOR]

Reading traditional meters is always time-consuming and expensive. Using smart meters solves most of the problems existing in the traditional meter network. Smart meters are an advanced form of traditional electromechanical devices that can measure energy consumption in real-time and communicate through one or more wired or wireless networks. These devices can communicate from long distances and get changed, making them an easy target for attacks. This paper studies the security mechanisms in smart meters networks and suggests some security solutions in such networks. We have developed software for managing the information of smart meters and controlling them remotely. In this paper, we present the implemented security mechanisms in the developed smart meter management software. The proposed solutions for enhancing the security of this software include implementing the authentication system, enabling user management, and defining different access levels to prevent users from connecting without proper authentication and access control in the developed software. Moreover, hashing the password with a random salt technique is implemented for securing the database. Furthermore, we have secured the software platform to prevent web attacks such as Clickjacking and CSRF attacks. [ABSTRACT FROM AUTHOR]

More energy-consuming devices such as household electronics and more production facilities worldwide are causing increases in electricity demand and energy prices. The public service has difficulty maintaining the energy balance. These problems can be overcome by rapidly transforming the traditional electricity grid into a smart grid (SG) infrastructure. Smart meters (SMs) are an essential component of SGs and have vital tasks. This study has developed an Internet of Things (IoT) based SM that can reach high data rate of 38,400 bps or frequency of 160 MHz, using SQL Server for data storage and bidirectional data transmission with 174 W total load. In this way, consumers can track their energy consumption hourly, daily, and monthly, learn how much they spent on consumption, and receive warnings for a power outage. A fuzzy system and mobile application software are integrated into the SM structure for all these purposes. The designed device is believed to contribute significantly to the spread of SGs due to all these features. [ABSTRACT FROM AUTHOR]

This paper presents a hardware-assisted security primitive that integrates the Trusted Platform Module (TPM) into IoT devices for authentication in smart grids. Data and device security plays a pivotal role in smart grids since they are vulnerable to various attacks that could risk grid failure. The proposed Fortified-Grid security primitive provides an innovative solution, leveraging the TPM for attestation coupled with standard X.509 certificates. This methodology serves a dual purpose, ensuring the authenticity of IoT devices and upholding software integrity, an indispensable foundation for any resilient smart grid security system. TPM is a hardware security module that can generate keys and store them with encryption so they cannot be compromised. Formal security verification has been performed using the random or real Oracle (ROR) model and widely accepted AVISPA simulation tool, while informal security verification uses the DY and CK adversary model. Fortified-Grid helps to validate the attested state of IoT devices with a minimal network overhead of 1984 bits. [ABSTRACT FROM AUTHOR]

Distributed smart home systems using wireless communication are increasingly installed and operated in households. Their popularity is due to the ease of installation and configuration. This paper presents a comprehensive technical, quality, and energy analysis of several popular smart home modules. Specifically, it focuses on verifying their power consumption levels, both in standby and active mode, to assess their impact on the energy efficiency of building installations. This is an important aspect in the context of their continuous operation, as well as in relation to the relatively lower power of loads popular in buildings, such as LED lighting. The author presents the results of measurements carried out for seven different smart home modules controlling seven different types of loads. The analysis of the results shows a significant share of home automation modules in the energy balance; in particular, the appearance of reactive power consumption due to the installation of smart home modules is noteworthy. Bearing in mind all the threads of the analysis and discussion of the results of measurement experiments, a short SWOT analysis is presented, with an indication of important issues in the context of further development of smart systems and the Internet of Things with wireless communication interfaces, dedicated to home and building applications. [ABSTRACT FROM AUTHOR]

The burgeoning Internet of Energy (IoE) paradigm, a fusion of the Internet of Things (IoT) and Smart Grid (SG) technologies, holds the promise of significantly enhancing the reliability and efficiency of energy production, transmission, and consumption across the entire energy chain, from generation to the end user. Two central technical aspects that enable this innovation are the advent of smart consumer electronics and the establishment of bidirectional IoT communications. These developments have facilitated the incorporation of novel applications into the Smart Grid, including smart metering, Consumer Demand-Response (CDR) management, and prepayment. In this study, our focus lies primarily on the development of a secure and efficient key management system for CDR communications. It is demonstrated herein that a previous key graph-based scheme, called EDR, is susceptible to collusion attacks and lacks support for broadcast CDR communications. In response to these vulnerabilities, we propose a novel key management scheme, referred to as Secure and Efficient key management scheme for CDR communications (SE-CDR). This scheme retains the strengths of the EDR while introducing a modified multi-group key graph technique, designed to ensure the secure, efficient, and scalable management of unicast, multicast, and broadcast CDR communications. The presented security analysis and performance evaluation results establish the robust security of the SE-CDR scheme. Moreover, a comparative analysis revealed that this new approach offers significant improvements in terms of storage and communication efficiency, outperforming existing state-of-the-art methods. This study thus presents a promising advancement in the realm of secure and efficient key management for the Internet of Energy paradigm. [ABSTRACT FROM AUTHOR]

In a smart grid, the main goals are to provide grid stability, improve power system performance and security, and reduce operations, system maintenance, and planning costs. The prediction stability of smart grid (SG) systems is essential in terms of power loss minimization and the importance of adequate energy policies. SG systems must accurately predict the energy demand and ensure the right amount of energy is available at the right time. If the prediction is inaccurate, it can lead to costly energy production or usage errors and create considerable inefficiencies in the power grid. Due to this, this manuscript offers five different cascade methods to detect the stability of SG systems. Detecting the stability of SG systems enables the grid to respond quickly to changes in demand and supply, improves system reliability, reduces power outages, and increases the overall efficiency of the grid. The present work proposed five different cascade methods with pre-processing, training and testing division, and the classification stages of the classification procedure for estimating SG stability. In the first pre-processing stage, the SG dataset is pre-proceeded with the feature selection (Relief, Fast Correlation-Based Filter (FCBF), and supervised attribute filter). The resampling (the bootstrapping), the Fuzzy C-Means Clustering-Based Feature Weighting (FCMFW), the resampling then feature selection (supervised attribute filter), and the feature selection (supervised attribute filter), then FCMFW. In the second stage, the training and testing division stage, the SG dataset was separated into three test and training data methods before the classification algorithm: The 5 Fold Cross Validation (FVC), 10 FVC, and hold-out (50–50%). In the third stage, the classification stage, five different classification algorithms, including Naive Kernel Bayes, Linear Support Vector Machine (SVM), Weighted K-Nearest Neighbors, Begged Trees, and Narrow Neural Network classifying algorithms, are used to classify the SG dataset. The simulation results of this study demonstrated that the suggested cascade ML system had achieved significant accuracy in predicting SG stability. The best cascade method is the feature selection (supervised attribute filter) + FCMFW + 10 FCV and then performing the bagged trees algorithm; thus, the new approach affords an accuracy of 99.9%. Furthermore, due to the rapid growth of ML techniques, sensors, and smart meters technologies, with Machine to Machine communication via the internet of things (IoT), the real-time identification process is made practical with higher accuracy. For this reason, our future research will focus on an IoT-based SG system, an E-stability determination system. Thanks to the proposed cascade method, the SG dataset can be classified easily, quickly, and reliably. E-stability determination systems can help to fast detect, predict, and respond, which is an important application of IoT on the grid systems. [ABSTRACT FROM AUTHOR]

Smart meters are rapidly installing by utility providers to improve the reliability and performance of Smart Grid. Utility providers analyze real-time smart meter data to monitor, predict, generate and distribute power. The customer's real-time activity and power usage can be revealed by analyzing the smart meter data. Therefore, the security and privacy of the data is a crucial issue for the smart grid. This paper proposes a secure and privacy-preserving data aggregation and classification (SP-DAC) model based on fog and cloud architecture. Data is aggregated at the fog node in the SP-DAC model, and classification is performed at the outsourced cloud with three machine learning classifiers. Simulation results analyze the cryptographic costs and classification performance. Real-world smart meter dataset "UMass Smart" is taken for experiments and classification accuracy, precision, recall, and F1 score achieved upto 88%, 87%, 90%, and 88%, respectively. The comparison with existing models shows the superiority of the SP-DAC model in terms of features and parameters. [ABSTRACT FROM AUTHOR]

The rising interest in the security of network infrastructure, including edge devices, the Internet of Things, and smart grids, has led to the development of numerous machine learning-based approaches that promise improvement to existing threat detection solutions. Among the popular methods to ensuring cybersecurity is the use of data science techniques and big data to analyse online threats and current trends. One important factor is that these techniques can identify trends, attacks, and events that are invisible or not easily detectable even to a network administrator. The goal of this paper is to suggest the optimal method for feature selection and to find the most suitable method to compare results between different studies in the context of imbalance datasets and threat detection in ICT. Furthermore, as part of this paper, the authors present the state of the data science discipline in the context of the ICT industry, in particular, its applications and the most frequently employed methods of data analysis. Based on these observations, the most common errors and shortcomings in adopting best practices in data analysis have been identified. The improper usage of imbalanced datasets is one of the most frequently occurring issues. This characteristic of data is an indispensable aspect in the case of the detection of infrequent events. The authors suggest several solutions that should be taken into account while conducting further studies related to the analysis of threats and trends in smart grids. [ABSTRACT FROM AUTHOR]

Today we are living in an era where everything is changing to be smart, whether it be a smart home, smart industries, smart irrigation, or a smart meter, where the word smart refers to the involvement of the Internet of Things (IoT). The increased use of IoT infrastructure in these fields has led to the failure of the nodes, increase in threats, attacks, abnormalities, and spying, which is the primary concern and an important domain of an IoT. The main objective of this paper is to use a supervised learning model to predict anomalies in the historical data which can later be incorporated into real-world scenarios to block the upcoming anomalies and attacks. This paper predicts the anomalies on the 350 K data set using the Machine Learning models and compares its performance based on the state of arts. In this paper, two different approaches are used based on the analysis done on the dataset. The classification algorithms were applied to the whole dataset in the first, and then the same classification algorithms were applied after excluding the data points having binary values (0 and 1) in the feature "value" and have achieved an average of 99.4% accuracy for the first case and 99.99% accuracy for the later. [ABSTRACT FROM AUTHOR]

Transparency in electricity measurement and efficient use of electricity are issues of concern to households. Electricity customers want to have confidence in their utility company's electricity meters installed at their houses. This study designs and constructs an internet of things (IoT)-based smart electric meter to measure electricity and remotely control domestic appliances. This device can monitor, calculate electricity bills and remotely control the appliances via the internet; electrical quantities are displayed in real-time on the Blynk app, liquid crystal display (LCD) screen and Google Sheet. The device is also capable of emailing the electricity user. The proposed system is designed and developed based on ESP32, a LCD, a PZEM 004T, four relays and a DC source. The results show that the device works stably and the device's error is less than 5%. This research makes it possible for users to conveniently monitor their power consumption while enhancing their energy-saving awareness. [ABSTRACT FROM AUTHOR]

Electric smart grids enable a bidirectional flow of electricity and information among power system assets. For proper monitoring and controlling of power quality, reliability, scalability and flexibility, there is a need for an environmentally friendly system that is transparent, sustainable, costsaving, energy-efficient, agile and secure. This paper provides an overview of the emerging technologies behind smart grids and the internet of things. The dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted and reliable power supply. In addition, the paper sorts out challenges in the traditional or legacy electricity grid, power generation, transmission, distribution, and revenue management challenges such as reducing aggregate technical and commercial loss by reforming the existing manual or semi-automatic techniques to fully smart or automatic systems. This article represents a concise review of research works in creating components of the smart grid like smart metering infrastructure for postpaid as well as in prepaid mode, internal structure comparison of advanced metering methods in present scenarios, and communication systems. [ABSTRACT FROM AUTHOR]

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The operation of modern housing infrastructure is characterized by a constant increase in the cost of the limited resources used. This necessitates the priority implementation in the concept of a smart home of elements aimed at resource saving and their rational management. The study provides an overview of the implementation architectures of the internet of things (IoT) concept in the construction of home automation systems and the requirements they impose on the implementation of smart primary meters of controlled physical quantities. Based on a diversion analysis, a promising smart water meter was developed. The prototype is ergonomic and has a structural form factor convenient for further integration. The designed model of the electronic module of the water flow monitoring system implements, in addition to typical tasks, additional functionality: transfer of recorded indicators and technical information to the cloud storage, warning the user about an emergency situation, accumulation of current data in non-volatile memory. It is possible to use the accumulated statistics for training the predictive analysis module. The proposed architecture option will allow creating energy-efficient elements of home automation systems in the future. [ABSTRACT FROM AUTHOR]

In the energy sector, since the adoption of remote device management for massive advanced metering infrastructure (AMI) devices and Internet of Things (IoT) technology using a representational state transfer (RESTful) architecture, a blurred boundary has been developed between traditional AMI and IoT. With respect to smart meters, the standard-based smart metering protocol, called the device language message specification (DLMS) protocol, still has a predominant role in the AMI industry. Thus, we aim to propose a novel data interworking model in this article that embraces the DLMS protocol in AMI using the most promising IoT protocol, the so-called lightweight machine-to-machine (LwM2M) protocol. We provide a 1:1 conversion model using the correlation of the two protocols with an analysis of the object modeling and resource management methods of both the LwM2M and DLMS protocols. The proposed model utilizes a complete RESTful architecture, which is the most beneficial in the LwM2M protocol. It improves the average packet transmission efficiency and packet delay on the plaintext and encrypted text (session establishment and authenticated encryption) by 52.9%p and 9.9%p, respectively, and by 11.86 ms for both cases, compared to the encapsulation method of the LwM2M protocol, KEPCO's current approach. This work provides the key idea to unify the protocol for the remote metering and device management of field devices into the LwM2M protocol, and it is expected that this work will improve the efficiency in the operation and management of KEPCO's AMI system. [ABSTRACT FROM AUTHOR]

Smart Metering (SM) systems allow frequent and accurate consumption readings that can be the source of multiple applications, generating new business in the future. However, it imposes interesting challenges for Distribution System Operators (DSO), retailers, aggregators and data scientists. The diversity of data formats, structure, granularity, and volume require additional investigation. Thus, this paper emphasizes on an Edge-Fog-Cloud computing architecture and proposes a SM and Internet of Things (IoT) data processing framework to develop applications and extract the meaningful insights from data. This research divides the applications between Edge, Fog and Cloud layers and designs their interactions. Furthermore, this research offers a methodology that distributes the processing between the layers to reduce the communication latency, response time, resource load and utilization. The methodology provides patterns for implementing the algorithms that monitor and control IoT appliances at the Edge layer and perform Load Forecast (LF) and Direct Load Control (DLC) at the Fog layer. [ABSTRACT FROM AUTHOR]

Smart energy has evolved over the years to include multiple domains integrated across multiple technology themes, such as electricity, smart grid, and logistics, linked through communication technology and processed in the cloud in a holistic way to deliver on global challenges. Advances in sensing, communication, and computation technologies have been made that enable better smart system implementations. In smart energy systems, sensing technologies have spanned multiple domains with newer techniques that are more accurate, have greater dynamic ranges, and are more reliable. Similarly, communication techniques have now evolved into very high-speed, flexible, and dynamic systems. Computation techniques have seen a quantum leap with greater integration, powerful computing engines, and versatile software stacks that are easily available and modifiable. Finally, the system integration has also seen advances in the form of management, automation, and analytics paradigms. Consequently, smart energy systems have witnessed a revolutionary transformation. The complexity has correspondingly grown exponentially. With regard to smart meters, the measurement component has to scale up to meet the demands of the evolved energy eco-system by relying on the advancements offered. The internet of things (IoT) is a key technology enabler in this scenario, and the smart meter is a key component. In recent years, metering technology has evolved in both complexity and functionality. Therefore, it must use the advances offered by IoT to deliver a new role. The internet of things (IoT) is a key technology enabler in this scenario and the smart meter a key component. In recent years, metering technology has evolved in both complexity and functionality. To deliver on its new role, it must use the advances offered by IoT. In this review, we analyze the smart meter as a combination of sensing, computing, and communication nodes for flexible and complex design paradigms. The components are, in turn, reviewed vis-à-vis the advances offered by IoT. The resultant gaps are reported for future design challenges in the conclusion. The identified gaps are the lack of usage of the full spectrum of the available technology and the lack of an inter-disciplinary approach to smart meter design. [ABSTRACT FROM AUTHOR]