The Internet of Things (IoT) is a significantly growing technology. IoT connects diverse smart devices that perform distributed sensing tasks to communicate with other devices or central servers and repositories and exchange data over the networks, enabling automation and efficient real-time monitoring [1]. It has received importance in research areas. Fig. 1 shows different applications of IoT in the real world.

A Smart Grid (SG) is an electrical grid with communications and information technology. It has overcome various challenges faced by traditional electric grid technology [2]. With ICT (information and communications technology) consumer's electricity consumption behaviour is automatically collected. This is in terms of increased reliability, sustainability of energy, and efficiency due to smart electricity transmission and efficient smart electricity networks that utilize cutting-edge technology. SGs are an intricate setup consisting of communication networks, sensors, and data analytics to enhance distribution, generation, and electricity consumption [3]. SGs can convert consumers into energy manufacturers through hydropower, solar energy, wind turbines, and additional sources of energy.

Smart meters IoT have gained reliability among people for smarter energy management at their homes, and companies for energy efficiency [4]. SG technology functions using a bidirectional communication model, allowing smooth interaction among different components. Real-time data collection is achieved through the coordination of IoT devices and sensors throughout grid infrastructure. Analysis of this information is then done to streamline energy flow, detection of failures, and response to evolving requirements in a dynamic way. Many electricity providers have already upgraded or incorporated SG technology into their current grid [2]. It works by tracking energy usage and enhancing efficient operations to control power systems by electric utilities. When energy consumption peaks, signals are transmitted to customers to lower their energy usage, helping to balance the energy load. The control center monitors the complete power supply system and protective devices to ensure the security of the load-balancing system during communication. Cloud Computing (CC), which provides resources as services, is used to communicate between power supply companies, power plants and substations [5]. The reliability, robustness, and security of this communication are increased through built-in redundancy.

In a standard Smart Grid-IoT network setup, multiple smart devices are connected to a smart meter. The meter tracks the energy usage of each device. It transmits this information to a central aggregator that is mostly an honest entity and ensures the user's privacy the smart meter readings are sent in encrypted form. The aggregator gathers the encrypted readings before delivering them which ensures privacy as well as saves time [6].

Recently, there have been developments in cloud-based SG systems, in which smart meters and fog devices from different domains send consumption data to the cloud through a central cloud hub. SG requires real-time data processing, and, in such scenarios, Fog computing is advantageous because it considerably enhances resilience, reliability, and efficiency. SGs analyze large amounts of real-time data by placing computing resources near sensors and meters. This facilitates quick decision-making and caters to changing needs with quicker response. For instance, analysis of power usage patterns, detection of anomalies, and grid operations optimization is done by fog nodes at substations. This strengthens grid security and reduces the load on network bandwidth [7]. Data consolidation is another technique that integrates summarization and combination of multiple data packets into a single larger packet for transmission that, in terms decreases the number of packets sent over the network, enhancing the network's overall efficiency and saving bandwidth. When data is consolidated, it reduces redundant information and minimizes the overhead linked to packet transmission, improving data transfer and boosting network performance [8].

Additionally, potential interference or tampering of data packets is reduced, improving data security through data consolidation. SG has recently implemented fog computing for efficient data consolidation. In the SG-IoT environment, there are several well-known groups. First, information from smart meters is uploaded to fog devices in an acyclic or cyclic manner before transmitting to cloud servers, and this data is collected through static nodes and sinks that monitor power consumption in smart homes. Second, there are mobile sensor nodes linked to static sinks, which is similar to delivery drones equipped with sensors flying around a city. Due mobility of sensor nodes and sinks that are deployed globally for monitoring poses a significant challenge in determining optimal pathways where data consumption consolidation arises at fixed-location fog devices [6]. This information has the potential to be utilized for invoicing, making predictions based on data, and estimating power requirements. However, there are also privacy concerns. Sending this data to the cloud may cause considerable delays in response time, especially when dealing with a high volume of smart meters [9]. This could potentially overload the cloud's ability to process requests efficiently. Transmitting large amounts of data from each smart meter can result in transmission network delays and overhead. To address these challenges, a model based on encryption has been developed for SGs. This model utilizes fog computing to partly offload the storage and processing capabilities of the cloud to terminal devices. Data from smart meters can be sent in acyclic format in compressed form to reduce transmission obstacles [10]. Smart meters condense data for transmission, whereas fog devices use cloud-fog techniques to centralize data. For effective data consolidation, built-in data centralization is crucial which uses fog devices for efficient communication, processing, and storage in comparison to traditional methods depending on a "consolidator" for data centralization and storage.

Various research studies have examined how SG applications can leverage CC to improve their reliability and efficiency. For instance, Kumar et al. [11] investigated how CC can optimize a SG's Demand Response (DR). This was achieved by using a scalable and flexible model of cloud virtual machines to evaluate resource availability for on-demand usage and to relinquish resources when idle. Moreover, cloud virtual machines were used to introduce redundancy for critical SG applications to duplicate computations and replicate data by the addition of extra virtual machines. In the study conducted by Rusitschka et al. [12], they introduced an alternative CC model designed for the real-time extraction of data and simultaneous processing for SG applications. In a different study carried out by Bhowmick et al. [13] for a SG condition monitoring application, it was found that CC offers effective and protected storage management. Moreover, CC presents numerous other benefits to a SG concerning cost-effectiveness and expandability. Kim et al. [14] proposed a DR framework based on customer communication, which aims to deliver rapid responses to customers by facilitating direct interaction between consumers and utilities.

Several studies have been conducted on grid-aware CC routing algorithms to address service request routing problems. Mohsenian-Rad and Leon-Garcia [15], Fayyaz and Nazir [16], and Mishra et al. [17] have focused on combining SG applications with CC to address security and reliability issues. Additionally, there are products in the market that implement SG applications using CC, such as Hohm, Microsoft's energy management tool, which is hosted on a cloud platform and designed for use in special residential buildings [18]. Hohm provides proprietary power-saving suggestions [18]. It allowed for the scalable monitoring of consumers' energy usage. Several other applications are also available for testing and observing the performance of CC in SG applications [19]–[21], with some already in use and others still under research. When integrating Cloud-IoT with SGs, a variety of techniques are used to improve security and encryption. Fog computing improves SG systems by providing a decentralized approach for securely consolidating data. It facilitates efficient communication and privacy protection through encryption, addressing issues posed by large IoT systems.

On the other hand, edge computing deals with the management of extensive data in SGs by enabling real-time data processing and decision-making closer to data sources. This enhances predictive capabilities and economic analysis through advanced algorithms such as LSTM and GAN. The use of Blockchain technology [22] could completely transform DR in SGs by improving efficiency, security, and consumer involvement while addressing issues related to scalability, interoperability, and regulatory complexities. Homomorphic Encryption [23], which utilizes the Paillier cryptosystem, can enhance the security of SGs by enabling computations on encrypted data, thus ensuring data privacy and non-repudiation and reducing cyber threats in interconnected power systems. Finally, Multi-Factor Authentication (MFA) [24] can improve data security and user privacy in SGs by requiring multiple verification methods, such as biometrics, to ensure that only authorized users can access sensitive information. By using the Puzzle Optimization Algorithm and Elliptic Curve Cryptography, this method helps reduce the chances of data leakage and unauthorized access, ultimately enhancing the security of power grid communication. These strategies collectively contribute to fortifying the security measures of SG applications.

for SGs. Finally, this paper is concluded in Section 6.

CC provides resources as a service over a network. It includes IaaS, PaaS and DaaS. It reduces cost and complexity and allows computations and resource sharing over the internet [29]. CC's Enterprise Data Center shifts resources to meet application needs, including security, network devices, and storage systems. Services are distributed across numerous computers, enabling easy access for systems like SGs. CC enhances SG scalability, efficiency, and security and provides real-time responses, self-healing, and reliable communication

In this section, we outline the established mechanism, which is divided into four subsections [6].

I. Initialization.

II. Authorization.

III. Generation of reports (smart meters).

IV. Consolidated data.

The primary objective is to secure data from external hackers and ensure that it is preserved securely in a unified manner.

In this research, the integration of IoT-based CC in a SG environment and an architecture for secure data consolidation deploying a data compression mechanism is proposed. The primary objectives of our proposed mechanism are:

Secure transmission of data is ensured by the system from IoT devices to fog devices, the aggregated data is sent to the cloud through a central cloud server. The fog device utilizes an algorithm to prepare consolidated data after the collection of data from multiple smart meters. Compression techniques are also employed by fog devices to reduce data size and energy usage during communication. The data extraction algorithm by the cloud server first extracts data from each fog device, followed by further splitting to gather data from each fog device. However, a significant challenge still prevalent is to proactively manage potential cyber threats facing the IoT-enabled SG system. Future research could focus on addressing emerging cyber threats while ensuring optimal performance and enhancing data security, advanced encryption techniques such as homomorphic encryption and quantum cryptography, real-time anomaly detection using machine learning, integration of blockchain technology, the design of systems capable of adapting to dynamic security needs could be considered by the development of robust communication protocols.

The author extends sincere gratitude to KIIT (Deemed to Be) University for providing the necessary resources and a supportive research environment. Special thanks to colleagues and mentors for their valuable insights and constructive feedback, which significantly contributed to the improvement of this work.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

The data supporting the findings of this study are available upon reasonable request from the corresponding author.

The author declares no conflict of interest related to this research.