## 2 Background
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Methodology** & **Common Problems** & **Solutions Identified** & **Summary of Findings** \\ \hline [PERSON] et al. (2019) [21] & Review of literature & Security and interoperability issues & Leveraging real-time data for demand response & Identified challenges: security and interoperability issues in IoT implementation in smart grids. \\ \hline [PERSON] and [PERSON] (2020) [22] & Case study & Energy consumption reduction & Access to real-time energy data & Consumers with access to real-time energy data reduced energy consumption by 15\% in urban areas. \\ \hline [PERSON] et al. (2018) [23] & Simulation & Peak demand reduction & Demand response strategies based on real-time data reduced peak demand by 20\%. \\ \hline [PERSON] and [PERSON] (2017) [24] & Survey & Grid reliability improvement & IoT implementation & 80\% of utility companies reported improved grid reliability after IoT implementation, survey found. \\ \hline [PERSON] et al. (2021) [25] & Comparative analysis & Coverage and power efficiency in remote areas & Implementation of LoRaWAN & LoRaWAN demonstrated better coverage and power efficiency in remote areas for IoT-enabled smart grids. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of some of the popular research work on IoT based smart grid. Table 1 summarizes key findings from various studies related to the electric power internet of things (IoT). Each study addresses specific challenges and solutions within the realm of smart grid implementation. Common challenges identified include security and interoperability issues, integration of renewable energy sources, and optimizing grid stability. Solutions often involve leveraging real-time data for demand response, implementing efficient communication protocols, and developing predictive maintenance models using IoT data. Effective communication and control techniques are essential for the successful implementation of PIoT-based DR schemes. Various communication technologies have been employed in smart grid systems, including wireless technologies like ZigBee, LoRaWAN, and cellular networks as mentioned in [33]. These technologies enable the collection and transmission of real-time data from smart meters, sensors, and other IoT devices. This comprehensive survey by [34] provides an overview of PIoT technology, its key components, and applications in smart grid systems. The authors explore the variations in agricultural energy systems with a focus on IoT applications [35]. The authors discuss the role of PIoT in demand-side management, energy efficiency, and system reliability, and identify research gaps and potential future directions in the field of PIoT-based smart grid demand schemes. Advanced control strategies, such as model predictive control (MPC) and optimization algorithms, have been employed to manage the energy consumption of consumers participating in DR programs as mentioned in [36]. Furthermore, machine learning techniques, like artificial neural networks (ANNs) and support vector machines (SVMs), have been used to predict energy consumption patterns and develop optimal DR strategies as shown in Figure 3[37].