Analyzing high-resolution data from smart meters (SMs) enables the identification of internal household activities and even personal user information, posing a significant privacy threat to energy consumers. Thus, preserving SM’s privacy is paramount. While data tampering methods offer a solution, they often lead to issues like inaccurate state estimation and complex billing. To address this, we propose demand-side energy management leveraging rechargeable batteries (RB) and solar panels. This is because larger RBs are costly and environmentally unfriendly. Thus, we prioritize the use of green energy sources, such as solar panels, minimizing RB’s reliance on enhanced consumer privacy. Strategically managing loads with solar panels and grid energy sales achieves cost-effective privacy. Considering realistic off-peak and peak energy consumption periods renders our approach practical. Theoretical analysis and simulations demonstrate the superiority of our method over existing state-of-the-art approaches.

The rapid expansion of the Internet of Things (IoT) marketplace requires intelligent and adaptable infrastructures capable of efficient resource allocation and management among diverse entities, such as electric vehicle charging stations and energy providers. As the IoT marketplace increasingly intersects with critical electricity and transportation infrastructures, the need for coordinated, intelligent decision-making becomes more urgent. Despite its promising potential, the IoT marketplace currently faces critical challenges, including inefficient resource allocation, system congestion, and unpredictable patterns of energy demand, all of which hinder its seamless operation. In response, this paper proposes an innovative AI-driven framework for the IoT marketplaces that integrates context-aware techniques with Q-learning to transform resource allocation and matchmaking processes. Using EV charging as the primary case study, we implement context-aware similarity matching to accurately pair EVs with optimal charging stations, while Q-learning algorithms dynamically enhance matchmaking decisions. Our experimental results clearly demonstrate that this integrated approach effectively reduces charging delays, optimizes energy allocation, and substantially improves overall system efficiency. This research marks a significant advancement toward an intelligent and agile IoT marketplace infrastructure, which addresses critical challenges and supports the sustainable evolution of future electricity and transportation infrastructures.

Improving reliability and response time in decision-making is crucial for efficient demand response (DR) programs in smart grid (SG) environments. By precisely predicting the DR in near real time, consumer premises can be more prepared to optimize energy utilization. We propose and develop an ultrareliable low-latency communication (URLLC)-based machine learning paradigm for orchestrating DR with guaranteed reliability and timeliness. To understand the context in depth and develop new insights, we use a random forest (RF) algorithm to predict the DR program. After that, we employ MuZero reinforcement learning (MuZero RL) on top of RF-based learning with URLLC, which leverages an efficient learned model under such SG DR dynamics. It considerably improves decision-making delays with better generalization to unforeseen situations. In sharp contrast to the state-of-the-art approaches, we observe that MuZero RL enables continuous learning by self-play, achieves higher sample efficiency, and adapts well to the underlying dynamic environments. Such features of an intelligent agent are precious in the context of the considered DR program. We develop theoretical derivations and analyses to study and utilize the framework’s capabilities and demonstrate that URLLC can substantially reduce energy costs.

The Smart Grid (SG) infrastructure plan offers growth opportunities for the electric vehicle (EV) industry and aims to reduce dependence on fossil fuels. Surprisingly, the literature lacks comprehensive research on data privacy issues within the EV-SG ecosystem. In response, this paper presents an efficient federated reinforcement learning (FRL) framework tailored to cost-effectively preserve privacy in wireless vehicle-to-grid (V2G) systems. Our approach involves the use of a small auxiliary battery to generate noise, conceal the true energy demand of electric vehicles, and learn the time-varying dynamics of energy usage for wireless EV charging through a federated process. Within this framework, we employ deep Q-learning to concurrently minimize costs and maximize privacy rewards, while exploring innovative techniques to enhance learning speed and communication efficiency through a global FRL approach.

Efficient data communication and machine learning aspects are crucial for orchestrating the distributed resources of smart grid (SG) networks. 5G telecom technologies have enabled ultrareliable low-latency communication (URLLC) to provide low-latency data communication and accelerate distributed machine learning [such as federated learning (FL)] with high reliability. For critical SG operations, such as islanding detection and instability of frequency regulation, the adoption of URLLC and FL appears paramount to enable near real-time communication and collaborative decision making for resource management. However, SG with URLLC and/or FL has been poorly studied in the literature. We develop a novel framework and demonstrate our findings on the importance of URLLC and FL for efficient energy trading between distributed energy sources and to minimize energy loss by enhancing the resilience of critical SG operations. Extensive experiments using real-time data sets validate our design assumptions and ideas.

Future vehicle-to-grid (V2G) systems require more flexible scheduling to adjust and flatten the peak energy demand. For efficient scheduling and energy trading, the utility provider (UP) needs to keep track of the state of charge (SoC) of vehicle batteries (VBs). However, sharing of SoC of VBs from electric vehicles (EVs) to UP may compromise owner privacy by analyzing the electricity usage in EVs. Therefore, we propose Reinforcement learning (RL)-based demand-side energy management using a rechargeable battery (RB) for enhanced cost-friendly privacy of EVs, efficient scheduling, and accurate billing. With existing Q-Learning-based RL (using$\epsilon $-greedy exploration and exploitation), we find that the reward maximization of efficient and private scheduling is often sluggish and incurs convergence issues. Therefore, we develop a genetic algorithm (GA)-based exploration and exploitation, which solves the convergence problems. We develop theoretical analysis and implement numerical results to demonstrate that the proposed GA-based RL framework accelerates convergence and enhances cost-friendly privacy considerably.

In this article, we consider ultra-reliable low-latency communication (URLLC) for efficient energy trading over a smart grid (SG) network using home-based smart meters (SM). We develop a cost-friendly privacy preservation framework based on existing demand-side energy management by employing random bidirectional energy trading among customers. Customers in our design can be either producers or consumers and mostly both (‘prosumers’). Our aim is to develop a decentralized optimization framework that not only reduces energy costs, but also improves privacy preservation and energy trading ability directly from the customer’s end. One of the vital costs for energy consumers is the supply charge. Our method can minimize it by orchestrating energy trading among customers in a decentralized adaptive fashion. To predict the energy demand by optimizing between privacy and cost, we employ an extension of the follow the regularized leader (FTRL) algorithm. We perform a theoretical analysis to demonstrate the convergence of the FTRL, the benefits of URLLC for the SG network, and the cost-effective privacy preservation ability of the proposed model. In addition to enabling energy trading efficiently, our extensive simulation results demonstrate that our proposed framework outperforms the state-of-the-art methods in terms of the cost-friendly privacy of SMs.

Nowadays, most farming technologies are gradually being transformed into real-time monitoring, control and actuation systems with the advent of the Internet of Things (IoT). These deployments of the IoT over farms have been accelerating due to advancements in sensor technology and communication protocols. Low orbit satellites, for example, has the capability to enable real-time monitoring of the crop over remote places and farms. However, there are numerous challenges to realising seamless farming with LEOs because of low power usage and long-distance transmission requirements from the LoRaIoT sensors over the farms. The main objective of this paper is to study state of the art and present a high-level link budget analysis of the ground sensors and gateways mounted over low earth orbit (LEO) satellites. While the link budget provides ideas on how LEO satellites are prepared for data networking, it also supports improving communication with optimal signal strength. We find that there is a possibility of determining an optimal set of parameters for the ground sensors and the LEO satellite to deliver the desired performance for technical readiness. We observe that different LoRa field parameters such as link budget, receiver power, receiver sensitivity, the path loss can be used at 923.3 MHz frequency. Based on the link budget analysis, we suggest the maximum feasible distance between ground sensors and LEOs. Most importantly, we performed a preliminary analysis of a beamforming approach to improve communication efficiency of the smart farming1.

Cost-friendly differential privacy (CDP) of smart meters can be preserved by an appropriate charging and discharging mechanism that uses rechargeable batteries (RBs) to generate Laplace distributed random noise. However, the existing CDP methods have several issues. First, the maximum discharge rate of an RB requires to vary with the maximal consumption of houses. Second, the probability of an RB to charge/discharge depends on the demand, regardless of the state-of-charge (SoC) of an RB. Third, in extreme SoC (near-empty or almost fully charged) of an RB, no noise added to the demand. To overcome these, we propose a mechanism in which a novel probability density function is designed to generate near Laplace distributed random noise. We also utilize a renewable energy source with small storage in cascade with an RB to enhance performance. Both theoretical analysis and simulations are performed to demonstrate the effectiveness of our proposed method.

Due to the rapid growth of smart grids, use of smart meters (SMs) have increased in the recent days. The main problem with the use of SMs is that by observing the SMs reading, it is possible to infer the daily activities of the consumers. Therefore, protection of privacy is a major concern related to SMs. Using rechargeable batteries (RBs) is a popular method in protecting the privacy in SMs as these methods do not tamper with SM readings. The major problem in RB-based mechanism is that the energy management unit (EMU) cannot protect privacy, if the demand is lower or higher for a longer period. To overcome this problem, in this paper a heuristic method has been proposed by considering time varying target output load based on the three major properties of artificial fish swarm optimization algorithm. For the optimal choice of the time varying target output load, RB constraints as well as reduction of the average cost of energy have been considered in our proposed method. We have proposed two privacy preserving mechanisms for both offline and online scenarios. The proposed method preserves privacy while reducing the cost of energy. Simulation results show that the proposed method is able to provide privacy by overcoming the problem identified in the existing methods.

Usage of smart meters (SMs) have significantly increased in the recent days due to the advantages they offer. However, it is possible for an adversary to extract private information about a consumer by observing the SM readings. Therefore, it is important to protect the privacy of consumers using SMs. Among the SM privacy protection mechanisms, rechargeable battery (RB)-based mechanisms are preferred as they do not alter SM readings. The existing mechanisms cannot protect the privacy when the consumer energy usage is either low or high for a longer period. Furthermore, these mechanisms do not perform well in online scenario where the energy management unit (EMU) only knows the current and past consumer energy demands. To solve this problem, in this article, we proposed a novel online privacy protection mechanism to protect the privacy of SMs using a progressive average-based algorithm (PABA). The proposed PABA uses two uniquely designed algorithms to ensure protection of privacy and energy cost reduction during peak and off-peak periods. Moreover, an adaptive output smoothing technique is used to further enhance the privacy. Compared with the privacy protection mechanisms designed for tackling SM privacy, the proposed mechanism achieves higher amount of privacy while reducing the energy cost. The validity of the proposed privacy enhancement mechanism is demonstrated by simulation results.