Power Line Communication (PLC) systems are facing increasing security threats as adversaries leverage low-cost Software-Defined Radios (SDRs) to launch physical-layer attacks, e.g., jamming and Radio Frequency Fingerprinting (RFF), for communication disruption and unauthorized device tracking, respectively. This paper investigates the dual role of Radio Frequency (RF) wireless jamming for PLC environments, through two distinct scenarios: (i) friendly RF jamming for privacy preservation of (cabled) PLC devices against unauthorized RFF, and (ii) adversarial RF jamming to degrade the performance of legitimate RFF-based authentication systems. We conducted various systematic experiments using nine USRP X310 SDRs connected to actual PLC couplers exchanging signals modulated according to the Binary-Phase Shift Keying modulation scheme to analyze the behavior of RFF in PLC scenarios under different RF jamming levels. Our results demonstrate, for the first time, that strategic RF jamming effectively obscures device fingerprints in cabled PLC communications while maintaining communication quality, with bit error rates remaining acceptable across most configurations. We also demonstrate that device identification accuracy degrades significantly as the jamming intensity increases. Our findings establish fundamental trade-offs between privacy protection and authentication reliability, providing insights for the design of robust PLC systems.

Power Line Communication (PLC) use existing electrical infrastructure for data transmission but are susceptible to security threats such as spoofing and impersonation attacks due to their open nature. This paper proposes a novel Device Fingerprinting (DF) approach for device authentication in PLC systems. The approach leverages hardware-induced imperfections in signals transmitted over power lines to identify devices based on their physical-layer characteristics. We develop a methodology that converts raw In-Phase Quadrature (IQ) samples from PLC channels into images, enabling the use of Convolutional Neural Networks for device classification. Our approach demonstrates the feasibility of CNN-based DF in PLC environments using only physical-layer information from received signals. Our experimental validation uses 8 Software Defined Radios and 2 power line couplers in real-world PLC measurements. We evaluate multiple Convolutional Neural Network (CNN) architectures and demonstrate that the PLC device fingerprint consists of two components: radio-specific and coupler-specific characteristics. The results show classification accuracy exceeding 0.9 across different configurations, establishing the viability of DF-based authentication in PLC systems without requiring additional security layers.

This paper introduces a new frequency domain index modulation (FD-IM) technique for a general orthogonal frequency-division multiplexing (OFDM)-based communication system. The proposed technique has been designed to enhance both the spectral and the energy efficiencies of OFDM-based communication systems. In particular, we propose a novel coding scheme for the symbols to be transmitted that leverages the absence of transmission itself to encode a symbol. To the best of our knowledge, this is the first usage of such a coding scheme in the FD-IM OFDM domain. The expected benefits of the proposed solution are as follows: (i) It enhances the spectral efficiency, by increasing the total number of transmit bits for a given set of subcarriers; and, (ii) It improves the energy efficiency. To evaluate and compare the advantages of the proposed FD-IM technique with a baseline subcarrier-index modulated (SIM)-OFDM method, we first have derived the closed-form expressions of the energy gain and the transmit bit gain for both techniques, with respect to the equivalent standard modulation. The theoretical results show a significant enhancement in terms of improving both the spectral and the energy efficiencies of a general OFDM-based communication system. Moreover, we run an extensive experimental campaign to support our findings. Results are striking. For instance, with a binary phase-shift keying (BPSK) modulation, an energy gain of 57% and a transmit bit gain of 58% are experimentally observed. These promising results pave the way to improve the different extension versions of the SIM-OFDM technique that have been presented in the literature. Finally, we also pointed out some further applications of our proposed encoding to the general field of information processing.

The integration of IoT devices with smart home energy management systems (SHEMS) presents a significant advancement in energy demand response (DR) optimization. However, due to the rapid proliferation of home appliances with varying operating characteristics as well as the variable comfort level demands of users, making effective DR decisions becomes more challenging. In this paper, we propose a hierarchical Stackelberg game-based incentive mechanism with multi-agent deep reinforcement learning (MADRL) to optimize DR in IoT-based SHEMS. We formulate the hierarchical decision-making problem as a Markov decision process (MDP) and then adopt the multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve it by finding an equilibrium solution. Through extensive simulations, we demonstrate that our proposed DR optimization approach can effectively reduce overall energy consumption and peak load by 30.41% and 28.57% from the benchmark approaches, respectively. In addition, the proposed approach maintains user comfort and increases system utility by 13.11% and 15.74% than the benchmark schemes, respectively, resulting in improved energy efficiency.

The integration of multi-agent deep reinforcement learning (MADRL) in adaptive and intelligent home energy management systems (AI-HEMS) enhances real-time energy management by enabling intelligent decision-making among multiple agents to optimize various problems. This approach allows smart homes to dynamically respond to changes in energy demand, pricing, and user preferences. The integration of Internet of Things (IoT) devices with AI-HEMS has been promoted to efficiently manage energy resources and maintain occupants’ comfort, where IoT devices collect data on energy consumption, usage patterns, and environmental conditions. However, ensuring trade-offs between conflicting optimization objectives, such as reducing energy consumption and electricity prices, and maximizing users’ comfort levels is challenging. In this paper, we propose a MADRL-based multi-objective demand response (MODR) optimization framework to efficiently manage and control the energy consumption of smart homes. The proposed approach aims to simultaneously reduce energy costs and maximize users’ comfort, improving the overall reliability of energy systems. We first formulate the MODR optimization problem as MDP and then adopt the MADRL algorithm to solve it. The simulation results demonstrate that our proposed DR optimization approach can effectively balance the trade-off between energy cost and user comfort levels, resulting in improved energy efficiency compared to benchmark approaches.

Demand response (DR) plays a significant role in modern energy management systems, particularly within the context of adaptive and intelligent building energy management systems (AI-BEMS). In the AI-BEMS context, DR focuses on dynamically adjusting energy usage in response to external factors, such as electricity prices, grid conditions, and environmental considerations. This survey paper explores the evolving landscape of DR within the framework of AI-BEMS, focusing on the integration of advanced technologies and adaptive strategies to optimize energy consumption and enhance grid reliability. This article reviews state-of-the-art research addressing the key concepts associated with integrating DR and AI-BEMS, including an overview of DR techniques in AI-BEMS, and an artificial intelligence and machine learning applications for the development of adaptive control strategies and DR optimization. Then, insights are provided on the future directions and the challenges in this field regarding the implementation of DR within AI-BEMS.

Due to the broadcast nature of power line communication (PLC) channels, confidential information exchanged on the power grid is prone to malicious exploitation by any PLC device connected to the same power grid. To combat the ever-growing security threats, physical layer security (PLS) has been proposed as a viable safeguard or complement to existing security mechanisms. In this paper, the security analysis of a typical PLC adversary system model is investigated. In particular, we derive the expressions of the corresponding average secrecy capacity (ASC) and the secrecy outage probability (SOP) of the considered PLC system. In addition, numerical results are presented to validate the obtained analytical expressions and to assess the relevant PLS performances. The results show significant impacts of the transmission distances and the used carrier frequency on the overall transmission security.

In this paper, we investigate and evaluate the performances of a subcarrier-index modulation (SIM) technique within an orthogonal frequency division multiplexing (OFDM)-based narrow-band (NB)-power line communication (PLC) system. The SIM technique has been proposed and used mainly in wireless communications to enhance energy and spectral efficiencies. To evaluate the advantages of this technique in PLC, Monte Carlo simulations were performed using field measurements of PLC noise and channel frequency response (CFR). The results show significant advantages in terms of improving the overall system energy and spectral efficiencies, especially for single-level modulation. For instance, when using the SIM-OFDM technique, with a binary phase-shift keying (BPSK) modulation, an energy gain of 66.66% and a bit gain of 50%, with respect to the standard modulation, can be observed.

Power Line Communications (PLC) is a well-established technology that allows devices connected to the power line to communicate with each other. While the majority of research in this field is devoted to issues of availability, the topic of Denial of Service (DoS) attacks has not been sufficiently addressed. Typically, current solutions might detect a jammer when situated near the target devices, yet the equipment under jamming interference may face challenges in communicating an alarm. However, when these systems are placed at a significant distance from the jammer, the negligible impact of the jamming renders its detection hardly detectable. In this work, we propose a solution to identify the presence of a jammer in a PLC infrastructure even when deployed at a significant distance. We analyze the physical layer of the PLC link and adopt state-of-the-art Deep Learning techniques to detect jamming even at a distance where the jammer's effect is negligible, thus allowing the device to trigger an alarm. Considering a jammer featuring the same transmission power as legitimate devices, we prove that we can detect the presence of such a jammer with an overwhelming probability (higher than 0.99) even at a distance of 75 m from the source.

In this paper, we analyze a typical PLC system's data transmission security and reliability. In particular, we consider a passive adversary model–commonly assumed in the literature–and a friendly jamming technique to thwart the attacker. Overall, several contributions are provided: First, the most relevant PLS techniques in the literature are detailed, focusing on the applications, advantages, and disadvantages of each technique, as well as the related PLS performance analysis metrics. Then, we derive the expression of a novel PLC performance analysis metric: the secure and successful transmission probability (SSTP) of the considered PLC system model. Such a metric captures aspects that are not considered in the available ones, and we use it to analyze our use case, considering the adoption of a friendly jamming technique to thwart a passive eavesdropping attack. A complete analytical characterization of the introduced use case is provided. Finally, numerical results are presented to validate the obtained analytical expressions and to assess the relevant PLS and link reliability performances. The results show the significant impacts of the transmission distances, the used carrier frequency, and the jamming signal power on the overall quality of the achieved security and transmission performances. Other than being interesting on their own, these results also provide direct guidance on effectively tuning countermeasures against the considered adversary.

Noise measurement in power line communication (PLC) systems is a common activity performed by grid operators for network tuning operations. Usually, these measurements are carried out with portable devices that have a fixed sensing and storage capacity. In this context, this paper presents a software-only solution for enhancing the performance of noise measurements in PLC systems. In detail: (i) we extend the measurement capacity in terms of the maximum number of samples that can be detected continuously, by using a machine learning (ML)-powered low complexity algorithm; and, (ii) we reduce the discontinuity period between successive measurements. This latter feature enables the possibility of collecting more continuous data. To show the viability of our proposal, we conducted a field measurements campaign to measure the scheme’s accuracy and the measurement capacity extension ratio (MCER). The introduced approach is able to increase the MCER by up to 8 times, in the considered PLC environments, with an accuracy above 90%. While the proposed approach has a clear application—improving current devices’ noise measurements capability without requiring costly hardware upgrades—, the technique herein shown has a general applicability, and could hence pave the way for further applications in related fields.