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Integration of renewable resources and increased growth in energy consumption has created new challenges for the traditional electrical network. To adhere to these challenges, Internet of Everything (IoE) has transformed the existing power grid into a modernized electrical network called Smart Grid. An integral part of this transformation is the Advanced Metering Infrastructure (AMI), which enables two-way communication for flow of information consisting of energy consumption, outages, and electricity rates between smart meters and the utilities. These enhanced AMI features and privileges have resulted in a larger surface for cyber-attack, enabling remote exploitation of these smart devices without any physical access. Therefore, consumer privacy and security has become a critical issue due to the interconnection of different smart devices in various communication networks and the information they carry. In this paper, we present a comprehensive survey of privacy related research in the IoE enabled smart grid environment. The survey presents a detailed analysis of privacy problems and their corresponding solutions in AMI. Our goal is to provide an in-depth understanding of the smart grid and shed light on future research directions.

Advanced metering infrastructure (AMI) is the core component of the smart grid. As the wireless connection between smart meters in AMI is featured with high packet loss and low transmission rate, AMI is considered as a representative of the low power and lossy networks (LLNs). In such communication environment, the routing protocol in AMI network is essential to ensure the reliability and real-time of data transmission. The IPv6 routing protocol for low-power and lossy networks (RPL), proposed by IETF ROLL working group, is considered to be the best routing solution for the AMI communication environment. However, the performance of RPL can be seriously degraded due to jamming attack. In this paper, we analyze the performance degradation problem of RPL protocol under jamming attack. We propose a backup node selection mechanism based on the standard RPL protocol. The proposed mechanism chooses a predefined number of backup nodes that maximize the probability of successful transmission. We evaluation the proposed mechanism through MATLAB simulations, results show the proposed mechanism improves the performance of RPL under jamming attack prominently.

Recently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we propose a hybrid approach to detect anomalies associated with electricity theft in the AMI system, based on a combination of two robust machine learning algorithms; K-means and Deep Neural Network (DNN). K-means unsupervised machine learning algorithm is used to identify groups of customers with similar electricity consumption patterns to understand different types of normal behavior. DNN algorithm is used to build an accurate anomaly detection model capable of detecting changes or anomalies in usage behavior and deciding whether the customer has a normal or malicious consumption behavior. The proposed model is constructed and evaluated based on a real dataset from the Irish Smart Energy Trials. The results show a high performance of the proposed model compared to the models mentioned in the literature.

With the widespread deployment of the Advanced Metering Infrastructure (AMI) in Power Industrial Control Systems (PICS), a significant and inherent property of network traffic data is its pronounced class imbalance. The continuous emergence of new types of cyberattacks significantly limits the detection accuracy of Intrusion Detection Systems (IDS). To overcome the limitations of traditional methods—particularly their poor adaptability in complex conditions and vulnerability to emerging threats—this paper introduces a novel hybrid intrusion detection framework. This framework synergistically combines data augmentation and a discriminative classification model for improved performance. Within this framework, a Multi-feature Constrained Conditional Generative Adversarial Network (MC-CGAN) is proposed. Its multi-feature constraint module (MC) preserves protocol-related invariant features, while the CGAN is responsible for conditionally generating the remaining continuous features based on class labels. By preserving the core semantic information of samples, this method reduces the risk of generating unrealistic data and decreases computational overhead. Furthermore, we develop ADS-Net, a lightweight Convolutional Neural Network that not only replaces traditional convolutions with depth-wise separable ones for efficiency, but also incorporates an attention mechanism to adaptively weight feature channels, thus improving discriminative focus. Extensive experiments demonstrate that, under conditions of extreme data imbalance, the proposed hybrid framework can generate industrially valid synthetic data while achieving accurate intrusion detection with an accuracy of 98.35%.

The recent trends in Advanced Metering Infrastructure (AMI) have paved the way for incremental needs within the consumers such as real time billing transparency, data exchanges without errors, high precision, individual load consumption, and remote-control features. In advanced metering infrastructure, the smart energy meter acts as the primary equipment for household metering appliances. Such energy meters come with different design topologies that have proven itself for remote billing, post paid and prepaid packages for electrical consumers, high accuracy, anti-tampering design and remote control over household mains. However, there are few studies that interpret energy meters can be used for energy conservation purposes. The energy conservation suggestions by modern energy meters are not observed effective as they do not assess the individual load characteristics. Moreover, the billing transparency is not based on real time and accurate in the modern energy meters. This paper focuses on advanced billing methodology and consumption of individual load without the need of additional sensors and the meter alerts the user by forecasting the energy consumption continuously.

Evaluating the effects of built environment factors (BEF) on residential land carbon emissions (RLCE) is an effective way to reduce RLCE and promote low-carbon development from the perspective of urban planning. In this study, the Grey correlation analysis method and Universal global optimization method were proposed to explore the effects of BEF on RLCE using advanced metering infrastructure (AMI) data in Zibo, a representative resource-based city in China. The results indicated that RLCE can be significantly affected by BEF such as intensity, density, morphology, and land. The morphology is the most critical BEF in reducing RLCE. Among them, the building height (BH) and building shape coefficient (BSC) had positive effects on RLCE, while the high-rise buildings ratio (HRBR) and RLCE decreased first and then increased. The R2 of BH, BSC, and HRBR are 0.684, 0.754, and 0.699. The land had limited effects in reducing RLCE, and the R2 of the land construction time (LCT) is only 0.075, which has the least effect on RLCE. The results suggest that urban design based on BEF optimization would be effective in reducing the RLCE.

Communication systems play an important role in smart grid (SG). Advanced Metering Infrastructure (AMI) is hybrid architecture in smart grid comprising of smart meters and gateways. With AMI network, consumers can achieve demand side management, real time pricing, load scheduling, and upgrading of software updates through gateways to a number of smart meters without the need to visit every place. Information exchange can occur in the form of meter readings from meters to utility, from meters to AMI and from AMI to utility. Broadcasting is one possible solution in these scenarios for information exchange and cognitive radio networks (CRNs) are one possible solution for communication that provides large data transmission utilizing available spectrum resources from licensed and unlicensed bands. Broadcasting is challenging in CRNs due to licensed/primary user (PU) activity, availability of multiple channels, and channel diversity. Therefore, PU protection must be provided at all cost and data transmission should be quick and reliable for a smooth operation. In this paper, we propose two novel reliable schemes, i.e., probability-based broadcasting scheme for CR-based smart grid and area-based broadcasting scheme for CR-based smart grid specifically designed for AMI networks. Our schemes provide reliable data to smart meters with sufficient PU protection. We have compared our schemes with two flooding techniques, i.e., key policy attribute based encryption (Fadlullah et al. in IEEE Commun Mag 50:150–156, 2012) and key management scheme (Liu et al. in IEEE Trans Ind Electron 60(10):4746–4756, 2013). Simulation in NS-2 validates our schemes as a viable solution in cognitive radio-based smart grid systems.

False Data Injection Attack (FDIA) is an attack that could compromise Advanced Metering Infrastructure (AMI) devices where an attacker may mislead real power consumption by falsifying meter usage from end-users smart meters. Due to the rapid development of the Internet, cyber attackers are keen on exploiting domains such as finance, metering system, defense, healthcare, governance, etc. Securing IoT networks such as the electric power grid or water supply systems has emerged as a national and global priority because of many vulnerabilities found in this area and the impact of the attack through the internet of things (IoT) components. In this modern era, it is a compulsion for better awareness and improved methods to counter such attacks in these domains. This paper aims to study the impact of FDIA in AMI by performing data analysis from network traffic logs to identify digital forensic traces. An AMI testbed was designed and developed to produce the FDIA logs. Experimental results show that forensic traces can be found from the evidence logs collected through forensic analysis are sufficient to confirm the attack. Moreover, this study has produced a table of attributes for evidence collection when performing forensic investigation on FDIA in the AMI environment.

Maintaining a high overall network reliability remains one of the most critical requirements for advanced metering infrastructures (AMIs) in smart grid. Ensuring reliable networks not only determines the robust communications of an AMI, but also guarantees assured information delivery in the access network. To prevent any communication failures, incremental designs based on legacy networks should be carried out in advance to improve the overall redundancy. Current communication architecture of an AMI follows a traditional access network structure with a tree-based topology, which does not always satisfy high robustness and is prone to network failures. To address the challenge, this study conducts a reliability study of the access network in an AMI. Specifically, this study first examines the basic network topology adopted in an AMI access network and its underlying connectivity issues. Secondly, this study proposes two practical solutions as parts of incremental network design to improve the communication robustness of existing communication architectures. Thirdly, mathematical models are formulated to solve network connectivity problems, for maintaining a high overall network reliability, while minimising the communication deployment cost at the same time. Simulation results are provided from the aspects of minimal path sets and minimal cut sets to demonstrate the redundancy analysis.