Smart grids (SGs) are becoming increasingly complex with the integration of communication, protection, and automation technologies. However, this digital transformation has introduced new vulnerabilities, especially false data injection attacks (FDIAs) and Denial of Service (DoS) attacks. FDIAs can subtly corrupt measurement data, misleading operators without triggering traditional bad data detection (BDD) methods in state estimation (SE), while DoS attacks disrupt the availability of sensor data, affecting grid observability. This paper presents a deep learning-based framework for detecting and localizing FDIAs, including under DoS conditions. A hybrid CNN, Transformer, and BiLSTM model captures spatial, global, and temporal correlations to forecast measurements and detect anomalies using a threshold-based approach. For further detection and localization, a Multi-layer Perceptron (MLP) model maps forecast errors to the compromised sensor locations, effectively complementing or replacing BDD methods. Unlike conventional SE, the approach is fully data-driven and does not require knowledge of grid topology. Experimental evaluation on IEEE 14–bus and 118–bus systems demonstrates strong performance for the FDIA condition, including precision of 0.9985, recall of 0.9980, and row-wise accuracy (RACC) of 0.9670 under simultaneous FDIA and DoS conditions. Furthermore, the proposed method outperforms existing machine learning models, showcasing its potential for real-time cybersecurity and situational awareness in modern SGs.

This paper seeks to provide a cost benefit analysis of the implementation of a vehicle-to-grid (V2G) charging strategy relative to a smart charging (V1G) strategy from the perspective of an individual electric vehicle (EV) owner with and without solar photovoltaics (PV) located on their roof. This work utilizes a novel AC optimized power flow model (ACOPF) to produce distributed location marginal prices (DLMP) on a modified IEEE-33 node network and uses a complete set of real-world costs and benefits to perform this analysis. Costs, in the form of the addition of a bi-directional charger and the increased vehicle depreciation incurred by a V2G strategy, are calculated using modern reference sources. This produces a more true-to-life comparison of the V1G and V2G strategies from the frame of reference of EV owners, rather than system operators, with parameterization of EV penetration levels performed to look at how the choice of strategy may change over time. Counter to much of the existing literature, when the analysis is performed in this manner it is found that the benefits of implementing a V2G strategy in the U.S.—given current compensation schemes—do not outweigh the incurred costs to the vehicle owner. This result helps explain the gap in findings between the existing literature—which typically finds that a V2G strategy should be favored—and the real world, where V2G is rarely employed by EV owners.

Smart cities use advanced infrastructure and technology to improve the quality of life for their citizens. Collaborative services in smart cities are making the smart city ecosystem more reliable. These services are required to enhance the operation of interoperable systems, such as smart transportation services that share their data with smart safety services to execute emergency response, surveillance, and criminal prevention measures. However, an important issue in this ecosystem is data security, which involves the protection of sensitive data exchange during the interoperability of heterogeneous smart services. Researchers have addressed these issues through blockchain integration and the implementation of smart contracts, where collaborative applications can enhance both the efficiency and security of the smart city ecosystem. Despite these facts, complexity is an issue in smart contracts since complex coding associated with their deployment might influence the performance and scalability of collaborative applications in interconnected systems. These challenges underscore the need to optimize smart contract code to ensure efficient and scalable solutions in the smart city ecosystem. In this article, we propose a new framework that integrates generative AI with blockchain in order to eliminate the limitations of smart contracts. We make use of models such as GPT-2, GPT-3, and GPT4, which natively can write and optimize code in an efficient manner and support multiple programming languages, including Python 3.12.x and Solidity. To validate our proposed framework, we integrate these models with already existing frameworks for collaborative smart services to optimize smart contract code, reducing resource-intensive processes while maintaining security and efficiency. Our findings demonstrate that GPT-4-based optimized smart contracts outperform other optimized and non-optimized approaches. This integration reduces smart contract execution overhead, enhances security, and improves scalability, paving the way for a more robust and efficient smart contract ecosystem in smart city applications.

Carsharing has long supported trip purposes typically made by private vehicles, with grocery shopping especially benefiting from the carrying capacity of a personal vehicle. BlueLA is a one-way, station-based electric vehicle (EV) carsharing service in Los Angeles aimed at improving access in low-income neighborhoods. We hypothesize that BlueLA improves grocery access for underserved households by increasing their spatial-temporal reach to diverse grocery store types. We test two hypotheses: (1) accessibility from BlueLA stations to grocery stores varies by store type, traffic conditions, and departure times; and (2) Standard (general population) and Community (low-income) members differ in perceived grocery access and station usage. Using a mixed-methods approach, we integrate walking and driving isochrones, store data (n = 5888), trip activity data (n = 59,112), and survey responses (n = 215). Grocery shopping was a key trip purpose, with 69% of Community and 61% of Standard members reporting this use. Late-night grocery access is mostly limited to convenience stores, while roundtrips to full-service stores range from 55 to 100 min and cost USD 12 to USD 20. Survey data show that 84% of Community and 71% of Standard members reported improved grocery access. The findings highlight the importance of trip timing and the potential for carsharing and retail strategies to improve food access.

The adoption of wind energy is pivotal for advancing sustainable power systems, particularly in off-grid microgrids where infrastructure limitations hinder conventional energy solutions. The inherent variability of wind generation, however, challenges grid reliability and demand–supply balance, necessitating accurate forecasting models. This study proposes a hybrid framework for short-term wind speed prediction, integrating deep learning (Long Short-Term Memory, LSTM) and ensemble methods (random forest, Extra Trees) to exploit their complementary strengths in modeling temporal dependencies. A multivariate approach is adopted using meteorological data (including wind speed, temperature, humidity, and pressure) to capture complex weather interactions through a structured time-series design. The framework also includes a feature selection stage to identify the most relevant predictors and a hyperparameter optimization process to improve model generalization. Three wind speed variables, maximum, average, and minimum, are forecasted independently to reflect intra-day variability and enhance practical usability. Validated with real-world data from Cuenca, Ecuador, the LSTM model achieves superior accuracy across all targets, demonstrating robust performance for real-world deployment. Comparative results highlight its advantage over tree-based ensemble techniques, offering actionable strategies to optimize wind energy integration, enhance grid stability, and streamline renewable resource management. These insights support the development of resilient energy systems in regions reliant on sustainable microgrid solutions.

The higher integration of photovoltaic (PV) units is an inevitable component of smart city development. Thanks to smart meter devices that can record the exchange of power between the grid and customers, it is expected that homeowners and businesses will tend to install PV arrays on their rooftops and parking lots to benefit from selling power back to the grid. However, the overvoltage issue resulting from high PV penetration is a major challenge that necessitates the active power curtailment of PV units to ensure power grid stability. Fairness-oriented methods aim to minimize the active power of PV units as much as possible, adopting a fairer approach, and then address the PV owner’s satisfaction with fair profit and loss. Maintaining voltage within a limited standard range under very low load conditions while prioritizing PV inverters’ participation in reactive power contribution and attempting to ensure fairer curtailment of active power presents challenges to existing control design approaches. This paper presents twelve new volt–watt curve design methods to achieve these goals and address the challenges. The methods yield polynomial curves, piecewise linear curves, and single linear curves. A unique voltage sensitivity value for each PV inverter is used to determine the control region area and the slope of the curve at the starting point in certain instances. The effectiveness of the proposed methods is discussed by evaluating their capabilities on the 37-bus IEEE system.

This work presents an innovative, energy-efficient IoT routing protocol that combines advanced data fusion grouping and routing strategies to effectively tackle the challenges of data management in smart cities. Our protocol employs hierarchical Data Fusion Head (DFH), relay DFHs, and marine predators algorithm, the latter of which is a reliable metaheuristic algorithm which incorporates a fitness function that optimizes parameters such as how closely the Sensor Nodes (SNs) of a data fusion group (DFG) are gathered together, the distance to the sink node, proximity to SNs within the data fusion group, the remaining energy (RE), the Average Scale of Building Occlusions (ASBO), and Primary DFH (PDFH) rotation frequency. A key innovation in our approach is the introduction of data fusion techniques to minimize redundant data transmissions and enhance data quality within DFG. By consolidating data from multiple SNs using fusion algorithms, our protocol reduces the volume of transmitted information, leading to significant energy savings. Our protocol supports both direct routing, where fused data flow straight to the sink node, and multi-hop routing, where a PDF relay is chosen based on an influential relay cost function that considers parameters such as RE, distance to the sink node, and ASBO. Given that the proposed protocol incorporates efficient failure recovery strategies, data redundancy management, and data fusion techniques, it enhances overall system resilience, thereby ensuring high protocol performance even in unforeseen circumstances. Thorough simulations and comparative analysis reveal the protocol’s superior performance across key performance metrics, namely, network lifespan, energy consumption, throughput, and average delay. When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. Specifically, our protocol extends network lifespan by 299% over PSO-EEC, 264% over LDIWPSO, 306% over OFCA, and 249% over NPSOP. It also reduces energy consumption by 254% relative to PSO-EEC, 264% compared to LDIWPSO, 247% against OFCA, and 253% over NPSOP. The throughput improvements reach 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By fusing data and optimizing routing strategies, our protocol sets a new benchmark for energy-efficient IoT DFG, offering a robust solution for diverse IoT deployments.

Understanding the interplay between traffic optimization parameters and their alignment with sensors, control algorithms, and Sustainable Development Goals (SDGs) is essential for improving urban traffic management. The appropriate selection of parameters in urban traffic management is crucial to optimize vehicular flow and meet the Sustainable Development Goals (SDGs). To find out which parameters are most commonly used and appropriate, a comprehensive study was conducted, the results of which are presented in this article. This study uses a three-phase approach: qualitative exploration, systematic literature review, and multiple-dimensional analysis. This study’s contributions include a practical five-level framework for traffic optimization addressing congestion problems, the identification of 19 commonly used traffic control parameters, the analysis of their implementations in recent intelligent traffic control systems, and a proposal of trends to orient these parameters towards efficiency and compliance with the SDGs. The results lay the groundwork for creating new parameters or modifying existing parameters so that the parameters are oriented not only towards efficiency in control algorithms or user experience but also towards meeting the SDGs.

Smart city operation assumes dynamic infrastructure in various aspects. However, organization and process modelling require domain expertise and significant efforts from modelers. As a result, such processes are still not well supported by IT systems and still mostly remain manual tasks. Today, machine learning technologies are capable of performing various tasks including those that have normally been associated with people; for example, tasks that require creativeness and expertise. Generative adversarial networks (GANs) are a good example of this phenomenon. This paper proposes an approach to generating organizational and process models using a GAN. The proposed GAN architecture takes into account both tacit expert knowledge encoded in the training set sample models and the symbolic knowledge (rules and algebraic constraints) that is an essential part of such models. It also pays separate attention to differentiable functional constraints, since learning those just from samples is not efficient. The approach is illustrated via examples of logistic system modelling and smart tourist trip booking process modelling. The developed framework is implemented in a publicly available open-source library that can potentially be used by developers of modelling software.

Smart cities aim to enhance the quality of life by advancing infrastructure, leveraging technology, and promoting sustainability, balancing environmental, societal, and economic needs for long-term efficiency. Given resource scarcity and environmental regulations, advanced supply chains play a crucial role in developing smart cities by adopting the circular economy concept, which emphasizes maximizing resource efficiency through recycling and remanufacturing. This study delves into the competition between two types of supply chains in the context of reverse logistics: the hybrid supply chain, which utilizes a dual channel including traditional and e-channels for collecting used products, and the traditional supply chain, which relies solely on a traditional channel. Both supply chain models are actively involved in remanufacturing and recycling used products, and each considers varied policies, including incentive-based policies, advertising investments, the acceptance return quality level, the return processing time, and transportation investments, to enhance their performance. Specifically, this research has two primary objectives: (1) evaluating the economic and environmental outcomes across three competitive scenarios, and (2) analyzing the impact of varied policy settings on these outcomes. These objectives frame the analysis of optimal incentive values, return rates, and profitability across the Nash equilibrium and Nash–Stackelberg structures, providing insights into both the strategic and policy dimensions of supply chain operations. The findings indicate that a hybrid supply chain in this case achieves higher return rates and profitability, highlighting the success of its dual-channel strategy and associated policies. Regarding economic goals, both supply chains achieve the highest profits under the Nash–Stackelberg traditional supply chain leadership structure. However, for environmental goals, the traditional supply chain favors Nash equilibrium for higher return rates, while the hybrid supply chain prefers Nash–Stackelberg with traditional leadership. These scenario-specific results emphasize the importance of aligning economic and environmental goals through tailored strategies. A sensitivity analysis, supported by Pareto prioritization, identifies the return quality level and processing time as critical for the hybrid supply chain, and advertisement investments and the return processing time as key for the traditional supply chain. These insights suggest that H-SCs should prioritize stricter quality standards, efficient inspection protocols, and automation (e.g., AI or optical scanning) to improve the quality and processing time efficiency. Meanwhile, T-SCs should focus on advertising traditional channels by emphasizing faster processing time and less restrictive quality standards, while adopting automated time management strategies similar to H-SCs to enhance engagement and profitability. These findings show that by integrating smart city internet-based initiatives and managing related policies, supply chains can enhance circular economy objectives by optimizing both the economic and environmental performance, ultimately fostering more resilient and sustainable supply chains.