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The growing adoption of electric vehicles (EVs) and the expansion of their charging infrastructure offer substantial potential to advance sustainable mobility in smart cities. Nevertheless, the highly interconnected design of EV charging stations (EVCSs) renders this infrastructure vulnerable to multiple cybersecurity threats, such as false data injection, man-in-the-middle attacks, malware infections, and denial-of-service attacks. Moreover, financial threats—including fraudulent billing and credit card data theft—pose significant risks to EV users. In this study, we introduce a blockchain network built on Hyperledger Fabric tailored for EVCSs to address these vulnerabilities. The proposed network employs smart contracts to securely and transparently handle essential operations, including authentication, charging session oversight, and payment confirmation. By identifying and preventing unauthorized data alterations or access attempts, the blockchain framework enhances the robustness of EVCS networks. A before-and-after comparative evaluation of the implemented blockchain network illustrates its effectiveness in countering prevalent cyberattacks targeting EVCS infrastructure. The evaluation encompasses performance indicators derived from the Hyperledger Caliper benchmarking tool, revealing the solution’s minimal latency suitable for real-time applications and its capacity to scale in line with expanding EV infrastructure demands. Implementing this blockchain-augmented security solution is anticipated to boost user confidence and dependability in EVCS systems. 

In large cities with evolving infrastructure, optimizing water supply systems is critical. However, tackling these issues becomes considerably more difficult without a thorough understanding of the network layout and water-use patterns. This paper introduces a methodology based on geospatial data analysis to address two primary objectives. The first is the definition of service zones for infrastructure facilities to improve overall system manageability. The second is the development of an approach to determine the optimal locations for devices that collect and transmit hydraulic network parameters, ensuring their placement aligns with both water supply sources and the areas they serve. The research utilizes data from the water supply network of a city with a population of more than half a million, applying hierarchical clustering with Ward’s method to examine spatial distribution. Four territorial clusters were distinguished, each exhibiting distinct characteristics related to consumer density and water consumption volumes. The resulting cluster boundaries were compared with the system’s current service scheme, demonstrating consistency with the actual infrastructure. Clustering quality was additionally assessed using the silhouette coefficient, which confirmed the high precision and dependability of the selected method. The study illustrates the value of visualizing cluster boundaries in evaluating the even placement of pressure sensors throughout the urban water supply network. The findings indicate that combining geographic data with water consumption details not only supports efficient infrastructure planning and resource distribution but also lays the foundation for the digital transformation of the hydraulic network—an essential element of sustainable progress in contemporary smart cities.

This research proposes a novel, low-power routing protocol tailored for IoT networks that merges intelligent data-aggregation clustering with optimized path selection to overcome critical data-handling challenges in smart urban environments. The protocol relies on a layered architecture that includes Data Fusion Heads (DFH), intermediate relay DFHs, and the marine predators algorithm—an effective nature-inspired optimizer equipped with a purpose-built fitness function. This function simultaneously refines multiple criteria: the geographic clustering density of Sensor Nodes (SNs) inside each data fusion group (DFG), the total hop distance toward the sink, intra-group node proximity, residual battery level (RE), Average Scale of Building Occlusions (ASBO), and the rotation interval of the Primary DFH (PDFH). One of the protocol’s core advancements is the systematic application of in-network data fusion, which drastically reduces duplicate packet forwarding and markedly improves the accuracy and usefulness of collected information across all DFGs. By aggregating readings from multiple sensors, the volume of outbound traffic is greatly reduced, yielding substantial improvements in energy efficiency. Two complementary forwarding strategies are supported: single-hop forwarding, where already fused payloads travel directly to the sink, and multi-hop forwarding, in which an appropriate PDF relay is dynamically elected using a weighted relay selection metric that incorporates remaining energy (RE), sink distance, and ASBO. By integrating robust fault-tolerance procedures, intelligent redundancy elimination, and efficient fusion mechanisms, the protocol achieves markedly greater dependability and sustains strong operation under adverse or unpredictable network conditions. Detailed simulation experiments, along with side-by-side benchmarking against contemporary methods, confirm the protocol’s clear advantage across essential performance indicators—namely, network longevity, total energy expenditure, data throughput, and mean end-to-end latency. Compared with leading recent approaches such as 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), the presented solution delivers outstanding gains. Concretely, network lifetime is prolonged by 299% versus PSO-EEC, 264% versus LDIWPSO, 306% versus OFCA, and 249% versus NPSOP. Energy usage drops by 254% relative to PSO-EEC, 264% relative to LDIWPSO, 247% relative to OFCA, and 253% relative to NPSOP. Throughput rises by 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By skillfully combining data aggregation with carefully tuned routing decisions, this protocol establishes a new benchmark for power-efficient organization and communication within IoT-based data fusion groups, delivering a dependable and adaptable framework suitable for diverse smart-city and IoT scenarios.

Swift technological progress has heightened the importance of managing charging demand and refining travel routes for electric vehicle (EV) fleets, particularly in urban settings. Sensors based on IoT, embedded in charging stations and vehicles, supply live information about driver charging routines, road congestion, vehicle positioning, and surrounding conditions—greatly strengthening forecasting and optimization techniques. This continuous stream of IoT data powers the combined deep learning architecture, GNN-ViGNet, to predict upcoming EV charging requirements. An analysis of readings from 400,000 IoT devices distributed across charging points and EVs in Texas was performed to reveal typical charging behavior. These devices continuously log key indicators, including user charging preferences, current traffic levels, and diverse external conditions. Data refinement was achieved through Frequency-Aware Dynamic Range Scaling, together with sophisticated preprocessing techniques including Categorical Encoding. Ultimately, the GNN-ViGNet framework delivered a prediction accuracy of 98.9%. Beyond conventional metrics such as Root-Mean-Square Error (RMSE) and Mean Squared Error (MSE), the research introduced the Forecast Accuracy Rate (FAR) and the Charging Load Variation Index (CLVI) to provide a more comprehensive evaluation of predictive capability. The work introduces both an advanced forecasting system and a novel hybrid Coati–Northern Goshawk Optimization (Coati–NGO) technique designed for route planning. Real-time route modifications become possible thanks to live IoT feeds that include congestion updates, vehicle coordinates, and remaining battery capacity. The Coati–NGO algorithm combines the broad search strength of Coati Optimization (COA) with the refined exploitation strength of Northern Goshawk Optimization (NGO). Compared against Particle Swarm Optimization (919 km) and the Firefly Algorithm (914 km), it substantially reduced the total distance traveled to 511 km. Moreover, this combined method exhibited quicker convergence, consistently locating high-quality solutions by the 100th iteration. Overall, the presented EV fleet management system leverages continuous IoT inputs to improve charging network performance, reduce operating expenses, and enhance operational effectiveness—delivering an adaptable, realistic framework suitable for city-wide electric mobility.

Cities undoubtedly play a crucial part in efforts to mitigate climate change. Many urban areas worldwide have pledged to pursue sustainability shifts by adopting environmentally friendly approaches. In recent years, Europe has seen a notable increase in smart city initiatives and progress toward building more sustainable urban environments. Currently, the idea of Positive Energy Districts (PEDs) is motivating districts and municipalities to move away from conventional practices and strive for greater carbon neutrality. This article examines five municipalities—Maia, Reykjavik, Kifissia, Kladno, and Lviv—that are participating in an active Horizon 2020 initiative. The main goal is to explore how these cities identified and chose the 10 solutions they plan to replicate. Data were gathered via conversations, interviews, and the replication roadmaps prepared by each city. It should be emphasized that every city faces unique conditions regarding political backing, funding availability, technical capabilities, and stakeholder engagement—circumstances that apply universally when introducing innovative, efficient measures. Municipalities throughout Europe and other regions may encounter comparable challenges, so this article also presents the experiences of these five Fellow Cities as they advance toward PED implementation.