Context: The growing adoption of distributed and industrial-grade applications built on blockchain infrastructures has intensified the need for systematic approaches to manage the lifecycle of long-running systems, where governance, auditability, and performance constraints must coexist. Objectives: This paper presents TRUST , a lifecycle-oriented system architecture for the governed evolution and integration of smart contract based components. Methods: The architecture incorporates on-chain governance, version traceability, rollback support, and code provenance to enable accountability and observability across successive deployments in multi-stakeholder systems. A full 2 4 factorial evaluation was conducted by treating governance, versioning, provenance, and ABI handling as independent system factors and measuring their effects on latency, throughput, and gas consumption. Results: The results show that governance and provenance introduce controlled and predictable overheads, while compact ABI handling improves throughput and reduces gas consumption by more than 20%. Conclusion: These findings indicate that a lifecycle-oriented architecture can balance accountability and efficiency in governed smart contract applications.

Abstract Video surveillance has benefited greatly from advances in artificial intelligence, particularly in computer vision, and the number of monitored environments has consequently increased, creating new challenges in extracting relevant information. When multiple cameras cover adjacent areas, overlapping fields of view can cause the same subject to be detected across cameras, introducing duplicate counts. Although the literature offers established solutions, many rely on high-quality recordings and complex, computationally intensive methods, limiting their use on resource-constrained devices. In this work, we address these challenges with a solution that leverages minimal information about target subjects and uses a transductive strategy to detect duplicates based on interactions within overlapping fields of view. Experiments in real-world settings show that our approach suppresses duplicates effectively, making it suitable for deployment on resource-limited hardware. We evaluate state-of-the-art lightweight models with high inference speed, as well as classical re-identification methods, in scenarios with low-quality video and constrained devices. The results underscore the effectiveness of the proposed approach and motivate exploration of re-ID with domain adaptation, as well as anchor-free methods with weak or semi-supervised learning.

Due to limited hardware, consumer-grade surveillance cameras usually rely on cloud-based computer vision models to detect people in video footage. However, this approach introduces a recurring cost, as users must continuously pay for cloud processing. One possible solution is to use mobile devices for person detection, as they can be found in most households. Yet, experimental evaluations on the impact of different computer vision models on mobile device resource usage are limited. This study examines the efficiency of YOLOv8 models on Android devices, assessing detection performance, inference time, memory consumption, and energy efficiency. The models were tested using two machine learning frameworks, LiteRT (formerly TensorFlow Lite) and ONNX Runtime, to determine the most suitable approach for mobile inference. Experimental results confirm that compact models, such as YOLOv8n and YOLOv8s, offer the best trade-off between computational efficiency and detection accuracy, while LiteRT outperforms ONNX in all evaluated metrics.

Smart homes are transforming domestic environments by integrating connected devices and sensors, enabling lighting, temperature, and security automation. While these systems enhance comfort and efficiency, they often rely on predefined settings or manual input due to the absence of adaptive recommendation systems. AI-driven recommendation systems personalize actions by learning from user behavior and environmental data, improving the smart home experience. However, they also introduce cybersecurity risks, particularly data poisoning attacks, where manipulated data disrupts system functionality. This paper exposes and examines vulnerabilities in smart home recommendation systems, categorizing data poisoning attacks and analyzing their impact. Through a literature review and attack vector analysis, we identify key weaknesses and propose mitigation strategies to enhance security. Our goal is to contribute to developing robust smart home technologies that protect user privacy, ensure reliability, and withstand adversarial threats.

ABSTRACT Context Smart home devices have become increasingly popular in modern households, powered by the Internet of Things (IoT) advances. The data generated by smart devices can provide valuable insights into users' behavior and preferences. By analyzing the data, one can understand how people interact with their homes, thus creating a “smart home profile”. To comprehend the complete IoT ecosystem dynamics of an intelligent environment, it is necessary to learn from each IoT device to predict its status in the future time. Nevertheless, dealing with real‐world IoT data structure requires considerable preprocessing tasks and the employment of classifiers that can learn multiple IoT inputs from a single IoT message. Objective Aware of these challenges, this paper proposes a novel methodology to process multi‐label IoT data and provide a comprehensive comparison of multi‐label classifiers for forecasting the status of smart devices, considering their efficiency and accuracy. Method We propose a data transformation method to preprocess the IoT data to be used by multi‐label classifiers. This method is based on real data structure. Results We evaluate our proposal in two real‐world scenarios and various multi‐label classifiers. The promising findings indicate that efficient classifiers can generate many correct predictions for a comprehensive IoT ecosystem in a small fraction of a second. Conclusions Our proposed data transformation can fit the context of prediction to smart homes and work with multi‐label classifiers to understand user behavior.

Integrating quantum computing with edge computing presents an approach to managing the vast data generated by Connected Autonomous Vehicles (CAVs). This article introduces a novel framework that leverages quantum algorithms at the network edge to significantly enhance the efficiency and speed of data analysis for CAVs. By combining the principles of quantum computing with the distributed nature of edge computing, we propose a solution that not only addresses the latency and bandwidth challenges of conventional cloud computing models but also paves the way for real-time decision-making processes essential for autonomous vehicle operations. Our findings reveal the potential for quantum edge computing to transform the processing capabilities at the edge, offering a scalable, efficient, and faster method for data analysis that could significantly improve traffic management, urban mobility, and overall safety on the roads.

Smart grids integrate intelligence, automation, and communication into the electrical grid infrastructure, primarily through the use of smart meters. These meters play a crucial role in collecting and transmitting data, either to the cloud, which may cause delays, or to the edge, where meters are closer to the data source. In this paper, we propose Q-Balance, a neural network-based solution for optimizing computational resources at the edge, thus minimizing service processing time. Q-Balance utilizes the Multi-Layer Perceptron (MLP) technique to estimate response times for requests processed by computational resources. Evaluation results demonstrate that Q-Balance can significantly reduce the average response time, achieving up to a 65% reduction compared to the Min-Load approach at the edge and up to 79% in the cloud.

Abstract With industry 4.0, data-based approaches are in vogue. However, extracting the essential features is not a trivial task and greatly influences the final result. There is also a need for specialized system knowledge to monitor the environment and diagnose faults. In this context, the diagnosis of faults is significant, for example, in a vehicle fleet monitoring system, since it is possible to diagnose faults even before the customer is aware of the fault, minimizing the maintenance costs of the modules. In this paper, several models using machine learning (ML) techniques were applied and analyzed during the fault diagnosis process in vehicle fleet tracking modules. Two approaches were proposed: ‘With Knowledge’ and ‘Without Knowledge’, to explore the dataset using ML techniques to generate classifiers that can assist in the fault diagnosis process. The approach ‘With Knowledge’ performs the feature extraction manually, using the ML techniques: random forest, naive Bayes, support vector machine and Multi Layer Perceptron; on the other hand, the approach ‘Without Knowledge’ performs an automatic feature extraction, through a convolutional neural network. The results showed that the proposed approaches are promising. The best models with manual feature extraction obtained a precision of 99.76% and 99.68% for detection and detection and isolation of faults, respectively, in the provided dataset. The best models performing an automatic feature extraction obtained, respectively, 88.43% and 54.98% for detection and detection and isolation of failures.

Even with advances in Smart Grids and their cybersecurity recommendations, recent attacks on automation and protection systems of these structures show that it is still necessary to investigate this research problem. With that in mind, this work proposes STRAYER: a SmarT aRchitecture Against cYbERattacks to reduce the vulnerability of automation equipment in Smart Grids. STRAYER integrates cybersecurity for monitoring and shielding access, interoperability for maintaining communication between equipment/devices, and risk management for maintaining reliability and preventing real-time cyberattacks on Smart Grids. To validate the STRAYER, we built a prototype commonly used in smart grids. The results showed that STRAYER increases the security efficiency compared to the traditional architecture, reducing the amount of infected equipment and the undue access time to Smart Grids. In addition to the reductions in the amount of IED’s affected by invasions, it was also possible to notice that STRAYER avoided the collapse of a Smart Grid, having only minimal and reversible losses, unlike the traditional architecture.

Delivering cloud-like computing facilities at the network edge provides computing services with ultra-low-latency access, yielding highly responsive computing services to application requests. The concept of fog computing has emerged as a computing paradigm that adds layers of computing nodes between the edge and the cloud, also known asmicro data centers,cloudlets, orfog nodes. Based on this premise, this article proposes a component-based service scheduler in a cloud-fog computing infrastructure comprising several layers of fog nodes between the edge and the cloud. The proposed scheduler aims to satisfy the application’s latency requirements by deciding which services components should be moved upwards in the fog-cloud hierarchy to alleviate computing workloads at the network edge. One communication-aware policy is introduced for resource allocation to enforce resource access prioritization among applications. We evaluate the proposal using the well-known iFogSim simulator. Results suggest that the proposed component-based scheduling algorithm can reduce average delays for application services with stricter latency requirements while still reducing the total network usage when applications exchange data between the components. Results have shown that our policy was able to, on average, reduce the overload impact on the network usage by approximately 11 percent compared to the best allocation policy in the literature while maintaining acceptable delays for latency-sensitive applications.

Traffic congestion is a major problem in metropolitan areas, which inevitably leads to substantial social and economic impacts. In the Connected Autonomous Vehicles (CAVs) context, Intelligent Transportation System (ITS) addresses routing techniques for building an efficient transportation system in an urban environment. In order to improve traffic management, CAVs use real-time traffic data to disseminate faster routes for vehicles. Meanwhile, Cloud Computing is used to manage the traffic congestion situation, but it is not a suitable option for low-latency requirements of autonomous vehicles. Fog-based approaches dealing with traffic congestion found in the literature do not consider the use of caching for a routing scheme. Therefore, we propose a reliable caching mechanism for autonomous vehicle path planning based on Fog Computing, which is called ReCall. ReCall caches real-time traffic information from different regions to dynamically perform route recommendations. The results have shown that ReCall is able to reduce travel time and emissions.

In order to feed a Traffic Congestion Detection System (TCDS), road safety messages (beacons) are continuously exchanged on Vehicular Ad hoc Networks (VANETs) through the IEEE 802.11p control channel. In VANET, the number of beacons in the communication network increases as the number of vehicles on the roads increases, raising communication costs. For a TCDS, clustering algorithms have been used to detect source and level of the traffic congestion based on vehicular density, as well as group similar traffic data that may lead to a reduction in the amount of data on the network. However, these clustering approaches have been employed to work only in a static dataset. Therefore, we propose a Fog Computing Framework that employs an adapted DBSCAN to reduce the amount of data produced in an online traffic data stream environment. The aim is to offer a more suitable approach for reducing the online traffic data stream, which is sent from Fog to the Cloud without losing accuracy of information related to road congestion. The evaluation results have shown that there is a dependence relationship between the size of the DBSCAN's radius, the amount of reduced data, and the congestion level accuracy.

As information about clients and businesses is migrating to the cloud, there are growing concerns about how safe this environment is. Furthermore, it is known that as more stringent levels of security are required, the countermeasures, necessary to maintain the security of the system, are subjected to increasing interference on the performance. In the case of cloud computing, it is possible to compensate the overhead generated by the security mechanisms by changing the number of available resources on-the-fly. The aim of this paper is to perform a performance evaluation of a service involving the application of security mechanisms. We considered the change of computing resources during the execution time by means of a dynamic and self-managed module, which was responsible for load balancing, efficient utilization of resources and Quality of Service level assurance. According to the results of the experiments, we verified that the approach in the Vertical environment provided the fulfilment of the requirements defined in the Service Level Agreement, even with security overhead, with slight changes in the service costs.

In the last 20 years, the amount of energy consumed has grown more than 50% and due to a shortage of energy resources in the future, will not be possible to meet all this demand. The current distribution model transports energy from stations to consumers, but does not consider the use of alternative sources. The smart grids have emerged to allow the inclusion of alternative forms of energy generation in the grid. Yet, to avoid an overload in the system is necessary to calculate the power flow in real time. In this paper, we use Fog Computing as mean to reduce the logical distance between the central distribution and consumption spot. IoT devices in the network edge have more effectiveness and less cost to handle the power flow information. We evaluate the performance of the Newton-Raphson and Gauss-Seidel algorithms with the objective of developing calculations in real time of the load flow problem with the help of fog. Our results have shown that is possible to make a smart grid based on Fog Computing and thus making smart electric networks that react to the environment.

There are a large number of cameras distributed throughout smart cities. As amount as the number of cameras increases, a huge streaming workload is produced. Although Fog computing has been used to reduce latency and jitter, Gateways IoT are unable to identify whether the data produced is invalid or absent, affecting the quality of service. Therefore, this paper presents an analysis of gap filling algorithms to smart surveillance environment. Our study shows that it is possible to maximize the accuracy of data imputation. Performance evaluation shows significant improvements in the imputation of missing data using Singular Spectrum Analysis (SSA), increasing the accuracy in the estimation of the Streaming video, and thus improving the quality of service.

The vast cloud computing environment holds out good prospects for researchers in the computing technology field. However, with several Cloud providers offering different pricing models, the evaluation and modeling of Cloud environments and applications are getting harder because there is a lack of tools for this task. We propose the CM Cloud Simulator to fill this gap since it provides a comprehensive and dynamic simulation of applications with various deployment configurations and incurs the cost it would require when implemented in a Cloud Provider, according to the cost model of any service provider. The CM Cloud Simulator also provides custom-built cost models through the XML file.

This project presents modeling, prototyping and results of the middleware developed for Web service dynamic selection in Web services composition named DWSC-M (Dynamic Web Service Composition Middleware). DWSC-M's main focus is to choose what Web services will be part of the composite Web services in runtime. The choose is made considering aspects of QoS - Quality of Service. To evaluate this approach two algorithms for Web Services selection has been proposed and implemented: the first one uses Random Selection (RS) and the second one uses Euclidean Distance (ED) for the selection of services and considers for this purpose the QoS attributes requested by a client.