Abstract In an era increasingly challenged by misinformation and synthetic media, ensuring the authenticity and integrity of digital videos has become essential for preserving trust and accountability. This paper presents ViDApp, a Decentralized Application (DApp) for video integrity verification that combines immutable on-chain provenance anchors with scalable off-chain media management. The contribution is positioned as a deployment-oriented systems contribution rather than as a new hashing primitive or media-forensic detector. Specifically, ViDApp defines a trust-aware hybrid verification architecture in which integrity-critical evidence is anchored on Ethereum, while large binary storage and contextual metadata are delegated to Firebase through cryptographically linked references that are never trusted without re-hashing. This design enables client-side hashing, wallet-bound ownership, auditable verification, and responsive operation on both desktop and mobile devices. The paper formalizes the registration and verification workflow, details an on-chain role-based access control extension, and provides a systematic security and threat analysis for hybrid video provenance. The evaluation reports scenario-based prototype validation and separates measured/observed evidence from analytical claims about gas cost, latency, storage overhead, and scalability. Overall, ViDApp demonstrates a practical provenance substrate for user-accessible video integrity verification, while explicitly identifying the benchmark-scale experiments, stress tests, and privacy-preserving extensions required for future work.

One of the leading causes of early health detriment is the increasing levels of air pollution in major cities and eventually in indoor spaces. Monitoring the air quality effectively in closed spaces like educational institutes and hospitals can improve both the health and the life quality of the occupants. In this article, we propose an efficient indoor air quality (IAQ) monitoring and management system, which uses a combination of cutting-edge technologies to monitor and predict major air pollutants like CO2, PM2.5, TVOCs, and other factors like temperature and humidity. The aim is to create an intelligent environment for IAQ. The data is captured and monitored using an Internet of Things network of sensors, manufactured by ourselves, in different lecture rooms at the university. The obtained data is then processed and correlated in real time using a complex event processing engine and analyzed by machine/deep learning algorithms. A long short-term memory neural network is proposed to forecast IAQ. Then a decision tree regressor is used to identify the relationships between temperature, humidity and different pollutants like CO2 and PM2.5.

Abstract Smart contracts (SC) are programs embodying certain business logic stored on a blockchain network like Ethereum. The execution of transactions on SC has a cost, measured in gas units, that depends on the low‐level operations performed. Therefore, a poor choice of high‐level language constructs could lead to overcharging users for their transactions. Thus, a testing process focused on possible deviations of the gas used in diverse scenarios could provide substantial global savings. This paper presents a gas‐centered mutation testing approach for taking care of the gas consumed by Solidity SCs. This approach can be useful to improve the test quality to detect gas‐related problems, reason about performance issues that only manifest in certain situations, and identify alternative more optimal implementations. We define and implement several mutation operators specifically designed to perturb gas consumption while preserving contract semantics in general. Our experiments using several real‐world SCs show the feasibility of the technique, with some mutants reproducing meaningful differences in the consumption and exposing some gas limits not tight enough in historic transactions. Therefore, our approach is shown to be a good ally to prevent the appearance of gas‐related issues and lays the groundwork for researchers seeking to improve performance testing practices.

Abstract In recent years, the Internet of Things (IoT) has grown rapidly, as has the number of attacks against it. Certain limitations of the paradigm, such as reduced processing capacity and limited main and secondary memory, make it necessary to develop new methods for detecting attacks in real time as it is difficulty to adapt as has the techniques used in other paradigms. In this paper, we propose an architecture capable of generating complex event processing (CEP) rules for real-time attack detection in an automatic and completely unsupervised manner. To this end, CEP technology, which makes it possible to analyze and correlate a large amount of data in real time and can be deployed in IoT environments, is integrated with principal component analysis (PCA), Gaussian mixture models (GMM) and the Mahalanobis distance. This architecture has been tested in two different experiments that simulate real attack scenarios in an IoT network. The results show that the rules generated achieved an F1 score of .9890 in detecting six different IoT attacks in real time.

The Internet of Things (IoT) has grown rapidly to become the core of many areas of application, leading to the integration of sensors, with IoT devices. However, the number of attacks against these types of devices has grown as fast as the paradigm itself. Certain inherent characteristics of the paradigm, as well as the limited computational capabilities of the devices involved, make it difficult to deploy security measures. This is why it is necessary to design, implement and study new solutions in the field of cybersecurity. In this paper, we propose an architecture that is capable of generating Complex Event Processing (CEP) rules automatically by integrating them with machine learning technologies. While the former is used to automatically detect attack patterns in real time, the latter, through the use of the Principal Component Analysis (PCA) algorithm, allows the characterization of events and the recognition of anomalies. This combination makes it possible to achieve efficient CEP rules at the computational level, with the results showing that the CEP rules obtained using our approach substantially improve upon the performance of the standard CEP rules, which are rules that are not generated by our proposal but can be defined independently by an expert in the field. Our proposal has achieved an F1-score of 0.98 on average, a 76 percent improvement in throughput over standard CEP rules, and a reduction in the network overhead of 86 percent over standard simple events, which are the simple events that are generated when our proposal is not used.

The control of the pandemic caused by SARS-CoV-2 is a challenge for governments all around the globe. To manage this situation, countries have adopted a bundle of measures, including restrictions to population mobility. As a consequence, drivers face with the problem of obtaining fast routes to reach their destinations. In this context, some recent works combine Intelligent Transportation Systems (ITS) with big data processing technologies taking the traffic information into account. However, there are no proposals able to gather the COVID-19 health information, assist in the decision-making process, and compute fast routes in an all-in-one solution. In this paper, we propose a Pandemic Intelligent Transportation System (PITS) based on Complex Event Processing (CEP), Fuzzy Logic (FL) and Colored Petri Nets (CPN). CEP is used to process the COVID-19 health indicators and FL to provide recommendations about city areas that should not be crossed. CPNs are then used to create map models of health areas with the mobility restriction information and obtain fast routes for drivers to reach their destinations. The application of PITS to Madrid region (Spain) demonstrates that this system provides support for authorities in the decision-making process about mobility restrictions and obtain fast routes for drivers. PITS is a versatile proposal which can easily be adapted to other scenarios in order to tackle different emergency situations.

One of the main advantages brought by the Internet of Things (IoT) is the possibility of having large amounts of data from several sources that allow us, once analyzed, to make decisions in various domains in real time. This implies the need to be able to process large volumes of data in more or less limited processing times depending on the application domain. In this sense, complex event processing (CEP), used in conjunction with an enterprise service bus (ESB), has proven to be very efficient in multiple domains. In search for greater efficiency, some CEP engines offer the option of using flow-based programming (FBP) rather than their traditional programming using CEP together with an event bus. However, its use, while it may be more efficient, can lead to other limitations. In this article, we analyze and describe the performance and limitations of using a CEP engine with an ESB versus a CEP engine with FBP. This will allow developers to decide which option is more convenient for their IoT system depending on the application domain and its specific needs.

Abstract Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exacerbated due to the ubiquity and complexity of Artificial Intelligence (AI)-based systems. This is the case of Reinforcement Learning (RL), where autonomous agents learn through trial-and-error how to find good solutions to a problem. Thus, the underlying decision-making criteria may become opaque to users that interact with the system and who may require explanations about the system’s reasoning. Available work for eXplainable Reinforcement Learning (XRL) offers different trade-offs: e.g. for runtime explanations, the approaches are model-specific or can only analyse results after-the-fact. Different from these approaches, this paper aims to provide an online model-agnostic approach for XRL towards trustworthy and understandable AI. We present ETeMoX, an architecture based on temporal models to keep track of the decision-making processes of RL systems. In cases where the resources are limited (e.g. storage capacity or time to response), the architecture also integrates complex event processing, an event-driven approach, for detecting matches to event patterns that need to be stored, instead of keeping the entire history. The approach is applied to a mobile communications case study that uses RL for its decision-making. In order to test the generalisability of our approach, three variants of the underlying RL algorithms are used: Q-Learning, SARSA and DQN. The encouraging results show that using the proposed configurable architecture, RL developers are able to obtain explanations about the evolution of a metric, relationships between metrics, and were able to track situations of interest happening over time windows.

Prioritized Colored Petri Nets (PCPNs) are a well-known extension of plain Petri nets in which transitions can have priorities and the tokens on the places carry data information. In this paper, we propose an extension of the PCPN model withblack sequencing transitions(BPCPN). This extension allows us to easily model the ordered firing of the same transition using an ordered set of tokens on one of its precondition places. Black sequencing transitions are then presented as a shorthand notation in order to model the processing of a flow of events, represented by one of their precondition places. We then show how black sequencing transitions can be encoded into PCPNs, and their application to model Complex Event Processing (CEP), defining a compositional approach to translate some of the most relevant event pattern operators. We have developed MEdit4CEP-BPCPN, an extension of the MEdit4CEP tool, to provide tool support for this novel technique, thus allowing end users to easily define event patterns and obtain an automatic translation into BPCPNs. This can, in turn, be transformed into a corresponding PCPN, and then be immediately used in CPN Tools. Finally, a health case study concerning the monitoring of pregnant women is considered to illustrate how the event patterns are created and how the BPCPN and PCPN models are obtained by using the MEdit4CEP-BPCPN tool.

The Internet of Things (IoT) paradigm demands adapting traditional cybersecurity solutions to address the inherent limitations of IoT environments, in particular their low computational power and limited amount of memory and bandwidth. The Complex Event Processing (CEP) technology has proven to be useful in this context by deploying a CEP engine for detecting real-time attacks in an IoT network. However, CEP is only capable of detecting attacks that have been previously modeled as event patterns. This requires a domain expert who knows the conditions that must be satisfied so that certain attacks can be detected, thus identifying unmodeled ones is not possible. This paper aims to address this problem by proposing a machine learning algorithm that allows for the automatic creation of CEP patterns based on categorized data if the goal is to classify attacks, or even uncategorized data if the objective is to detect anomalies. An evaluation of the effectiveness of the automatically generated patterns for recognizing different attacks in IoT environments is also conducted in this paper.

Blockchain provides an immutable distributed ledger for storing transactions. One of the challenges of blockchain is the particular processing of dynamic queries due to accumulating costs. Complex Event Processing (CEP) provides efficient and effective support for this in a way, however, that is difficult to integrate with blockchain. This paper addresses the research challenges of integrating blockchain with CEP. More specifically, we envision an effective development environment in which (i) event-driven smart contracts are modeled in a graphical way, which are, in turn, (ii) automatically transformed into complementary code that is deployed in both a CEP engine and a blockchain network, and then (iii) executed on off-chain CEP applications which, connected to different data sources and sinks, automatically invoke smart contracts when event pattern conditions are met. We follow a classic systems engineering approach for defining the concepts of our system, called CEPchain, which addresses the described requirements. CEPchain was evaluated using a real-world case study for vaccine delivery, which requires an unbroken cold chain. The results demonstrate that our approach can be applied without requiring experts on event processing and smart contract languages. Our contribution simplifies the design of integrated CEP and blockchain functionality by hiding implementation details and supporting efficient deployment.

Organisations today are constantly consuming and processing huge amounts of data. Such datasets are often heterogeneous, making it difficult to work with them quickly and easily due to their format constraints or their disparate data structures. Therefore, being able to efficiently and intuitively work with such data to analyse them in real time to detect situations of interest as quickly as possible is a great competitive advantage for companies. Existing approaches have tried to address this issue by providing users with analytics or modelling tools in an isolated way, but not combining them as a onein- all solution. In order to fill this gap, we present MEdit4CEP-SP, a model-driven system that integrates Stream Processing (SP) and Complex Event Processing (CEP) technologies for consuming, processing and analysing heterogeneous data in real time. It provides domain experts with a graphical editor that allows them to infer and define heterogeneous data domains, while also modelling, in a user-friendly way, the situations of interest to be detected in such domains. These graphical definitions are then automatically transformed into code, which is deployed in the processing system at runtime. The alerts detected by the system, in real-time, allow users to react as quickly as possible, thus improving the decision-making process. Additionally, MEdit4CEP-SP provides persistence, storing these definitions in a NoSQL database to permit their reuse by other instances of the system. Further benefits of this system are evaluated and compared with other existing approaches in this paper.

The Internet of Things (IoT) is growing globally at a fast pace: people now find themselves surrounded by a variety of IoT devices such as smartphones and wearables in their everyday lives. Additionally, smart environments, such as smart healthcare systems, smart industries and smart cities, benefit from sensors and actuators interconnected through the IoT. However, the increase in IoT devices has brought with it the challenge of promptly detecting and combating the cybersecurity attacks and threats that target them, including malware, privacy breaches and denial of service attacks, among others. To tackle this challenge, this paper proposes an intelligent architecture that integrates Complex Event Processing (CEP) technology and the Machine Learning (ML) paradigm in order to detect different types of IoT security attacks in real time. In particular, such an architecture is capable of easily managing event patterns whose conditions depend on values obtained by ML algorithms. Additionally, a model-driven graphical tool for security attack pattern definition and automatic code generation is provided, hiding all the complexity derived from implementation details from domain experts. The proposed architecture has been applied in the case of a healthcare IoT network to validate its ability to detect attacks made by malicious devices. The results obtained demonstrate that this architecture satisfactorily fulfils its objectives.

Air pollution generated by road traffic in large cities is a great concern in today’s society since pollution has an important impact on human health, even causing premature deaths. To address the problem, this paper presents an Intelligent Transportation System model based on Complex Event Processing technology and Colored Petri Nets (CPNs). It takes into consideration the levels of environmental pollution and road traffic, according to the air quality levels accepted by the international recommendations as well as the handbook emission factors for road transport methodology. This proposal, therefore, tackles a common problem in today’s large cities, where traffic restrictions must be applied due to environmental pollution. CPNs are used in this work as a tool to make decisions about traffic regulations, so as to reduce pollution levels.

Complex Event Processing (CEP) is an event-based technology that allows us to process and correlate large data streams in order to promptly detect meaningful events or situations and respond to them appropriately. CEP implementations rely on the so-called Event Processing Languages (EPLs), which are used to implement the specific event types and event patterns to be detected for a particular application domain. To spare domain experts this implementation, the MEdit4CEP approach provides them with a graphical modeling editor for CEP domain, event pattern and action definition. From these graphical models, the editor automatically generates a corresponding Esper EPL code. Nevertheless, the generated code is syntactically but not semantically validated. To address this problem, MEdit4CEP is extended in this paper by Prioritized Colored Petri Net (PCPN) formalism, resulting in the MEdit4CEP-CPN approach. This approach provides both a novel PCPN domain-specific modeling language and a graphical editor. By using model transformations, event pattern models can be automatically transformed into PCPN models, and then into the corresponding PCPN code executable by CPN Tools. In addition, by using PCPNs we can compare the expected output with the actual output and can even conduct a quantitative analysis of the scenarios of interest. To illustrate our approach, we have conducted an air quality level detection case study and we show how this novel approach facilitates the modeling, simulation, analysis and semantic validation of complex event-based systems.