Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios.

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.

Generation of a dataset for DoW attack detection in serverless architectures.

Denial of Wallet (DoW) attacks refers to a type of cyberattack that aims to exploit and exhaust the financial resources of an organization by triggering excessive costs or charges within their cloud or serverless computing environment. These attacks are particularly relevant in the context of serverless architectures due to characteristics like pay-as-you-go model, auto-scaling, limited control and cost amplification. Serverless computing, often referred to as Function-as-a-Service (FaaS), is a cloud computing model that allows developers to build and run applications without the need to manage traditional server infrastructure. Serverless architectures have gained popularity in cloud computing due to their flexibility and ability to scale automatically based on demand. These architectures are based on executing functions without the need to manage the underlying infrastructure. However, the lack of realistic and representative datasets that simulate function invocations in serverless environments has been a challenge for research and development of solutions in this field. The aim is to create a dataset for simulating function invocations in serverless architectures, that is a valuable practice for ensuring the reliability, efficiency, and security of serverless applications. Furthermore, we propose a methodology for the generation of the dataset, which involves the generation of synthetic data from traffic generated on cloud platforms and the identification of the main characteristics of function invocations. These characteristics include SubmitTime, Invocation Delay, Response Delay, Function Duration, Active Functions at Request, Active Functions at Response. By generating this dataset, we expect to facilitate the detection of Denial of Wallet (DoW) attacks using machine learning techniques and neural networks. In this way, this dataset available in Mendeley data repository could provide other researchers and developers with a dataset to test and evaluate machine learning algorithms or use other techniques based on the detection of attacks and anomalies in serverless environments.

Data transmission reduction formalization for cloud offloading-based IoT systems.

Computation offloading is the solution for IoT devices of limited resources and high-cost processing requirements. However, the network related issues such as latency and bandwidth consumption need to be considered. Data transmission reduction is one of the solutions aiming to solve network related problems by reducing the amount of data transmitted. In this paper, we propose a generalized formal data transmission reduction model independent of the system and the data type. This formalization is based on two main ideas: 1) Not sending data until a significant change occurs, 2) Sending a lighter size entity permitting the cloud to deduct the data captured by the IoT device without actually receiving it. This paper includes the mathematical representation of the model, general evaluation metrics formulas as well as detailed projections on real world use cases.

A Secure Multi-Tier Mobile Edge Computing Model for Data Processing Offloading Based on Degree of Trust.

Current mobile devices need to run applications with high computational demands and critical response times. The mobile edge computing (MEC) paradigm was developed to improve the performance of these devices. This new computation architecture allows for the mobile devices to execute applications on fog nodes at the network edge; this process is called data processing offloading. This article presents a security model for the externalization of application execution in multi-tier MEC environments. The principal novelty of this study is that the model is able to modify the required security level in each tier of the distributed architecture as a function of the degree of trust associated with that tier. The basic idea is that a higher degree of trust requires a lower level of security, and vice versa. A formal framework is introduced that represents the general environment of application execution in distributed MEC architectures. An architecture is proposed that allows for deployment of the model in production environments and is implemented for evaluation purposes. The results show that the security model can be applied in multi-tier MEC architectures and that the model produces a minimal overhead, especially for computationally intensive applications.

A Novel Active Imaging Model to Design Visual Systems: A Case of Inspection System for Specular Surfaces.

The use of visual information is a very well known input from different kinds of sensors. However, most of the perception problems are individually modeled and tackled. It is necessary to provide a general imaging model that allows us to parametrize different input systems as well as their problems and possible solutions. In this paper, we present an active vision model considering the imaging system as a whole (including camera, lighting system, object to be perceived) in order to propose solutions to automated visual systems that present problems that we perceive. As a concrete case study, we instantiate the model in a real application and still challenging problem: automated visual inspection. It is one of the most used quality control systems to detect defects on manufactured objects. However, it presents problems for specular products. We model these perception problems taking into account environmental conditions and camera parameters that allow a system to properly perceive the specific object characteristics to determine defects on surfaces. The validation of the model has been carried out using simulations providing an efficient way to perform a large set of tests (different environment conditions and camera parameters) as a previous step of experimentation in real manufacturing environments, which more complex in terms of instrumentation and more expensive. Results prove the success of the model application adjusting scale, viewpoint and lighting conditions to detect structural and color defects on specular surfaces.

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services.

The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.

A Computational Architecture Based on RFID Sensors for Traceability in Smart Cities.

Information Technology and Communications (ICT) is presented as the main element in order to achieve more efficient and sustainable city resource management, while making sure that the needs of the citizens to improve their quality of life are satisfied. A key element will be the creation of new systems that allow the acquisition of context information, automatically and transparently, in order to provide it to decision support systems. In this paper, we present a novel distributed system for obtaining, representing and providing the flow and movement of people in densely populated geographical areas. In order to accomplish these tasks, we propose the design of a smart sensor network based on RFID communication technologies, reliability patterns and integration techniques. Contrary to other proposals, this system represents a comprehensive solution that permits the acquisition of user information in a transparent and reliable way in a non-controlled and heterogeneous environment. This knowledge will be useful in moving towards the design of smart cities in which decision support on transport strategies, business evaluation or initiatives in the tourism sector will be supported by real relevant information. As a final result, a case study will be presented which will allow the validation of the proposal.