The growth of data generation capabilities, facilitated by advancements in communication and computation technologies, as well as the rise of the Internet of Things (IoT), results in vast amounts of data that significantly enhance the performance of machine learning models. However, collecting all necessary data to train accurate models is often unfeasible due to privacy laws. Federated Learning (FL) evolved as a collaborative machine learning approach for training models without sharing private data. Unfortunately, several in-design vulnerabilities have been exposed, allowing attackers to infer private data of participants and negatively impacting the performance of the federated model. In light of these challenges and to encourage the development of FL solutions, this paper provides a comprehensive analysis of secure FL proposals that both protect user privacy and enhance the performance of the model. We performed a systematic review using predefined criteria to screen and extract data from multiple electronic databases, resulting in a final set of studies for analysis. Through the systematic review methodology, the paper groups the security vulnerabilities of FL into model-performance and data privacy attacks. It also presents an analysis and comparison of potential mitigation strategies against these attacks. Additionally, the paper conducts a security analysis of state-of-the-art FL applications and proposals based on the vulnerabilities addressed. Finally, the paper outlines the main applications of secure FL and lists future research challenges. The survey highlights the crucial role of security strategies in ensuring the protection of user privacy and model performance in the context of future FL applications.

In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

Address assignment is a key challenge in ad hoc networks due to the lack of infrastructure. Autonomous addressing protocols require a distributed and self-managed mechanism to avoid address collisions in a dynamic network with fading channels, frequent partitions, and joining/leaving nodes. We propose and analyze a lightweight protocol that configures mobile ad hoc nodes based on a distributed address database stored in filters that reduces the control load and makes the proposal robust to packet losses and network partitions. We evaluate the performance of our protocol, considering joining nodes, partition merging events, and network initialization. Simulation results show that our protocol resolves all the address collisions and also reduces the control traffic when compared to previously proposed protocols.

Smart grid refers to the advancement of the current electric grid with the help of information and communication technologies. In such scenarios, an efficient and secure smart grid communication system is essential. Accordingly, Software-Defined Networks (SDN) have been gaining more prominence for smart grids in recent years, as this paradigm allows monitoring and managing these communication networks efficiently. An example is the Autonomic and Resilient Framework for Smart Grids (ARES). ARES allows integration between the supervisory system and the SDN controller, allowing for better energy applications. Although ARES emphasizes the need for security as a premise for its implementation, it does not provide a concrete implementation of this aspect. In this work, we provide an extensive review of the related literature about Authentication, Authorization, and Accountability (AAA) for smart grids, classifying approaches according to several relevant characteristics. We further propose 3AS, extending the ARES framework with a security component based on the IEEE 802.1X standard. 3AS has been implemented on the Ryu SDN controller and evaluated in an emulated environment. Results indicate 3AS successfully provides authentication, authorization, and accountability with low control load and delay, thus being suitable for diverse smart grid scenarios.

The next-generation TV broadcasting systems must meet the demand for better audio and video quality, higher accessibility and interactivity, and personalized services on fixed and mobile devices. In Brazil, field measurement tests are being performed to evaluate the performance of candidate technologies. The Advanced ISDB-T is a promising Japan Broadcasting Corporation (NHK) technology. It evolves from the current ISDB-T standard, already adopted in Brazil, increasing the system’s flexibility, robustness, and spectrum efficiency. It also includes MIMO technologies to allow for high transmission rates. This work assesses the Advanced ISDB-T system performance in field tests, considering a 2x2 MIMO transmission through a Rayleigh channel, considering the requirements defined for the next generation of Brazilian Digital TV systems. We evaluate the channel power, threshold C/N ratio, and reception threshold on 20 measurement sites with outdoor reception. The measurements of the parameter Condition Number, which quantifies the suitability of the radio channel for MIMO transmission, indicate that the channel was well-conditioned at all measurement sites but one and that the tested system efficiently demodulated the MIMO received signal, even under less unfavorable conditions. This occurs when the C/N threshold is less than zero and the corresponding condition number is equal to or less than 2 dB.