Motivated by stringent power constraints, duty cycling - the practice of turning a mote's radio on and off to conserve energy - has become a fundamental mechanism in the design of Wireless Sensor Networks. Because of its importance, a variety of approaches to duty cycling have emerged during the last decade and are being now proposed with increasingly ambitious goals, such as achieving ultra low duty cycles as low as 0.1%. Such propositions differ mostly in their reliance on nodes' synchronization, which, in turn, translates into different hardware requirements and implementation complexity. However, duty cycling may also differ in other aspects as topology dependency, network density requirements and increase in end-to-end delay. This paper organizes the most important proposals into a taxonomy and provides insights into their strengths and weaknesses in relation to important characteristics of applications, mote's hardware and network deployments.

WMNs are low-cost access networks built on cooperative routing over a backbone composed of stationary wireless routers. WMNs must deal with the highly unstable wireless medium. Therefore, the design of algorithms that consider link quality to choose the best routes are enabling routing metrics and protocols to evolve. In this work, we analyze the state of the art in WMN metrics and propose a taxonomy for WMN routing protocols. Performance measurements for a WMN, deployed using various routing metrics, are presented and corroborate our analysis.

The Internet of Things (IoT) is increasingly a reality today. Nevertheless, some key challenges still need to be given particular attention so that IoT solutions further support the growing demand for connected devices and the services offered. Due to the potential relevance and sensitivity of services, IoT solutions should address the security and privacy concerns surrounding these devices and the data they collect, generate, and process. Recently, the Blockchain technology has gained much attention in IoT solutions. Its primary usage scenarios are in the financial domain, where Blockchain creates a promising applications world and can be leveraged to solve security and privacy issues. However, this emerging technology has a great potential in the most diverse technological areas and can significantly help achieve the Internet of Things view in different aspects, increasing the capacity of decentralization, facilitating interactions, enabling new transaction models, and allowing autonomous coordination of the devices. The paper goal is to provide the concepts about the structure and operation of Blockchain and, mainly, analyze how the use of this technology can be used to provide security and privacy in IoT. Finally, we present the stalker, which is a selfish miner variant that has the objective of preventing a node to publish its blocks on the main chain.

Deep Learning is a very active and important area for building Computer-Aided Diagnosis (CAD) applications. This work aims to present a hybrid model to classify lung ultrasound (LUS) videos captured by convex transducers to diagnose COVID-19. A Convolutional Neural Network (CNN) performed the extraction of spatial features, and the temporal dependence was learned using a Long Short-Term Memory (LSTM). Different types of convolutional architectures were used for feature extraction. The hybrid model (CNN-LSTM) hyperparameters were optimized using the Optuna framework. The best hybrid model was composed of an Xception pre-trained on ImageNet and an LSTM containing 512 units, configured with a dropout rate of 0.4, two fully connected layers containing 1024 neurons each, and a sequence of 20 frames in the input layer $(20\times 2018)$. The model presented an average accuracy of 93% and sensitivity of 97% for COVID-19, outperforming models based purely on spatial approaches. Furthermore, feature extraction using transfer learning with models pre-trained on ImageNet provided comparable results to models pre-trained on LUS images. The results corroborate with other studies showing that this model for LUS classification can be an important tool in the fight against COVID-19 and other lung diseases.

Recently, IEEE started a task group to investigate adding wireless mesh capabilities to its ubiquitous IEEE 802.11 wireless local area networks standard. The proposal is specified as the IEEE 802.11s amendment. Although the IEEE802.11s amendment is still a draft, some implementations based on it may already be found. The first and most widespread of these implementations is the one developed by One Laptop per Child (OLPC) for its educational laptop - the XO. One notable feature of IEEE 802.11s is the fact that the mesh network is implemented at the link layer, relying on MAC addresses rather than IP addresses for its mechanisms. This feature enables the design and development of new CPU-free network devices that provide layer-2 multihop communication. This tutorial describes the main characteristics of the IEEE 802.11s proposal illustrating the advantages and disadvantages of the MAC layer approach in comparison to the traditional layer three paradigm to multihop wireless networks. To achieve this, this work provides a detailed analysis of 802.11s traffic captured in a real testbed, with special attention to the path discovery mechanism. The step by step explanation of the mesh mechanisms highlights how some of the design choices may impact on the scalability and reliability of such networks.