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.

Connected and digital electricity substations based on IEC–61850 standards enable novel applications. On the other hand, such connectivity also creates an extended attack surface. Therefore, Intrusion Detection Systems (IDSs) have become an essential component of safeguarding substations from malicious activities. However, in contrast to traditional information technology systems, there is a serious lack of realistic data for training, testing, and evaluating IDSs in smart grid scenarios. Many existing substation IDSs rely on datasets from other contexts or on proprietary datasets that do not allow reproducibility, validation, or performance comparison with competing algorithms. To address this issue, we propose the Efficacious Reproducer Engine for Network Operations (ERENO) synthetic traffic generation framework based on the IEC–61850 standard specifications. As an additional contribution, and as a proof-of-concept, we create and make available a suite of realistic IEC–61850 datasets that model 8 use cases, namely traffic for 7 common attacks and one for normal network traffic. Based on those datasets, we further evaluate how enriched features combining raw data from the substation can significantly improve intrusion detection performance. Our results suggest that it can improve F1-Score up to 47.22% for masquerade attacks.

Smart meters are responsible for keeping track of user energy consumption in a smart grid infrastructure. This data is periodically sent to one or more data aggregation points (DAPs), typically via wireless communication. Efficiently choosing the best positions for installing DAPs is an NP-Complete problem and therefore a difficult task, specially in big cities that may contain thousands of smart meters in a single neighborhood. Nevertheless, network planning has a major impact on network performance. This work proposes a reduction of the DAP positioning problem to a problem in the optimization area known as the Set Covering and a heuristic to solve it. This reduction considers a pre-processed subset of reliable links estimated based on the neighborhood characteristics, device's communication technology, antenna heights and transmission rate. The obtained solution corresponds to the least number of DAPs, and its positions, necessary to cover an entire neighborhood. Our heuristic divides the problem in smaller independent sets that are solved separately and united afterwards. A post-optimization method is also applied in order to improve the heuristic's solution. Heuristic and linear programming techniques are compared and results show that our heuristic is capable of obtaining solutions 0.05% close to the optimal while reducing both the execution time and the memory consumption by 2.27 and 8.14 times, respectively. Additionally, our heuristic was able to obtain results for large instances where the optimal solution failed due to insufficient memory.