The rapid growth of grid-integrated solar plants and Distributed Energy Resources (DER) has introduced advanced electrical devices into both domestic and industrial environments. In this expanding landscape, standardization is essential to ensure compatibility, security, and seamless communication among devices from various manufacturers. This paper explores the IEEE 2030.5-2018 standard, which is designed to facilitate communication between the Smart Grid and its end-users. While providing a comprehensive overview of the standard, we particularly focus on its security features, with an emphasis on the challenges associated with managing digital certificates within the IEEE 2030.5 framework. Additionally, we conduct a systematic review of the existing literature related to this standard.

The Restricted Access Window (RAW) was introduced in IEEE 802.11ah to improve the performance of Dense Wireless Networks (DWN). RAW restricts the number of stations that can access the channel by separating them into groups. This work proposes two clustering strategies for forming RAW groups particularly targeting the reduction of hidden terminals within groups, increasing the performance of DWN. The first strategy uses K-Means assuming the availability of the geographical positions of the stations, while the second relaxes that assumption by using spectral clustering on Received Signal Strength Indicator (RSSI) information. Extensive simulations indicate that the proposed strategies significantly outperform the standard grouping used in IEEE 802.11ah and the Hidden Matrix-based Regrouping (HMR) in terms of collision rate, throughput and delay. O mecanismo de Restricted Access Window (RAW) foi introduzido pelo IEEE 802.11ah para melhorar o desempenho das Redes Sem Fio Densas (ou Dense Wireless Networks, DWN). O RAW restringe o número de estações que podem acessar o canal, separando-as em grupos. Este trabalho propôs duas estratégias de agrupamento para formação dos grupos RAW, com o objetivo principal de diminuir os terminais ocultos dentro de cada grupo, aumentando o desempenho da DWN. A primeira estratégia utiliza o K-Means assumindo a disponibilidade da posição geográfica das estações, enquanto a segunda relaxa esta hipótese e utiliza o agrupamento espectral sobre a informação de Received Signal Strength Indicator (RSSI). Simulações extensivas indicam que as estratégias propostas superam significativamente o agrupamento padrão do IEEE 802.11ah e o algoritmo Hidden Matrix-based Regrouping (HMR) em termos de taxa de colisão, vazão e atraso.