A fair and distributed congestion control mechanism for smart grid neighborhood area networks

Abstract

The need for significant improvements in the management and efficient use of electrical energy has led to the evolution from the traditional electrical infrastructures towards modern Smart Grid networks. Taking into account the critical importance of this type of networks, multiple research groups focus their work on issues related to the generation, transport and consumption of electrical energy. One of the key research points is the data communication network associated with the electricity transport infrastructure, and specifically the network that interconnects the devices in consumers' homes, the so-called Neighborhood Area Networks (NANs). In this paper, a new fair and distributed congestion control mechanism for NANs is proposed, implemented and evaluated. The main goal of this mechanism is to provide fairness in the access to the network, thus avoiding that some network nodes monopolize the use of the channels due to their higher traffic generation rate, or to their geographical position. Besides, different priorities have been considered for the traffic flows transmitted by different applications. The goal here is to provide the needed Quality of Service (QoS) to all traffic flows, especially when the traffic load is high. The proposal is evaluated in the context of a wireless ad hoc network composed by a set of smart grid meter devices. Applying our proposed congestion control mechanism leads to performance improvements in terms of packet delivery ratio, network throughput fairness between different traffic sources, packet network transit time and QoS provision.

Intelligent Secured Traffic Optimization Model for Urban Sensing Applications With Software Defined Network

“…Moreover, blockchain provides decentralized attack detection to address the "single point of failure" issue. In [36], authors proposed and analyzed a novel fair and distributed congestion control strategy for Neighborhood Area Networks (NANs). The major goal is to ensure fair network access by preventing hightraffic nodes from controlling channels.…”

A Reinforcement Learning Congestion Control Algorithm for Smart Grid Networks

“…In the smart grid context, solutions for static-node wireless multi-hop networks have been discussed in [2], [3]. The first paper addresses potential emergency scenarios where relay nodes are tasked with taking action in situations of network congestion.…”

“…However, that work does not consider equal distribution of network resources, and the congestion control mechanism requires several available wireless channels, one for control traffic and one to four wireless channels for data traffic. The second study [3] highlights the fair allocation of network resources through the application of congestion control at the source nodes and compares their approach with the enhanced distributed channel access (EDCA) mechanism. The authors emphasize that the EDCA mechanism operates locally.…”

“…Expanding on the concept of employing machine learning for congestion control, [6] showcases a method designed for multi-hop wireless networks. This method utilizes the CatBoost algorithm (which is also based on decision trees) to predict the success of packet transmissions depending on the network load.…”

“…Without a feedback mechanism during network congestion, UDP can cause network performance degradation. Several solutions have been proposed to solve this problem, some based on machine learning techniques [1]- [3], [6]. Unlike supervised learning, which requires a time-consuming process of collecting labeled data sets, reinforcement learning (RL) offers a notable advantage.…”