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Routing as a Bayesian Coalition Game in Smart Grid Neighborhood Area Networks: Learning Automata-based approach

机译：作为智能电网邻域网络中的贝叶斯联盟博弈的路由：学习基于自动机的方法

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Routing issues in the existing Smart Grid (SG) literature are focused on Home Area Networks (HANs), Neighborhood Area Networks (NANs), or Wide Area Networks (WANs). Among these, routing in NANs is the most challenging as it entails construction and maintenance of backhaul having various Mesh Routers (MRs). Wireless networks are generally used for communication between backhaul and centralized controller for power distribution. This triggers increased chances of congestion due to scarce resources of available bandwidth and number of channels. Keeping in view of the same, in this paper, we propose a new Efficient Routing Scheme (ERS) as a Bayesian Coalition Game (BCG). The solution strategy integrates the concepts of Learning Automata (LA) in NANs. LA are assumed to be the players in the game, which are deployed at the MRs in NANs. Coalition among the players of the game is scaffolded upon the concepts of Bayesian Networks. Each player in the game is allowed to move from one coalition to another depending upon the payoff function. Corresponding to each move of the player in the game, its action may be rewarded or penalized from the environment. Based upon reward/penalty from the environment, each player updates its action probability vector. The proposed scheme is evaluated with respect to various performance evalaution metrics such as load utilization factor, user satisfaction levels, delay and probability of transmission.
机译：现有智能电网（SG）文献中的路由问题主要集中在家庭局域网（HAN），邻域局域网（NAN）或广域网（WAN）上。其中，NAN中的路由最具挑战性，因为它需要具有各种Mesh路由器（MR）的回程的构建和维护。无线网络通常用于回程和集中控制器之间的通信，以进行功率分配。由于可用带宽和信道数量的稀缺资源，这会导致拥塞的机会增加。考虑到这一点，在本文中，我们提出了一种新的有效路由方案（ERS）作为贝叶斯联盟博弈（BCG）。解决方案策略整合了NAN中的学习自动机（LA）的概念。假定洛杉矶是游戏中的玩家，这些玩家部署在NAN中的MR处。贝叶斯网络的概念支持游戏玩家之间的联盟。根据收益功能，游戏中的每个玩家都可以从一个联盟移动到另一个联盟。与玩家在游戏中的每一个动作相对应，其行为可能会受到环境的奖励或惩罚。基于来自环境的奖励/惩罚，每个玩家更新其动作概率向量。针对各种性能回避指标（如负载利用率，用户满意度，延迟和传输概率）对提出的方案进行了评估。

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