Learning automata-based multi-constrained fault-tolerance approach for effective energy management in smart grid communication network

Smart grid networks aim to overcome the inadequacies of the existing electricity grids infrastructure. These networks may exhibit faults due to over-usage of data on electricity grids. Moreover, they must be configured such that energy utilization can be minimized while dealing with energy delivery networks. In order to optimize the performance of smart grids on these two issues of fault tolerance and energy management, a learning automata (LA)-based data transmission path selection algorithm with multiple constraints such as cost, delay, and energy consumption is proposed in this paper. The proposed algorithm named LAMCR is simulated for a real-time environment using NS-2 and is evaluated for QoS parameters such as packet delivery ratio, end-to-end delay, and energy consumption. LAMCR is tested and compared with the legacy systems such as OMCR (Kuipers et al., 2002) and HWMP (Zhu et al., 2011). The results show that LAMCR performs better, while exhibiting higher packet delivery ratio, lesser delay, and lower energy consumption, which improves the throughput of the system.

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