A Dynamic Leader Election Algorithm for Decentralized Networks

Leader election algorithms play an important role in orchestrating different processes on distributed systems, including next-generation transportation systems. This leader election phase is usually triggered after the leader has failed and has a high overhead in performance and state recovery. Further, these algorithms are not generally applicable to cloud-based native microservices-based applications where the resources available to the group and resources participating in a group continuously change and the current leader may exit the system with prior knowledge of the exit. Our proposed algorithm, the dynamic leader selection algorithm, provides several benefits through selection (not, election) of a set of future leaders which are then alerted prior to the failure of the current leadership and handed over the leadership. A specific illustration of this algorithm is provided with reference to a peer-to-peer distribution of autonomous cars in a 5G architecture for transportation networks. The proposed algorithm increases the efficiencies of applications that use the leader election algorithm and finds broad applicability in microservices-based applications.

Optimal PV array reconfiguration under partial shading condition through dynamic leader based collective intelligence

This paper applies the innovative idea of DLCI to PV array reconfiguration under various PSCs to capture the maxi-mum output power of a PV generation system.DLCI is a hybrid algorithm that integrates multiple meta-heuristic algo-rithms.Through the competition and cooperation of the search mechanisms of different metaheuristic algorithms,the local exploration and global development of the algorithm can be effectively improved to avoid power mismatch of the PV system caused by the algorithm falling into a local optimum.A series of discrete operations are performed on DLCI to solve the discrete optimization problem of PV array reconfiguration.Two structures(DLCI-I and DLCI-II)are designed to verify the effect of increasing the number of sub-optimizers on the optimized performance of DLCI by simulation based on 10 cases of PSCs.The simulation shows that the increase of the number of sub-optimizers only gives a relatively small improvement on the DLCI optimization performance.DLCI has a significant effect on the reduction in the number of power peaks caused by PSC.The PV array-based reconstruction system of DLCI-II is reduced by 4.05%,1.88%,1.68%,0.99%and 3.39%,when compared to the secondary optimization algorithms.