HBMFTEFR: Design of a Hybrid Bioinspired Model for Fault-Tolerant Energy Harvesting Networks via Fuzzy Rule Checks

Designing energy harvesting networks requires modelling of energy distribution under different real-time network conditions. These networks showcase better energy efficiency, but are affected by internal & external faults, which increase energy consumption of affected nodes. Due to this probability of node failure, and network failure increases, which reduces QoS (Quality of Service) for the network deployment. To overcome this issue, various fault tolerance & mitigation models are proposed by researchers, but these models require large training datasets & real-time samples for efficient operation. This increases computational complexity, storage cost & end-to-end processing delay of the network, which reduces its QoS performance under real-time use cases. To mitigate these issues, this text proposes design of a hybrid bioinspired model for fault-tolerant energy harvesting networks via fuzzy rule checks. The proposed model initially uses a Genetic Algorithm (GA) to cluster nodes depending upon their residual energy & distance metrics. Clustered nodes are processed via Particle Swarm Optimization (PSO) that assists in deploying a fault-tolerant & energy-harvesting process. The PSO model is further augmented via use of a hybrid Ant Colony Optimization (ACO) Model with Teacher Learner Based Optimization (TLBO), which assists in value-based fault prediction & mitigation operations. All bioinspired models are trained-once during initial network deployment, and then evaluated subsequently for each communication request. After a pre-set number of communications are done, the model re-evaluates average QoS performance, and incrementally reconfigures selected solutions. Due to this incremental tuning, the model is observed to consume lower energy, and showcases lower complexity when compared with other state-of-the-art models. Upon evaluation it was observed that the proposed model showcases 15.4% lower energy consumption, 8.5% faster communication response, 9.2% better throughput, and 1.5% better packet delivery ratio (PDR), when compared with recently proposed energy harvesting models. The proposed model also showcased better fault prediction & mitigation performance when compared with its counterparts, thereby making it useful for a wide variety of real-time network deployments

Distance-layer structure of the De Bruijn and Kautz digraphs: analysis and application to deflection routing

This is the peer reviewed version of the following article: Fàbrega, J.; Martí, J.; Muñoz, X. Distance-layer structure of the De Bruijn and Kautz digraphs: analysis and application to deflection routing. "Networks", 29 Juliol 2023, which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/net.22177. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.In this article, we present a detailed study of the reach distance-layer structure of the De Bruijn and Kautz digraphs, and we apply our analysis to the performance evaluation of deflection routing in De Bruijn and Kautz networks. Concerning the distance-layer structure, we provide explicit polynomial expressions, in terms of the degree of the digraph, for the cardinalities of some relevant sets of this structure. Regarding the application to defection routing, and as a consequence of our polynomial description of the distance-layer structure, we formulate explicit expressions, in terms of the degree of the digraph, for some probabilities of interest in the analysis of this type of routing. De Bruijn and Kautz digraphs are fundamental examples of digraphs on alphabet and iterated line digraphs. If the topology of the network under consideration corresponds to a digraph of this type, we can perform, in principle, a similar vertex layer description.Partially supported by the Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación, Spain, and the European Regional Development Fund under project PGC2018-095471-B-I00; and by AGAUR from the Catalan Government under project 2017SGR-1087.Peer ReviewedPostprint (author's final draft