AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
An innovative EDSP-QCH (Energy-efficient Dynamic Sub-Station Placement and reward-based Q-Learning reinforcement model for Cluster Head selection) strategy is proposed, which indicates significant improvements in energy efficiency and network robustness.
The results indicate that E2CMR improves energy efficiency, network stability, and routing performance and is applicable for large-scale energy-constrained WSNs.
WSNs continue to struggle with the issue of energy usage, network lifetime, and dependable data transfer, especially in heterogeneous networks where nodes have diverse computational and energy resources. The classical clustering algorithms are usually characterized by uneven distribution of cluster-heads, inefficient routing paths, and poor responsiveness to changes in network dynamics. This article presents a new framework, OEEMLCR (Optimized Energy-Efficient Machine Learning-Based Clustering and Routing), which is a combination of Particle Swarm Optimization-based K-means clustering and Coati Optimization Algorithm-based routing and Q-learning adaptation. The suggested methodology fills in crucial gaps through the use of a dual-objective fitness function that both maximizes the compactness of space and the homogeneity of energy when forming clusters and introduces the learning technique of reinforcement to allow adaptive routing behavior under the influence of experience gained in the network. Experimental validation of heterogeneous network situations show that OEEMLCR can attain significant gains over existing protocols: network lifetime is increased 13.6% over baseline machine learning methods, cumulative packet delivery is increased 108%, energy efficiency is increased 340%. The framework operates in a stable way during 1000 rounds of the simulation and is 96.7 percent of the initial network power, far outperforming LEACH, DMHT, EDMHT, and EEMLCR in lifetime, throughput, percentage ratio of delivering packets, and the network resilience criterion.
Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, healthcare, industrial automation, and Internet of Things (IoT) applications. However, limited energy resources and hotspot formation near the base station significantly reduce network lifetime and communication efficiency. This study proposes a Hotspot-Aware Load-Balanced Clustering and Routing Protocol (HALBCRP) to improve energy utilization and mitigate hotspot formation in WSNs. The proposed protocol integrates energy-aware cluster formation, hotspot-aware cluster head selection, dynamic load balancing, and fault-tolerant multi-hop routing into a unified framework. Sensor nodes are grouped using an energy-distance-based clustering strategy, while cluster heads are selected based on residual energy, connectivity, traffic load, and proximity to the base station. To prevent energy holes and traffic congestion, overloaded cluster heads dynamically redistribute traffic to neighboring cluster heads. The performance of HALBCRP was evaluated using MATLAB and compared with ITSA-UCHSE, HHDAP, Q-DAEER, and IPSO based on energy consumption, average throughput, number of alive nodes, number of dead nodes, and network lifetime. Simulation results show that HALBCRP achieves lower energy consumption (0.605–1.053 J), maintains a higher number of alive nodes throughout the simulation, and attains an average throughput of 28.62 packets per round. Furthermore, the protocol records superior network lifetime values of FND = 800 rounds, HND = 1150 rounds, and LND = 1340 rounds. These results demonstrate that HALBCRP effectively mitigates hotspot formation, balances network load, and significantly extends network lifetime compared with existing approaches.
The core research goal of this paper is to optimize dynamic routing protocols to improve the performance of the classic LEACH protocol in heterogeneous WSNs through a real-time adaptive scheme, which relies on two core methods: a cluster head selection mechanism based on the residual energy criterion, and a priority hop count strategy.
ThGCDTR-RP is proposed, an energy-efficient clustering and routing protocol that integrates Grey Wolf Optimizer, Cheetah Optimizer, and Differential Evolution for cluster-head (CH) selection that consistently outperforms LEACH, LPSO, LGWO, WOA-P, and LACO.
A novel cluster-based routing protocol that integrates a Fungal Growth Optimizer for adaptive cluster head (CH) selection and a Graph Neural Network for inter-cluster routing, which demonstrates FGOGNN’s potential for deployment in real-time WSN applications, where energy efficiency and dynamic adaptability are paramount.
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