Given the intrinsic low energy and high consumption characteristics of sensor nodes, it is imperative to explore strategies for achieving energy-efficient routing within wireless sensor networks (WSNs). A significant body of existing research on clustered routing algorithms for WSNs has concentrated on employing heuristic optimization algorithms to facilitate the selection of routing paths. However, once the number of sensor nodes or the deployment environment changes, the algorithm’s performance can fluctuate significantly, potentially requiring redesign and retuning. In this article, we propose the clustered routing algorithm based on forwarding mechanism optimization (CRFMO), which defines separate routing rules for intracluster and intercluster communication, providing suitable communication paths for nodes. The algorithm eschews the complex procedure of parameter tuning during the routing path selection process and contributes to expediting WSN deployment and balancing node load pressure, ultimately extending the network’s operational lifespan. Simulation outcomes reveal that, in comparison to LEACH-IACA and IMP-LEACH, the CRFMO algorithm markedly enhances energy distribution balance, equalizes the burden among nodes, sustains high network coverage over an extended period, which enhances the quality of network monitoring, and significantly extends the lifetime of the network.

Wireless sensor network (WSN) is usually deployed in unattended monitoring environments, with limited energy and large-scale characteristics. Therefore, the performance of WSN is susceptible to the influence of external environment and its own energy. The existing routing protocols try to optimize energy efficiency and routing reliability from the perspective of the network itself. However, they do not take into account the environmental impact from the outside, which makes them unable to properly respond to harsh environment. Therefore, a new Environment Fusion Routing Protocol (EFRP) is proposed. Cluster heads are selected in a probabilistic manner according to the ratio of the energy of each node and environmental factors. Nodes with excellent environment have a higher probability of being selected as cluster heads. Besides, the EFRP optimal path is determined according to the comprehensive potential field superimposed by the distance field, residual energy field and environmental field. The best path can be weighed among delay, energy saving and routing invulnerability. Therefore, the balance of network energy is realized and the invulnerability of the network in harsh environment is improved. The experimental results show that EFRP can significantly improve the data transmission and invulnerability of the network in harsh environments.