{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:06:07Z","timestamp":1790075167793,"version":"4.0.1"},"reference-count":47,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Research"},{"name":"Italian National Recovery and Resilience Plan","award":["CUPE83C22004640001"],"award-info":[{"award-number":["CUPE83C22004640001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deploying artificial intelligence (AI) for wildlife monitoring in remote environments remains challenging due to limited energy, computational resources, and unreliable communication. Conventional edge AI systems rely on continuous sensing, high-power communication, and full-precision models, resulting in excessive energy consumption. This paper presents a green-energy-based hierarchical network architecture for sustainable wildlife monitoring at the extreme edge. The framework distributes sensing, communication, and AI inference across edge, far-edge, and extreme-edge layers. A low-power IEEE 802.11ah network with on-demand channel allocation reduces transmission overhead, while passive infrared (PIR)-based event-driven activation replaces continuous camera operation. Model quantization (FP32, FP16, INT8) and distributed split inference improve computational efficiency, and tracking-assisted communication eliminates redundant transmissions. Experimental results demonstrate significant sustainability improvements. PIR-based operation reduces daily energy consumption by 95.11% compared with continuous operation. Tracking-assisted communication reduces both bandwidth requirements and energy consumption by more than 99% (p&lt;0.001). Token-based channel allocation provides 39.4% energy savings compared with Target Wake Time (TWT)-based scheduling (sensitivity range: 29.2\u201345.1%). INT8 quantization delivers 4.7\u00d7 higher throughput, 61% lower memory usage, and 39% lower power consumption, with a 2.6% mean average precision (mAP) reduction. Field validation confirms the INT8 model\u2019s effectiveness with an overall mAP of 82.89% (86.40% daytime, 79.37% nighttime) and precision above 96%. Combined with PIR activation, the INT8 system achieves 63.76 Wh\/day\u2014a 95.17% reduction versus FP32 continuous operation. The integration of renewable energy harvesting, efficient sensing, lightweight AI, and communication optimization enables scalable, autonomous, and sustainable wildlife monitoring in resource-constrained environments.<\/jats:p>","DOI":"10.3390\/s26185942","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:21:48Z","timestamp":1790072508000},"page":"5942","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sustainable Hierarchical Networking for Extreme-Edge AI-Based Wildlife Monitoring in Resource-Constrained Environments"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7252-5373","authenticated-orcid":false,"given":"Md Auhidur","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy"},{"name":"Software Engineering Program, Institute of Information Technology, Noakhali Science and Technology University, Noakhali 3814, Chattogram, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"given":"Ervisa","family":"Marku","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, Via G. 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