RANCANG BANGUN SISTEM MONITORING KESEHATAN DOMBA BERBASIS IoT

Internet pada dewasa ini telah menjadi bagian tak terpisahkan dari masyarakat modern, dengan Indonesia mengalami peningkatan yang signifikan dalam pengguna internet dan koneksi seluler. Untuk pemanfaatkan adopsi yang meluas ini, penelitian ini mengusulkan sistem pemantauan kesehatan domba berbasis IoT untuk mendukung pengelolaan ternak, khususnya di peternakan ruminansia kecil. Dengan terus memantau parameter fisiologis utama seperti suhu tubuh, denyut nadi, dan laju pernapasan, sistem ini bertujuan membantu peternak dalam mencegah penyebaran penyakit dan kerugian finansial. Penelitian ini menggunakan metode Research and Development yang meliputi tahapan pengembangan sistem mulai dari analisis kebutuhan hingga implementasi, pengujian, dan evaluasi. Komponen penting, termasuk mikrokontroler ESP32, sensor suhu MLX90615 IR, Pulse Sensor dari PulseSensor.com, dan sensor tekanan udara MPS20N0040D-S, menunjukkan tingkat akurasi di atas 90% dengan kesalahan kurang dari 10% selama pengujian perangkat keras. Sistem pemantauan kesehatan domba berbasis IoT terintegrasi dengan platform ThingSpeak, menyediakan pengumpulan, analisis, dan visualisasi data secara real-time. Antarmuka pengguna menawarkan dasbor untuk pemilik peternakan dan petugas veteriner untuk mengakses dan melacak data kesehatan, menerima pemberitahuan peringatan untuk pembacaan yang tidak normal, dan memastikan intervensi yang cepat untuk menjaga kesejahteraan dan produktivitas domba. Dengan teknologi IoT dan platform ThingSpeak, sistem ini mengatasi tantangan dalam pemantauan kesehatan ternak yang dihadapi peternak skala kecil. Dengan potensi untuk meningkatkan produktivitas ternak, kesejahteraan, dan pencegahan penyakit. Solusi inovatif ini berkontribusi untuk mencapai tujuan pembangunan berkelanjutan, seperti ketahanan pangan dan pengentasan kemiskinan, yang digariskan oleh Perserikatan Bangsa-Bangsa (PBB). The Internet of Things (IoT) has become an integral part of modern society, with Indonesia experiencing a significant rise in internet users and cellular connections. Leveraging this widespread adoption, this research proposes an IoT-based sheep health monitoring system to support livestock management, specifically in small ruminant farms. By continuously monitoring key physiological parameters such as body temperature, pulse, and respiratory rate, this system aims to aid breeders in preventing disease spread and financial losses. The study employs the Research and Development method, encompassing system development stages from needs analysis to implementation, testing, and evaluation. Critical components, including the ESP32 microcontroller, MLX90615 IR temperature sensor, Pulse Sensor from PulseSensor.com, and MPS20N0040D-S air pressure sensor, exhibit an accuracy rate above 90% with an error of less than 10% during hardware testing. The IoT-based sheep health monitoring system integrates with the ThingSpeak platform, providing real-time data collection, analysis, and visualization. The user interface offers a dashboard for farm owners and veterinary officers to access and track health data, receive alert notifications for abnormal readings, and ensure prompt interventions to maintain sheep well-being and productivity. By embracing IoT technology and leveraging the ThingSpeak platform, this system addresses the challenges in livestock health monitoring faced by small-scale breeders. With the potential to improve livestock productivity, welfare, and disease prevention, this innovative solution contributes to achieving sustainable development goals, such as food security and poverty alleviation, outlined by the United Nations (UN)

Cross-layer Framework for Energy Harvesting-LPWAN Resource Management based on Fuzzy Cognitive Maps and Adaptive Glowworm Swarm Optimization for Smart Forest

Modern forestry research and management increasingly rely on precise environmental data. Presently, Low Power Wide Area Networks (LPWANs) offer potential advantages for such field monitoring tasks. However, their applicability requires enhancements in aspects such as power consumption, transmission range, data rate, and consistent quality of service. This paper introduces a novel control model emphasizing cross-layer collaboration, aiming to bolster the efficiency and reliability of Energy Harvesting (EH) LPWANs within the context of intelligent forest management. By employing the influential factors of EH-LPWAN as conceptual nodes, an innovative fuzzy cognitive map (FCM) can be designed. The interrelations among these concepts become instrumental in developing the cross-layer optimization model, addressing various objectives and tackling overlapping constraints. To further refine the model’s efficacy, an adaptive glowworm swarm optimization (AGSO) driven dynamic FCM method is presented to ascertain the conceptual weights while facilitating real-time updates. Preliminary results manifest a noteworthy enhancement in communication range by 40.2%, a betterment in packet delivery accuracy by 19%, and an extension in the LoRaWAN’s projected lifespan by 33.8% during scenarios with diminished EH rates. It’s evident that the energy self-sustainability of EH nodes coupled with the data handling capacity of the entire network fully aligns with the stringent real-time and consistency criteria mandated for meticulous forest observation