Arsitektur Edge–Cloud Berbasis AI untuk Optimalisasi Internet of Things Hemat Energi pada Smart City

Muhammad zulfariansyah, Gubtha Mahendra Putra, Muhammad Fawaz Saputra, Imam Muhammad Hakim

Abstract


ABSTRAK 


Konsep Smart City berbasis Internet of Things (IoT) telah banyak diterapkan di kawasan perkotaan dengan dukungan infrastruktur listrik dan jaringan yang memadai. Salah satu pendekatan yang berkembang adalah Sensor-Cloud, yang memadukan sensor, IoT, dan komputasi awan untuk menjamin ketersediaan data waktu nyata di ekosistem kota modern. Namun, pendekatan ini sulit diadaptasi pada kawasan hutan tropis yang memiliki keterbatasan energi listrik, akses jaringan, serta biaya operasional tinggi. Tantangan ini menuntut rancangan arsitektur alternatif yang lebih hemat energi dan sesuai dengan kondisi lapangan.

Penelitian ini bertujuan merumuskan sebuah arsitektur konseptual Edge–Cloud berbasis AI (AI-driven) yang mendukung implementasi Green IoT di kawasan hutan tropis. Mikrokontroler pada smart sensor node dilengkapi algoritma AI ringan (TinyML) untuk melakukan pre-processing berupa filtrasi, deteksi anomali, dan kompresi data. Data yang telah diproses kemudian dikirim ke edge server untuk agregasi dan analisis awal, sebelum diteruskan ke pusat data (cloud). Sistem ini ditenagai oleh Pembangkit Listrik Tenaga Surya (PLTS) sederhana sebagai sumber energi terbarukan untuk menjaga keberlanjutan operasional.

Hasil konseptual menunjukkan bahwa arsitektur ini berpotensi menekan konsumsi energi transmisi, mengurangi beban bandwidth, dan meningkatkan kecepatan respon sistem terhadap peristiwa penting seperti kebakaran hutan atau intrusi. Simpulan dari penelitian ini adalah bahwa kombinasi Edge–Cloud, AI, dan energi surya dapat menjadi solusi strategis untuk mendukung implementasi Smart City yang berkelanjutan di kawasan hutan tropis.


Kata Kunci: Smart City, Edge Computing, AI, Green IoT, TinyML, Pembangkit Listrik Tenaga Surya, Hutan Tropis


ABSTRACT 


The Smart City concept based on the Internet of Things (IoT) has been widely applied in urban areas with sufficient electricity and network infrastructure. One of the developed approaches is the Sensor-Cloud, which integrates sensors, IoT, and cloud computing to ensure the availability of real-time data in modern urban ecosystems. However, this approach is difficult to adapt in tropical forest areas due to limited electricity, network access, and high operational costs. These challenges demand an alternative architecture that is more energy-efficient and suitable for remote environments.

This study aims to formulate a conceptual AI-driven Edge–Cloud architecture to support the implementation of Green IoT in tropical forest regions. Microcontrollers in smart sensor nodes are equipped with lightweight AI algorithms (TinyML) to perform pre-processing such as filtering, anomaly detection, and data compression. The processed data is then sent to the edge server for aggregation and initial analysis before being forwarded to the central cloud. The system is powered by a simple Solar Power Plant (PLTS) as a renewable energy source to ensure sustainable operation.

The conceptual results indicate that this architecture has the potential to reduce transmission energy consumption, lower bandwidth requirements, and improve system responsiveness to critical events such as forest fires or intrusions. The conclusion of this study is that the integration of Edge–Cloud, artificial intelligence, and solar energy can serve as a strategic solution to support sustainable Smart City implementation in tropical forest areas.

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Keyword: Smart City, Edge Computing, Artificial Intelligence, Green IoT, TinyML, Solar Power, Tropical Forest



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References


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Published by:

Fakultas Teknik

Universitas Mulawarman

Jalan Sambaliung No. 9 Sempaja Selatan

Kec. Sempaja, Kota Samarinda, Kalimantan Timur

Kode Post. 75117