Implementasi Protokol Routing Directed Diffusion pada WSN dengan Modul Komunikasi LoRa - Rafif Nurmanda Ghafurutama; Agung Setia Budi; Wijaya Kurniawan (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2020)
Routing is the core of an infrastructure network that regulates how the path of sending a packet from one node to another node, the routing network is divided into 2 namely cable networks and wireless networks. One of the routing protocols of wireless networks that have a flat topology is Directed Diffusion (DD), Directed Diffusion (DD) is a routing mechanism in WSN where the sink node will request the data source on the source node by sending an interest then the source node will search the best route for sending data from the source node to the sink node. To do wireless network routing, a wireless communication module is needed, one of the wireless communication modules is LORA SX1278. In previous studies managed to make a comparison between the Directed Diffusion routing algorithm and Geographic Adaptive Fidelity which proves that Directed Diffusion routing is better when it is simulated on network simulation software. Therefore in this study the writer implements the Directed Diffusion routing algorithm and tries to develop it using the LORA communication module. In this study there are 6 nodes contained LORA SX1278 module, where 1 functions as a sink node, 1 functions as a source node, and 4 functions as a neighbor. In this study the authors did not maximize the range of LORA distances due to the lack of testing sites during the COVID-19 pandemic. The purpose of this study is to analyze the results of the Directed Diffusion routing algorithm which is the RSSI value and the routing path obtained. In this test the author tests the routing algorithm by providing data transmission variations of 8 bytes, 16 bytes, 32 bytes, and 64 bytes. The results of the tests obtained show that the RSSI values ​​obtained dynamically from -118dB to -72dB and the routing paths obtained are not affected by changing data variations.