Construction of Internet of Things System in Coastal Aquaculture Environment

Changes in fishing industry resources has long been determined by climate and its fickleness, for changes in climate directly or indirectly result in changes in the oceanic environment. In recent years, the Taiwanese government has devoted its efforts towards developing land-based aquaculture as well as offshore aquaculture. On one hand, land-based aquaculture requires heavy demand for water resources, electricity, and labor; moreover, excessive groundwater withdrawal can cause land subsidence. On the other hand, when it comes to offshore aquaculture, typhoons and the Northeast Monsoon often damage net cages used in cage aquaculture - during a cloudburst, fish in cages bump into each other, which can lead to abrasion, injuries, and even subsequent bacterial diseases. Given the above, crucial issues in fish farming include attempts to lower farming costs and reducing losses and damages. This study utilized a coastal fish farm in the Penghu Islands for experiment and implementation. Penghu is surrounded by sea and promotes solar power generation; hence, by establishing our outdoor fish farm close to the sea, we can withdraw seawater for use in our fish pond's water circulation and subsequently reduce costs for water resources. Additionally, solar power generation significantly reduces electricity costs and can provide power for our system's sensors. Our study relies mainly on the Internet of Things (IoT) to reduce costs and needs in labor, electricity, and water resources. Meanwhile, considering the general phenomenon of ageing population, our proposed system keeps track of all the internal and external environmental factors in each step of the farming process as well as farming indices, and then apply self-learning so that, in the future, fish farming will be freed from the need of manual operations. These indices can help resolve future issues in technology gap. This study is divided into three sections: (1) automated farming and activation of surrounding devices, (2) the monitoring system, and (3) IoT sensor design aspects including (i) base station cluster planning, (ii) adaptability routing table, and (iii) self-updating sensor programs.