Internet of Things in aquaculture

Internet of Things in aquaculture: A review of the challenges and potential solutions based on current and future trends

previous arrow
next arrow

In aquaculture, it is important to evaluate and address the challenges related to Internet of Things (IoT). Here is a review that categorizes the challenges associated with implementing IoT systems in aquaculture according to the literature and provides some potential solutions based on trends.

In aquaculture, keeping water quality at the desired level, providing proper nutrition, promoting breeding, and preventing diseases and predators are the main factors that result in successful farming. Aquaculture production enjoyed a growth rate of at least three percent during 2011-2019.

This is due to the growing trend of the world population and limited wild aquatic resources. However, there are still many challenges in aquaculture that need to be addressed. Maximizing the yield via efficient use of resources is one of the main challenges that could be addressed by precision aquaculture.

“Precision aquaculture tries to achieve its goal, i.e. ensuring profitability, sustainability and protection of the environment, by incorporating different innovative technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) in aquaculture.”

In other words, this type of aquaculture converts traditional experienced based practices to knowledge based with the aid of state-of-the-art technologies.

Smart aquaculture, also known as intelligent aquaculture or digital aquaculture, is a concept that involves the use of advanced technologies and data-driven approaches to improve the efficiency, sustainability, and productivity of aquaculture operations.

Actuators and sensors are placed in the physical environment so that they can either measure different water parameters or affect the environment in a specific manner. A gateway acts as a transceiver of data between sensor/actuator nodes and other parts of the IoT system with the help of a gateway. IoT devices are usually connected to a Wireless Sensor Network (WSN) by which all network communication happens.

An example of a WSN can be seen in Figure 1, highlighted and tagged in light blue color, by combining sensors, actuators, and a gateway. This article presents a summary from a paper that introduces a broad categorization of reported challenges in literature and then seeks possible solutions for these challenges.

Internet of Things in aquaculture

Challenges of IoT in Aquaculture

Using literature as a guide, were identified two major categories of challenges. The first category (Common challenges) includes general problems that may occur for most IoT systems including aquaculture.

The second category (Specific Challenges) covers specific challenges raised by different aspects of aquaculture, such as scarcity of electricity, inadequate communication coverage, harsh environment, and inadequate technology.

Common challenges

The permanent contact of sensors with water, which is usually polluted, reduces the accuracy of readings over time. To overcome this issue, the literature either suggests regular maintenance, i.e., cleaning, of the probes or in some cases special mechanism was developed for automatic probe cleaning.

“The incorporation of a sensor cleaning mechanism increases the overall cost of any IoT solution. However, it assures accurate reading of the need for manual maintenance.”

The design of the cleaning system must be in such a way that does not affect sensor readings. In an effort for cleaning sensors, a mechanism was designed that disseminates air bubbles around sensors to prevent dirt on the surface. Such solutions might cause inaccurate readings of dissolved oxygen due to the high concentrations of oxygen molecules around the probe.

A self-cleaning mechanism was designed to clean sensor’s probe after every measurement. This was done by designing a special robotic arm that automatically performs the measurement, rinsing the sensor probe. The proposed system submerges the sensor in the protective solution if it is not going to be used for long time.

Specific Challenges

Depending on the species and accessibility of farm, certain challenges occur more frequently in different farming setups. Table 1, lists challenges for implementing IoT in aquaculture systems over two dimensions. Rows are referring to the farm’s accessibility to basic facilities and columns refer to the type of species under culture.

Internet of Things in aquaculture

A farm with no access to electricity or internet access is considered to be in a rural area. Whereas farms in urban areas have access to both of these facilities. A farm with basic facilities would face fewer challenges in comparison to a farm that is located in a rural area. Likewise, sensitive species demand higher reliability and quality in design, hardware, and software.

Contrary to this, simpler and more economical IoT systems could be designed for species that are less sensitive. Such Categorization resulted in four different scenarios in which any given farm falls in one of them.

“There are different challenges related to each scenario that are grouped into three different categories including infrastructure related challenges, data related challenges, and perception related challenges.”

The higher is the number of challenges, the higher is implementation cost of the IoT system. It is due to the number of challenges that need to be resolved. As it could be seen, most of the challenges are for the farms with sensitive species that are located in rural area. Resolving the challenges could be done by taking one of the following approaches.

Infrastructure Related Challenges

Access of the farm to the electricity and internet are two main infrastructural issues that can affect the design of IoT systems.


The lack of electricity in rural areas or marine aquaculture and the unreliability of electricity in urban area has been recognized as one the challenges that need to be addressed. Most IoT devices are designed in such a way as to operate on low power, meaning that they can operate for a reasonably long time on batteries.

Solar panels and photovoltaic batteries have been shown to be a realistic solution that addresses this issue. However, a reliable operation of other hardware equipment using solar energy demand careful design. For example, reliable powering of aerator for fish farm using solar photovoltaic and the battery has shown to be dependent on the size of the pond.

“It is worth mentioning that, while the incorporation of solar panels and batteries would add to the overall costs of the farm it has shown to be more cost effective than a gasoline-based solution or even electricity for the power grid.”

Most of the proposed solutions in the literature are based on online monitoring of water quality. Continuous monitoring of water quality consumes more power and therefore, optimizing power consumption by reducing sampling periods to as low as possible would result in maximizing the battery life span.

Using the current technology IoT nodes could go to sleep mode by which power consumption reduces effectively. Though periodical sampling would reduce the number of readings, most of the time incorporation of the machine learning methods would result in predictions that are comparable with actual reading.


Internet access enables the IoT system to transfer data to the cloud and thus it could be analyzed and monitored from anywhere. Different networking protocols could be used for transceiving data from/to the farm to/from the internet. However, it has to be noted that the long-range networking protocols are designed for low-rate data transfer.

“As a result, while most of the requirements of IoT aquafarm will be covered, special requirements like transferring video data would not be possible using most of these technologies. Authors asserted that due to the nature of these networks it is impossible to transfer video over them.”

However, there are other protocols like CoAP that can transfer high-rate data including video. Recent technological advancements show that globally accessible internet is no more a dream. This technology, i.e. reliable high-speed internet, has been available in some parts of the world for the past few years and is expected to be expanded to more locations in near future.

Internet of Things in aquaculture

However, just like any other new technology early adoption of this technology would be costly. Sharing is a potential strategy that can be considered by small farms in rural locations to reduce adoption costs. In recent years, edge computing has enabled most computations to be performed on-site. Edge computing could increase Quality of Service (QoS), response time, and security.

“Moreover, when no additional offsite data is required for computing it reduces the dependency of the IoT system on to the cloud. In other words, the implementation IoT system by considering edge computing enables the whole system to act without reliance on cloud resources.”

Another common problem, more relevant to offshore farms, is known as the cable breakage problem of underwater sensor networks. Optimizing the wireless network and proposing a mixed wireless-wired topology have shown to resolve the problem effectively.

Data Related Challenges

The quality of measured data is very important as the whole purpose of an IoT system is to facilitate datadriven decision making. Many factors affect the quality of data along the way starting from the source of data to the quality of sensors that is of high importance for collecting high quality data.

Device Error. Different vendors provide different made of the same sensor, suitable for different environments ranging from hobby usage to industrial use. Moreover, different sensors have different qualities that generally will be mentioned in terms of error rate for each sensor.

The lower is the error the higher is accuracy (quality). The accuracy of the sensors is in direct relation to their price. There is a direct relationship between the price and the accuracy of a temperature sensor.

Another source of data inaccuracy is related to the fact that some sensors may demand regular maintenance which could be in the form of performing regular procedures recommended by manufacturers or changing consumable parts. For example, pH sensors need to be recalibrated from time to time; most sensors require routine cleaning.

Internet of Things in aquaculture

This is due to multiple reasons such as biofouling that cause drift in sensor accuracy. A method was proposed for automatic cleaning of the sensor head by air through an air pump. Nevertheless, this method demands energy for powering the air pump and also does not apply to all environments.

While sensors with higher prices offer higher accuracy, the chance of receiving erroneous data increases over time. Therefore, the incorporation of the following methods, besides performing maintenance procedures, for correcting the sensor readings has shown to be effective.

✓ Incorporating the data fusion technique to improve data quality and also reduce the frequency of data transmission over the network and thus saving energy.

✓ Incorporating anomaly detection and other intelligent algorithms to improve and predict sensor values.

In a multi-depth temperature sensor network, 15 marine buoys were placed in different locations in the sea. The proposed system consisted of two software modules namely the accumulation system, and the visualization system. Sensor nodes send the temperature data by emailing them to a mail server.

Data of each node then will be stored in the text format in a separate file that will be used later for visualization purposes. Interpolation was used to calculate the temperature for both missing sensor data and the predicting temperature value in the location between sensors.

Network Error. Lossy networks affect the smooth collection of data. Most of the IoT platforms store collected data as time series. Losing data at a different time could affect the overall computation. Packet loss in networks is a well-known topic of research where the transition of data packets fails due to many reasons such as often poor received signal strength indicator (RSSI) values and signal-to-noise ratios (SNR).

Resending the same reading multiple times could decrease the chance of data loss. However, different policies could be taken into account to overcome this issue such as interpolation or other statistical methods, utilizing more reliable radio and antenna configuration, or even deployment of an IoT platform on a local server as a solution that removes the need for internet connection.

Perception Related Challenges

According to an empirical study, the general perception of small business farmers was that IoT is not useful in aquaculture. Lack of confrontation of the farmers with successful exemplary farms was found as the main reason for this finding.

Many authors, suggested that policy makers should come up with pilot farms equipped with IoT facilities and show the effectiveness of it to the farmer empirically which may ease the adoption by farmers. The cost of the IoT systems has also been an adoption barrier for small business aquacultures.

While incorporating industrial grade sensors should not be missed if possible, it has been shown that it is possible to implement practical and reliable IoT systems using low cost sensors as well. The economical design of the IoT system could also reduce costs without losing quality.

For example, combining the gateway with a node eliminated the need for a specific gateway node and therefore the cheaper system was offered.

Future Trends

Some potential future trends that may help tackling the challenges of IoT for aquaculture are discussed with regards to the type of the challenges categorized as follows:

Common challenges. Development of antifouling methods is an active research field in literature where it’s outcome could be used for sensors in aquaculture. These methods are more focusing on use of chemical and Nanocomposite technology for reducing accumulation of biofilm on the surface of the sensors and other equipment in direct contact to water.

Specific challenges. The specific challenges are subject to the aquaculture environment and setup, i.e. type of species and location of the farm. Therefore, not all of the IoT systems in aquaculture are expected to face the related challenges in this category.

Nevertheless, advent of energy harvesting and renewable energy technologies such as solar, wind, wave etc. are expected to alleviate some of the challenges related to the energy and electricity of the farm located in rural area. While utilization of floating solar panel and wind turbine are a feasible common evolving practice, application of newer sources of energy such as hydrogen has also shown promising applicability.

Advancement in battery production industry could also affect the challenges in IoT systems for aquaculture. Data fusion and sensor fusion are also active research fields that could be practiced for tackling both network and data errors and increasing accuracy and reliability of data.


The application of IoT is spread out in almost all industries. Aquaculture as a subfield of agriculture is not an exception. While there are many challenges related to the IoT in general, some of the challenges are related to aquaculture. The present article is a review of reported challenges regarding the incorporation of IoT systems in aquaculture.

Furthermore, possible solutions that could resolve reported challenges were discussed.

This is a summarized version developed by the editorial team of Aquaculture Magazine based on the review article titled “INTERNET OF THINGS IN AQUACULTURE: A REVIEW OF THE CHALLENGES AND POTENTIAL SOLUTIONS BASED ON CURRENT AND FUTURE TRENDS” developed by: HAJAR RASTEGARI- University Malaysia Terengganu, FARHAD NADI – University Malaysia Terengganu and Institute of Tropical Aquaculture and Fisheries, SU SHIUNG LAM, MHD IKHWANUDDIN AND NOR AZMAN KASAN – University Malaysia Terengganu, ROMI FADILLAH RAHMAT – Universitas Sumatera Utara, WAN ADIBAH WAN MAHARI -University Malaysia Terengganu.
The original article was published on MES, AÑO, through SMART AGRICULTURAL TECHNOLOGY.
The full version, including tables and figures, can be accessed online through this link:

previous arrow
next arrow

Leave a comment

Tu dirección de correo electrónico no será publicada. Los campos obligatorios están marcados con *