With the rapid increase in the human population and urbanization worldwide, the demand for food production has played a significant role in driving the integration of technology into agriculture. Various Cloud-based systems, such as livestock tracking systems, have been proposed. In those systems, data were collected by the sensors and sent to the Cloud for processing. However, significant issues with those systems were noted, such as high bandwidth utilization and security concerns, such as a high volume of row data traveling from the data collection devices (such as sensors) to the Cloud through the Internet. Additionally, the long distance between the Cloud and the data collection devices makes it unsuitable for latency-sensitive livestock disease monitoring and tracking systems. Therefore, this paper proposes a Fog–Cloud-based approach, where the processing is conducted at the Fog layer, closer to the data collection devices, and only the result is sent to the Cloud for remote viewing. The proposed method aims to reduce power consumption and latency in communication. To validate the proposed method, both the Cloud-based and Fog–Cloud-based scenarios are simulated using iFogSim (a novel simulation tool for IoT and Cloud computing), and the result shows that there is less than twice the power consumption in some scenarios and that the time consumed in the proposed Fog–Cloud-based system, depending on the number of sensors, is five to ten times lower. This study further supports the point that the Fog–Cloud-based is suitable for latency-dependent farming systems such as livestock tracking systems.

Livestock theft is a significant threat to the agricultural industry, necessitating innovative preventive strategies. This study proposes a Wireless Livestock Tracking System (WLTS) that uses real-time Internet of Things (IoT) technologies to prevent livestock theft. The WLTS integrates GPS sensors with Long Range Radio (LoRa) wireless communication modules, overcoming the limitations of Wi-Fi and Bluetooth-based systems. It uses a single LoRa network receiver to facilitate real-time communication between farmers and their livestock. Simulation results show the WLTS effectively mitigates livestock theft, enabling farmers to quickly identify and recover stolen animals. Geofencing alerts enhance the system's sensitivity to potential theft scenarios. The WLTS has a user-friendly interface, allowing farmers to remotely monitor their livestock. Data analytics capabilities enable predictive analysis of probable theft trends based on historical data. The findings pave the way for practical implementation, revolutionizing livestock protection and safeguarding farmers' livelihoods worldwide.

The Internet of Things (IoT) is a futuristic technology that promises to connect tons of devices via the internet. As more individuals connect to the internet, it is believed that communication will generate mountains of data. IoT is currently leveraging Wireless Sensor Networks (WSNs) to collect, monitor, and transmit data and sensitive data across wireless networks using sensor nodes. WSNs encounter a variety of threats posed by attackers, including unauthorized access and data security. Especially in the context of the Internet of Things, where small embedded devices with limited computational capabilities, such as sensor nodes, are expected to connect to a larger network. As a result, WSNs are vulnerable to a variety of attacks. Furthermore, implementing security is time-consuming and selective, as traditional security algorithms degrade network performance due to their computational complexity and inherent delays. This paper describes an encryption algorithm that combines the Secure IoT (SIT) algorithm with the Security Protocols for Sensor Networks (SPINS) security protocol to create the Lightweight Security Algorithm (LSA), which addresses data security concerns while reducing power consumption in WSNs without sacrificing performance.