## 4 Discussion  
The integration of edge computing and fog computing promises to revolutionize pet care by enhancing the data processing efficiency and speed. Collaboration between edge devices enables data to be processed locally on the device, reducing latency, improving real-time responsiveness, and minimizing energy costs. This is crucial for applications requiring constant monitoring, such as pet health and behavior video surveillance. In these applications, optimal video transmission is essential to ensure a longer device lifespan and to enable more efficient video transmission, processing, and analysis [119]. An activity monitoring device can utilize edge computing to obtain and store physiological data and then provide immediate alerts in case of anomalies (e.g., heart rate and fever). Furthermore, fog computing extends processing to an intermediate layer between the edge and the cloud, achieving a greater analytical and storage capacity without relying entirely on a centralized infrastructure. This technology will improve the management and analysis of large volumes of data from multiple IoT devices to ensure more functionalities (e.g., malnutrition and mood). On the other hand, cloud support is fundamental for the expansion and improvement of IoT solutions for pets. The cloud offers robust and scalable storage for the large volumes of data generated by monitoring devices such as cameras and sensors. Additionally, it facilitates remote access to these data, allowing owners to monitor and manage their pets from anywhere. The ability to perform large-scale data analysis in the cloud can also contribute to the creation of predictive models for pet health, enabling proactive care management. An activity enabled by cloud use is the ability to analyze behavioral and health patterns over time so to identify trends and offer personalized recommendations for pet care and disease prevention (e.g., obesity, diabetes, and chronic diseases). However, it is crucial to optimize cloud usage, as it can lead to latency issues. To address this, it is recommended to implement optimization mechanisms for edge-cloud communications, such as intelligent task scheduling by dividing tasks between the edge and the cloud, using 5G networks to ensure high-speed and low-latency communication, and deploying mechanisms that distribute processing across multiple edge nodes to reduce the cloud load. For instance, algorithms can be employed to mitigate the impact of selfish user devices by allocating fewer edge server resources to those that maliciously discard tasks from other devices or offload an excessive number of tasks to edge or cloud servers [120]. There is a projected increase in the development of multifunctional devices that combine various capabilities in a single unit. These devices will integrate functions such as automatic feeding, health monitoring, and environmental management into a single appliance. By simplifying the daily management of pet care and reducing the need for multiple independent gadgets, these devices will not only improve the operational efficiency but also offer a more cohesive experience for owners. The studies analyzed show a trend towards integrated solutions that will facilitate more holistic and effective care tailored to the diverse needs of pets. Artificial intelligence (AI) is a trending topic in the current society. The impact of AI on pet care is evident in several key areas that promise to revolutionize how owners interact with their animals. Firstly, AI optimizes health monitoring by analyzing large volumes of data from sensors and other devices connected to layers, such as the edge and fog. By analyzing historical and real-time data, AI can identify trends and patterns that allow for the anticipation and prevention of potential problems (e.g., early signs of obesity). These predictive applications offer recommendations based on future forecasts, such as diet adjustments, changes in exercise routine, or veterinary visits.