# Emerging Technologies for Automation in Environmental Sensing: Review
## 3 Emerging Technologies in Environmental Sensing
### Edge Computing in Environmental Sensing
Therefore, edge computing expands on cloud computing, offering distinct features. Cloud computing excels in comprehending the entirety, processing extensive data, and contributing to non-real-time data processing, such as in business decision-making. In contrast, edge computing focuses on the local context, proving more effective in small-scale, real-time intelligent analysis, especially in meeting the immediate needs of local businesses. In applications involving intelligence, cloud computing is better suited for the centralized processing of voluminous data, while edge computing can be applied for compact intelligent analysis and localized services [97]. Smart agriculture envisions leveraging diverse Information and Communication Technologies (ICTs) to enhance productivity in a sustainable and economically feasible way. Edge computing [98] presents a method by which the farming community could more efficiently tap into and make use of smart agriculture services. In their research [99], the authors presented a budget-friendly solution for the extensive monitoring of environmental parameters through the utilization of flying IoT and edge-cloud computing. Implemented and evaluated on an actual Medenine farm in Tunisia, the system assists farmers, government entities, or manufacturers in anticipating environmental conditions across the vast farm area. This contributes to improving crop productivity and farm management in a cost-effective and timely manner. An advanced, cooperative, and hierarchical UAV-WSN system for intelligent crop monitoring in precision agriculture was proposed in [100]. The system demonstrated both  
Figure 5: Edge computing workflow [97]. resilience and efficiency, with improved performance and optimized trajectories, allowing for the effective utilization of limited resources within the ground sensor network. The effectiveness of edge computing in scalable data analytics is demonstrated in [101]. Utilizing a Raspberry Pi as both a base station and an edge node, cherry tomato growth states are predicted by the system. These predictions are then transmitted to a central cloud server for consolidation, model integration, and analysis, leading to yield predictions. This approach minimizes data traffic and provides farmers with the capability to secure and selectively share their data. Amidst the rapid proliferation of IoT devices, the significance of edge computing as a viable alternative to cloud computing has grown considerably. Edge computing involves technologies that enable computation at the network's edge. In a study [102], the researchers created a prototype to evaluate pollution using an Arduino board and the IBM Watson IoT platform. With edge computing, their model aims to reduce the computational load on the cloud. Merging low-cost sensors with Wireless Sensor Networks (WSNs) forms a comprehensive system. These models enhance the efficient detection of air pollutant dispersion, enabling individuals, including community users, to assess personal exposure to pollutants through wearable sensor nodes [103]. Various methodologies exist for air quality assessment. Edge computing tackles issues like delays and network instability in cloud computing, finding applications in smart transportation, urban settings, and water conservancy. Research by [104] efficiently predicted ecological water demand by combining edge computing and GIS. Ref. [105] successfully minimized service response times through mobile edge computing. Additionally, ref. [106] proposed a framework integrating blockchain and edge computing to promote water-saving practices, fostering conservation efforts intelligently. Table 4 showcases how edge computing techniques are applied across different domains in agriculture and environmental monitoring. From livestock management to environmental sensing, edge computing is utilized for tasks such as reducing latency, offloading computation, and managing data traffic.