# Emerging Technologies for Automation in Environmental Sensing: Review
## 3 Emerging Technologies in Environmental Sensing
### Edge Computing in Environmental Sensing  
The surge in data and processing reveals drawbacks in cloud-based big data processing. Challenges include real-time constraints due to amplified data transmission from edge devices, jeopardizing privacy and causing delays. Figure 5 presents the edge computing workflow that involves real-time data processing, data caching, buffering, and optimization, and decision-making, followed by storage and communication for data transmission, ultimately enabling iterative refinement for enhanced efficiency and effectiveness. The growing number of smart devices leads to substantial energy consumption in data centers, hampering efforts to boost energy efficiency. These issues underscore the need for alternative solutions to address the evolving demands of our intelligent society. As human needs continually evolve, and smart societies advance, intelligence becomes an integral part of our daily routines, as seen in smart homes, autonomous vehicles, transportation, cameras, and industrial manufacturing components. The rapid progression of IoT, AI, big data, and cloud computing has given rise to edge computing, a distinctive open forum networking philosophy. This model harnesses network, computing, storage, and application core capabilities in the proximity of physical surroundings, aiming to achieve faster network service responses by reducing latency and bandwidth utilization [90]. Before edge computing, traditional cloud computing centralized data transfer to the cloud center. The concept originated in 2006 when Google's CEO introduced it at a search engine conference. With Google's influence, cloud computing became a robust platform with distributed computing, load balancing, and virtualization. Despite strengths, the IoT's rise challenges cloud computing with increased device data, impacting bandwidth for time-sensitive and real-time systems [91; 92]. This affects load management, real-time \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Location** & **Model/Approach** & **Performance Measure** & **Key Findings** \\ \hline
[79] & California & Support Vector Regression & Accuracy & Precise prediction of pollutants \\
[80] & N/A & LSTM, LSTM-MVR & Forecasting & LSTM-MVR superior in forecasting \\
[81] & N/A & Random Forest, & Precision & LSTM showed enhanced precision for \\
[82] & Delhi & BiLSTM-A & Forecasting & Promising foresight for NO2, PM10, and PM2.5 \\
[83] & Taiwan & RNN with LSTM & RMSE & Comparable RMSE values for PM2.5 prediction \\
[84] & South Korea & RNN with GRU & Forecasting & Successful PM10 and PM2.5 forecasting \\
[85] & Seoul & N/A & MSE & Fine dust forecasting achieved with MSE & below 10.7\% \\
[86] & N/A & Various factors considered & N/A & Pollution mitigation in water bodies and air \\
[87] & N/A & Evolutionary & & Detection of water pollution with sensors \\  & IoT-based approach & & & and algorithms \\
[88] & N/A & Automated IoT-driven & N/A & Introduction of an automated water quality \\  & water quality analysis & & analysis system \\
[89] & N/A & Deep learning & N/A & Detection of water pollution through \\  & for biomonitoring & & Caenorhabditis elegant swimming behaviors \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of various studies addressing air and water pollution monitoring and mitigation strategies. performance, bandwidth, energy consumption, and data security [93; 94; 95]. The escalating number of mobile and IoT devices strains network bandwidth, resulting in delays in data delivery. Consequently, ref. [96] introduces a strategy aimed at selecting high-performance cloudlets within edge computing frameworks. Edge computing improves scalability issues in IoT platforms by decentralizing data processing, reducing latency, optimizing bandwidth, and increasing security. By dispersing computation nearer to the data source, edge computing enables instantaneous decision-making, diminishes data transmission expenses, and enhances system robustness.