# Emerging Technologies for Automation in Environmental Sensing: Review
## 1 Introduction  
Automated sensing systems optimize processes, minimizing human intervention, ensuring uniformity, and facilitating remote surveillance. According to the International Union of Pure and Applied Chemistry (IUPAC), \"automation\" denotes mechanization with systematic control, emphasizing the coordination of sequential manipulations [1]. Modern analytical systems exhibit varying degrees of mechanization, incorporating both hardware and software components. However, the interpretation of \"automation\" in the analytical systems literature lacks consistency. Advancements in electronics, computer science, and robotics propel the evolution of automated sensing systems, driving their progression [2]. Traditionally, data collection was manual, restricted by weather and cost, limiting coverage. A discussion about traditional methods and the transformative role of environmental sensor networks (ESNs) is reported in [3]. These wireless networks enhance data observation, providing high-quality time-series data. Yet, challenges like interoperability, calibration, communication, and longevity hinder the broad use of ESNs and robots in ecosystem sciences. Solving these problems is crucial for the widespread use of environmental robots. Researchers are focusing on creating better smart sensors that can measure things accurately in tough places like hospitals, space, and weather stations. These sensors help monitor machines and devices, but there are challenges, especially with wireless sensors. They face issues like tracking problems, being reliable, and working well in harsh conditions. Sometimes, they have trouble communicating, leading to delays and signal loss. Another issue is about how much area they can cover, affecting how well they work. Solving these problems is important to make a network that works well and is affordable for Industry 4.0 [4]. The challenges in environmental sensing systems include high costs and maintenance requirements for traditional monitoring instruments, with a need for cost-efficient solutions. While expensive systems provide accurate data, low-cost alternatives often compromise accuracy. Obtaining 3-dimensional air pollution statistics poses challenges due to cost and power consumption. Passive monitoring systems lack the flexibility and quality offered by active monitoring. Limited flexibility and scalability in existing systems hinder adaptability to changes in sensing node classes. Power consumption, particularly in outdoor sensor networks, remains a concern, requiring sustainable energy sources and ongoing research efforts [5; 6]. Monitoring water pollution through sensor networks offers a simplified approach. The primary challenge lies in detecting pollution, often determined by monitoring values surpassing defined thresholds. Distinguishing between natural pollution concentrations and those from specific sources poses a complexity for sensor nodes. To address this, cutting-edge techniques are introduced. Methods based on hypothesis testing, as presented in [7; 8], aim to enhance accuracy and reliability in identifying water pollution, thereby advancing robust monitoring systems. In smart agriculture, data from various sources need to work together seamlessly. These data, coming from farms, animal industries, and businesses, often come in different formats, making integration a challenge. Achieving interoperability--ensuring diverse data types can work together--is crucial for enhancing the value of widely distributed agricultural data [9]. For successful communication among different devices, interconnected and interoperable devices play a vital role in improving the overall efficiency of the system through cross-technology communication [10]. As smart agriculture advances, addressing these challenges becomes crucial for maximizing the benefits of diverse and distributed data. Again, setting up a smart farming system in extensive open areas comes with big challenges.