Wireless Sensor Networks in Precision Agriculture
Components of a Wireless Sensor Network
A wireless sensor network (WSN)’s core components include sensor nodes, the gateway, cloud and edge servers, a machine language model layer, and a power supply. Sensor nodes are the fundamental building blocks. They are small, autonomous devices with sensors for measuring environmental parameters like temperature, humidity, or pressure. The gateway collects and transmits data to a central location. Sensor nodes gather data, which is then processed locally by edge servers and subsequently sent to a cloud server for more comprehensive analysis and storage. Machine learning (ML) is used throughout WSN architecture, from data collection and analysis to network management and security. In a WSN, the power supply source for WSN components can be a battery, solar power, or energy harvesting.
Sensor Nodes
The sensor node, also known as a mote, can communicate wirelessly through the communication link and forward its data to the base station, which acts as a processing unit in the WSN system or coordinator node through the gateway communication based on various topologies such as mesh, star, etc. A sensor node collects, computes, and communicates the data and information with its associate nodes in a particular network. The communication can collect information from various sensor units in the sensor nodes from simple (i.e., humidity, pressure, and temperature) to complex (i.e., positioning, tracking) and then combine and transmit it to the wireless sensor network to realize real-time monitoring of WSN.
Components of Sensor Nodes
Each sensor node has four main components: the power and power management module, a sensor, a microcontroller, and a wireless transceiver, see Figure 4.2. The power module offers the reliable power needed for the system. A power scavenging unit like solar cells can support power units. The sensor is the bond of a WSN node, which can obtain the environmental and equipment status. A sensor is in charge of collecting and transforming the signals, such as light, vibration, and chemical signals, into electrical signals and then transferring them to the microcontroller. Sensor response is typically in the form of an electrical property such as voltage, current, or resistance, and mathematical formulae are applied to transform the raw data into a measurement unit associated with the property being measured by the sensor.
Classification of Sensors
Depending on the quantity being measured by the sensor, the following are some of the sensors that have been applicable in precision farming:
Gateway
In WSNs, the gateway or sink node, also known as the base station, is a crucial component that serves as a central point for data collection and aggregation from the sensor nodes. It acts as the interface between the WSN and the external world, transmitting aggregated data to a central server or the internet. The gateway is at the upper level of the hierarchical WSN.
Cloud and Edge Servers
In WSNs, cloud and edge servers play crucial roles in data processing and management. Cloud servers offer centralized processing and storage, while edge servers provide local processing closer to the sensors, offering benefits like reduced latency and bandwidth usage. In a WSN-edge-cloud system architecture, edge computing can be regarded as a relay between the cloud and sensor nodes in the WSN. The edge computing nodes extend the cloud computing paradigm to the network’s edge bidirectionally. In the node-to-cloud direction, edge nodes focus on local functionality to support geographically closer sensing with the additional feature of data preprocessing and rapid reaction time.
Machine Learning Model Layer
Machine learning (ML) is used throughout WSN architecture, from data collection and analysis to network management and security. It helps to optimize network performance, improve security, and enable intelligent decision-making in various WSN applications. Specifically, ML algorithms can be used to optimize data aggregation at cluster heads, reducing redundant data transmission and improving network efficiency. ML algorithms are used for routing by selecting optimal paths for data transmission, considering factors like network topology, traffic load, and latency.
Power Supply in WSNs
The power supply is a critical component in WSNs, especially in agriculture, where sensor nodes are often deployed in remote or hard-to-reach areas. The efficiency, autonomy, and reliability of a WSN heavily depend on the choice and management of its power source.
Click on the following topics for more information on wireless sensor networks in precision agriculture.
