# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 3 Related Work
They used Mica2 433 MHz motes with MDA300 sensor boards comprising of soil moisture and rainfall sampling nodes, a base node connected to a Superlite GSM gateway and routing nodes. They evaluated their network by taking into account three performance parameters, i.e., responsiveness, robustness, and longevity of the network. Researchers at UC Berkeley deployed a wireless sensor network, termed \"Macroscope in the Redwoods\", to monitor the complex spatiotemporal variations of the microclimate of a redwood tree [21]. The sensor node was a Mica2 Dot, a repackaging of the Mica2 manufactured by Crossbow. The more consisted of an Atmel ATmega 128 microcontroller operating at 4 MHz, a 433-MHz radio, and 512 KB of flash memory. The measured biological parameters were temperature, relative humidity, and light levels. Although Macroscopic was deployed at a small scale, it had the potential to measure the microclimate phenomena of a redwood tree at a spatiotemporal large scale. Further, it provided useful knowledge regarding deployment methodology and data analysis. LOFAR-agro (Low Frequency Array) is the first deployment of a large-scale sensor network in precision agriculture to protect potato crops from Phytophthora, a fungal disease, with close monitoring of the microclimate [22]. In this project, around 100 nodes were deployed in a potato field to measure the temperature and relative humidity of the environment, known to be the main factors in causing the disease. The researchers experienced a range of unanticipated technical challenges due to poor design, improper application of software engineering principles, semantic mismatches, programming bugs, and insufficient time for testing. Researchers at Harvard University in collaboration with researchers at the University of North Carolina and the University of New Hampshire deployed a sensor network on an active volcano in Volcan Reventador, northern Ecuador to measure seismic and infrasonic signals [10]. The network comprised of 16 stations with seismic and acoustic sensors. Each node consisted of a Moteiv TMote Sky, a descendant of the UC Berkeley's Mica mote, an 8-dBi 2.4 GHz external omnidirectional antenna, a seismometer, a microphone, and a custom hardware interface board. The network was deployed for more than three weeks and collected data from several events. The model was based on event triggering which detected 230 eruptions and captured about 107 Mbytes of data. In a collaborative project called \"Life Under Your Feet\", researchers at John Hopkins University and with Microsoft deployed an experimental wireless sensor network in a Baltimore urban forest to monitor soil ecology [23]. They used MicaZ from Crossbow Inc. with a MTS101 data acquisition board comprising of ambient light and temperature sensors. They attached Watermark soil moisture sensors and a soil thermistor to the board. They reported a mixture of previously known and new observations from the operation of their testbed, such as reprogramming the motes, sensor nodes and calibration cost, the importance and challenge of low-level programming, the need for fast and efficient routing infrastructure and network design, and reliable delivery and high quality of data. [PERSON] et al. [24] designed and tested a prototype of a neck-collar-based virtual fencing system to monitor and control livestock movement and behaviour. They conducted an experiment on 25 steers at the Belmont Research Station, Australia for over a month. The electronics used in the experiment consisted of the Fleck WSN (Wireless Sensor Network) device in combination with a control board which could produce an electrical pulse. Two 6-volt batteries placed in the collar's compartments,were used to charge and power the Fleck. For radio communication, they used a 433 MHz wave flex whip antenna. A statistical analysis was done on observational and videotaped data to see how animals reacted to the stimuli. The experiment showed that the sensory cue has the potential to be used for monitoring livestock in large grazing systems.