## 3 Communication in IoT
### Commonly Used Sensors for Smart Farming and Heavy Metal Identification
Lately, an Arduino-based potentiostat was fabricated from cost-efficient components and was able to execute simple electrochemical experiments, whereby the results were recorded and analyzed in a Windows operating system via USB interface [136; 137; 138]. As an addition to Arduino-based potentiostat, Raspberry Pi (RPi) controller was also used to execute the electrochemical experiments, whereby the results were displayed on the LCD touch panel connected to the controller [139]. Both Arduino- and Raspberry Pi-based potentiostat have the potential to incorporate wireless sensors for data transmission; however, these controllers do not contain a built-in Analog to Digital Converter (ADC) and Digital to Analog Converter (DAC) which make the overall design more sophisticated. In this regard, [PERSON] et al. [142] came up with a portable Heavy Metal Potentiostat (HMstat) to detect heavy metal composition on-site. Their potentiostat comprised a digital Control Signal Component (CSC) and the electronic component, which is the analog Potentiostat Read-out Circuit Component (PRCC), as shown in Figure 5[142]. Nonetheless, it is worth noting that both the Arduino and RPi controller board do support the incorporation of various sensor modules. Therefore, researchers have room to incorporate soil moisture and temperature sensors along with other sensors depending on the slots available in the controller; thus, an integrated system for soil moisture and heavy metal analysis can be developed. Figure 5: **(a)** The HMstat consists of Control Signal Component (CSC) and Potentiostat Read-out Circuit Component (PRCC) connected to the electrochemical cell (consisting of Screen Printed Electrode Gold (SPGE) and (b) overall connection of HMstat [142]. Other soil physical properties can be calculated to populate the map of the soil with other soil properties such as soil organic content, pH, sand, silt particles percentage, and nutrients such as Mg, P, OM, Ca, base saturation Mg, base saturation K, base saturation Ca, CEC, and K/Mg [143; 144; 145]. In situ, calculating these properties in real time also faces challenges due to scale, cost, and technology limitations. In precision farming, some of the long-lasting decisions can be taken using yield monitoring. This method helps in providing spatial distribution of crop yields at the end of the growing season [146; 147]. Yield sensors are normally mounted on farm equipment and capture yield data automatically in the course of the harvest. In particular, mass flow sensors on grain containers are mounted to record grain inflows along with the position [148]. The collected data are analyzed with tools such as ArchInfo, Mapinfo, and Environment System Analysis International [149]. In order to get an insight into the crop yield combined with field topography, Electrical Conductivity (EC) sensors are used [150]. Soil's ability to conduct current is measured by electrical conductivity. EC assessment is used to assess the use of phosphorus, cations in water, drainage, and rooting depths [150]. EC maps are used for zoning the area. The zoning is also used to incorporate precision agricultural practices such as variable rate irrigation, variable rate seeding, and drainage management. Electromagnetic Induction (EMI) methods can be used for the mapping of the EC by apparent Electrical Conductance (ECa) and Visible Near Infrared Reflectance (VNIR) [151]. There are a number of commercial tools available, i.e., Veris 3100, EC400 sensors in conjunction with GPS systems [149; 152]. In the domain of soil sensing, macronutrients such as nitrogen, potassium, and phosphorus are essential to the growth of crops. The evaluation of these nutrients helps to assess the effects of fertilizer and potential applications. The optical detection is based on reflectance spectroscopy to measure the macrosimulation's reflection and absorption [153; 154]. A sensing system using planar electromagnetic sensors has been developed in the detection of nitrate and sulphate concentration in natural water resources [155].