## 3 Communication in IoT
### Edge Computing in Precision Farming
MySense is a generic framework for the rapid development and use of precision wine scenarios monitoring application [205]. It consists of four layers, including sensors/actuators, WSN/gateway, web, and cloud applications. For local tasks and real-time alarm generation, fog computing is used on the WSN/gateway layer. The platform was used in a winery to study the dynamics of diseases in the current microclimatic setting. The necessary ingredient that makes farming operations work is consistent, productive soil. [PERSON] et al. [206] came up with an Internet-of-Things-based sensing framework for assessing nitrogen, phosphorous, potassium (NPK) concentrations in soil. In order to allow a colorimetric approach in the sensor, Light Dependent Resistors (LDR) and Light Emitting Diodes (LEDs) were used. A fuzzy rule-based method implemented on an edge computer using Raspberry Pi was used to assess the sensed values pertaining to nutrient deficiency. To assess other nutrient shortages, additional rules were added to this rationale. This method was low-cost and provided a quick alternative compared to laboratory testing methods, which take a longer time and are also costly. Soil fertility is optimized by assisting farmers in effectively managing their soil nutrition, resulting in cost savings, improved field management, and increased environmental conservation by eliminating runoff and leaching pollution. [PERSON] et al. [207] presented an exemplary case study in the domain of tomato production that demonstrated how edge computing generated scalable data analytics. In this case, a Raspberry Pi served as a sensor setup base station and a network edge node. On the edge node, a prediction analysis was implemented for the growth state of cherry tomato, and the data generated were sent to a cloud-based central server for model integration and analytical interpretation to gain insights to predict results. This strategy not only reduces data flow, but it also allows farmers to preserve their data. Atmospheric temperature is a critical factor for the productivity of plants, and in this regard, it plays a prominent role in irrigation planning and greenhouse management. [PERSON] et al. [208] used Single Spectrum Analysis (SSA) along with linear regression to introduce a temperature forecast system using low-cost microcontrollers and single board computer. The introduction of edge computing in their methodological paradigm drastically reduced the factor of latency. The advantageous part of their developed system is that the farmers can opt for a microclimate monitoring network rather than installing a sophisticated weather station. [PERSON] and [PERSON] [209] also looked at task parallelism in mobile edge computing. An offloading delay may arise because of the transmission characteristics. In addition, computation delay normally happens over cloud transactions while determining when to continue with the operation. [PERSON] et al. [210] looked at edge computing as a foundation for metagenomics research in agriculture, and found feasibility for remote microbial studies of water, air, and soil. Traditional methods for metagenomics research necessitate a large amount of data transfer to the cloud. However, with the advent of advanced edge technologies, remote analyses can be performed, and the results can be sent to the cloud using a combination of System-on-a-Chip (SoC) and edge computing. A mix of edge and cloud provides a highly appealing approach to provide complete analysis workflows. The results of the experiments revealed a 95 percent reduction in data streaming, thereby demonstrating the feasibility of metagenomic research at a remote level. For singular analyses, an edge solution is feasible. On the other hand, a subsequent work by the authors in the domain of metagenomics revealed that shifting computation to the cloud improves cost and efficiency as the frequency of analyses increases [211].