R. Kumar

Measurement of soil moisture and control of irrigation according to the measured data is crucial in agriculture fields where water scarcity is always a serious issue. For this purpose, a cost-effective distributive network system has been proposed and developed using technology like IoT to control complex irrigation processes. An internet-enabled embedded moisture sensing unit was designed that consists of a capacitive sensor probe and electronic system to process the soil moisture value. The sensor probe was calibrated for six different varieties of soil using the Thermo gravimetric method. The output response is inspiring, with a goodness of fit value of 0.99. Algorithms are developed for irrigation control that operates by a developed web-based application from the control station. The system was implemented at a total cost of 122.37 US dollars and tested in cassava agriculture field for loam soil in Nagaland, India, for 91 days and showed magnificent water saving of up to 95% compared with traditional approaches.

The global demand for food can be eliminated by precision farming. This research work proposes a low-cost IoT-enabled handy device to measure soil water content. Three different sensor probes are designed in COMSOL Multiphysics 5.4 and fabricated using PCB Technology. The designed sensor probes are calibrated to effectively measure moisture content for three different soil types (silt/sandy/clay). An electronic system has been programmed according to Optimized-Moisture-Value (OMV) algorithm to read and collect the soil moisture information. Three sensor probes, capacitance, and voltage responses are analyzed using linear fitting. It has been observed from the response data that model B's performance is better than the other two presented models in terms of soil moisture. The obtained goodness of fitness value for model B is around 0.999 for all the categories of soils. The electronic system is built around W78E054D and ESP8266 controllers. The W78E054D controller is used to excite the sensor probe with a signal having a frequency of 500 kHz. The IoT-enabled controller ESP8266 reads and collects the soil moisture data according to the OMV algorithm.

A greater community of researchers widely studies fog computing as it reduces the massive data flow to the existing cloud-connected network and performs better for real-time systems that expect a quick response. As the fog layer plays a significant role in a fog-cloud system, all of the devices participating in fog computing must be balanced with appropriate load to upstretched the system performance. The proposed method is founded on a tree-based dynamic resources arrangement mechanism that refreshes the fog clusters created using Fuzzy C Mean (FCM) to increase the speed of resource allocation. With the help of Fuzzy rule-based load calculation and intra-cluster job allocation, the load inside the group is maintained. The system also has the facility of inter-cluster job forwarding, which works on demand. A novel load balancing strategy, Real-Time Flexi Forwarded Cluster Refreshing System (RTFRS) is proposed by which all the tasks can be handled efficiently within the fog cloud system. The proposed system is designed so that overall complexity is not upraised and becomes suitable for fog computing architecture with low processing capacity by maintaining the quality of service. Experimental results show that the proposed model outperforms standard methods and algorithms used in fog computing concerning average turnaround time, average waiting time, resource utilization, average failure rate, and the load on the gateway.