With the implementation of the Internet of Things, the agricultural domain has become data-driven, allowing for well-timed and cost-effective farm management while remaining environmentally sustainable. Thus, the incorporation of Internet of Things in the agricultural domain is the need of the hour for developing countries whose gross domestic product primarily depends on the farming sector. It is worth highlighting that developing nations lack the infrastructure for precision agriculture; therefore, it has become necessary to come up with a methodological paradigm which can accommodate a complete model to connect ground sensors to the compute nodes in a cost-effective way by keeping the data processing limitations and constraints in consideration. In this regard, this review puts forward an overview of the state-of-the-art technologies deployed in precision agriculture for soil assessment and pollutant monitoring with respect to heavy metal in agricultural soil using various sensors. Secondly, this manuscript illustrates the processing of data generated from the sensors. In this regard, an optimized method of data processing derived from cloud computing has been shown, which is called edge computing. In addition to this, a new model of high-performance-based edge computing is also shown for efficient offloading of data with smooth workflow optimization. In a nutshell, this manuscript aims to open a new corridor for the farming sector in developing nations by tackling challenges and providing substantial consideration.

With the advent of the industrial revolution 4.0, the goal of the manufacturing industry is to produce a large number of products in relatively less time. This study applies the Taguchi L27 orthogonal array methodological paradigm along with response surface design. This work optimizes the process parameters in the turning of Aluminum Alloy 7075 using a Computer Numerical Control (CNC) machine. The optimal parameters influenced the rate of metal removal, the roughness of the machined surface, and the force of cutting. This experimental investigation deals with the optimization of speed (800 rpm, 1200 rpm, and 1600 rpm) and feed (0.15, 0.20, and 0.25 mm/rev) in addition to cutting depth (1.0, 1.5, and 2.0 mm) on the turning of Aluminum 7075 alloy in a CNC machine. The outcome in terms of results such as the removal rate of material (maximum), roughness on the machined surface (minimum), along with cutting force (least amount) were improved by the L27 array Taguchi method. There were 27 specimens of Al7075 alloy produced as per the array, and the corresponding responses were measured with the help of various direct contact and indirect contact sensors. Results were concluded all the way through diagrams of main effects in favor of signal-to-noise ratios and diagrams of surfaces with contour diagrams for various combinations of responses.

Rising greenhouse gas emissions stemming from fossil fuel-driven vehicles are causing damage to the environment. To counteract this, one solution is the adoption of electric vehicles (EV) for transportation requirements. In this regard, one category of EVs that requires special attention is light electric vehicle (LEV), mainly because of their wide potential in public transportation—especially in developing countries. To realise widespread adoption of LEVs for this purpose, it is imperative to make their charging systems more robust. Consequently, the subject of LEV charging has gained considerable traction, and numerous research works have been reported on this subject in recent years. Hence, this paper aims to chronicle recent research developments on LEV charging techniques, by placing special attention on DC-DC converter topologies used in both on-board and off-board chargers. This review explores recent LEV charger DC-DC converters in literature by segregating them into isolated and non-isolated topologies. Lastly, this work explores challenges and emerging trends in LEV charging, which can potentially be explored by researchers in the future.