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Wireless Sensor Network consist thousand number of intelligent tiny sensor nodes that form a self-sustainable and self-organising network. These network offers various potential interdisciplinary applications such as in infrastructure development for smart cities, critical health care, precision agriculture etc. Recent technological development has steered up demand of pervasive networks in day-to-day life. However, the fast pace development of wireless sensor network is hampered by few constraints like coverage problem and connectivity issues between nodes that in turn affects network lifetime. Strategic deployments of sensor nodes help in providing some pre-determined number of nodes in ensuring optimal or suboptimal coverage in designated field that also prolong network lifetime. Since maximum coverage with ensured connectivity are two separate issues, however together either they are addressed for finding set of most probable location of nodes or to density of nodes for utmost coverage of event in monitored area. These coverage cavities cause connectivity issues between normal nodes, router nodes and gateway nodes affecting performance of nodes opens interesting research thrusts. Here in this paper an approach has been presented in calculating critical points at boundary of region of interest (ROI) for multi-tier hierarchical network to detect and recovery from coverage cavities and attain long life for network. [ABSTRACT FROM AUTHOR]

As the world population is rising rapidly, there is a fast depletion of resources. So to meet the requirements of increasing population, innovation is required especially in the field of agriculture. In Precision agriculture, smart sensors are used to impart data which helps the farmers in monitoring and optimizing the crop production along with record of changing environmental factors. IoT and sensor networks play a vital role in agriculture. The agricultural industry is being transformed with the use of sensors, software and AI. Combinational sensors and sensor hardware gives the data that helps in increasing the crop production and producing high yield crops which are disease free to keep pace with the increasing demand for food. This article presents a review of precision agriculture, soil less agriculture and sensors used in precision agriculture. [ABSTRACT FROM AUTHOR]

There is a rapid increase in the agriculture field due to the fertilizer use, the fertilizer has increased the growth of crop production and negative effect over the fertilization is also reduced. The fertilizer is considered as the crucial point in the world of agriculture. In precision agriculture correct amount of fertilizer is given based on the smart fertilization this includes the information or data set, sensor or smart tools. This could provide smart fertilization and integrated nutrient management. The recent research in prediction method like machine learning based hyperspectral imaging support the N fertilization accuracy. By using image processing method, the damage caused by the various nutrients such as iron, potassium and nitrogen can be predicted by using Red, Green and Blue (RGB) color image. The present imaging technology cannot predict the exact nutrients present in the soil. Hence, a biomarker method has been proposed to overcome the challenges. This could be able to predict the exacts quantity of nutrients present in the soil and the prediction rate of the particular nutrients is with high specificity. This paper presents an overview of the present approach to plant phenotyping by imaging and plant biomarker-technology and to soil estimation by using image technologies for smart fertilization management. [ABSTRACT FROM AUTHOR]