Improved Load-Balanced Clustering for Energy-Aware Routing (ILBC-EAR) in WSNs

Sensors are considered as important elements of electronic devices. In many applications and service, Wireless Sensor Networks (WSNs) are involved in significant data sharing that are delivered to the sink node in energy efficient manner using multi-hop communications. But, the major challenge in WSN is the nodes are having limited battery resources, it is important to monitor the consumption rate of energy is very much needed. However, reducing energy consumption can increase the network lifetime in effective manner. For that, clustering methods are widely used for optimizing the rate of energy consumption among the sensor nodes. In that concern, this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing (ILBC-EAR), which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member nodes. For providing equal rate of energy consumption among nodes, the dimensions of framed clusters are measured. Moreover, the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base station. The evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head node. Additionally, the model also provides higher throughput and minimal delay in delivering data packets.     

A Review of Tunnel Field-Effect Transistors for Improved ON-State Behaviour

Silicon - Tunnel Field-effect transistor (TFET) is regarded as the most promising candidate which can possibly replace the traditional MOSFET from current IC technology. It has gained much...     

A systematic review on selection characterization and implementation of probiotics in human health

Food Science and Biotechnology - Probiotics are live bacteria found in food that assist the body's defence mechanisms against pathogens by reconciling the gut microbiota. Probiotics are...     

Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around. Hence, the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield. In present decade, the application of deep learning models in many fields of research has created greater impact. The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model, leads to the incorporation of deep learning method to predict the soil quality. With that concern, this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning (ISQP-DL). The work considers the chemical, physical and biological factors of soil in particular area to estimate the soil quality. Firstly, pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression (DNNR) model. Secondly, soil nutrient data has been given as second input to the DNNR model. By utilizing this data set, the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated. For training and testing, the model uses Deep Neural Network Regression (DNNR), by utilizing the dataset. The results show that the proposed model is effective for SQP (Soil Quality Prediction Model) with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy. The results show that the proposed model achieves 96.7% of accuracy rate compared with existing models.