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Today, farmers are suffering from the low yield of crops. Though right crop selection is the main boosting key to maximize crop yield by doing soil analysis and considering metrological factors, the lack of knowledge about soil fertility and crop selection is the main reason for low crop production. In the changed current climate, the farmers having primitive knowledge about conventional farming are facing challenges about making sagacious decisions on crop selection. The selection of the same crop in every seasonal cycle makes the low soil fertility. This study is aimed at making an efficient and accurate system using IoT devices and machine learning (ML) algorithms that can correctly select a crop for maximal yield. Such a system is reliable as compared to the old laboratory testing manual systems, which bear the chances of human errors. Correct selection of a crop is predominantly a priority in agricultural arena. As a contribution, we propose an ML-based model, Smart Crop Selection (SCS), which is based on data of metrological and soil factors. These factors include nitrogen, phosphorus, potassium, CO2, pH, EC, temperature, humidity of soil, and rainfall. Existing IoT-based systems are not efficient as compared to our proposed model due to limited consideration of these factors. In the proposed model, real-time sensory data is sent to Firebase cloud for analysis. Its results are also visualized on the Android app. SCS ensembles the following five ML algorithms to increase performance and accuracy: Decision tree, SVM, KNN, Random Forest, and Gaussian Naïve Bayes. For rainfall prediction, a dataset containing historical data of the last fifteen years is acquired from Bahawalpur Agricultural Department. This dataset and an ML algorithm, Multiple Linear Regression leverages prediction of the rainfall in future, a much-desired information for the health of any crop. The Root Mean Square Error of the rain fall prediction model is 0.3%, which is quite promising. The SCS model is trained for 11 crops’ prediction, while its accuracy is 97% to 98%.

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A Mobile Ad-hoc Network (MANET) is a collection of wireless nodes that can be dynamically set up anytime and anywhere without requiring existing infrastructure. In the network each node works as a router to discover and maintain the routes form the source to destination. Nowadays, demanding for transferring multimedia traffic over MANET is increased while the different factors that effect in MANET to maintain real-time communication in the presence of a dynamic network topology. One of the factors that affect the ability of MANET to transfer multimedia traffic from source to destination is routing protocol. The main goal of this paper is the study, selection and evaluation the performance of two reactive routing protocols: Adhoc On Demand Distance Vector (ADOV) and Dynamic Source Routing (DSR) in a high mobility case under low, medium, and high density scenarios in order to transfer video conferencing application by using an OPNET simulator. While AODV and DSR share similar On Demand behavior, the protocols internal mechanism leads to significant differences in performance. The metrics used for performance analysis are average end-to-end delay, network load and throughput. As a result of our studies that the performance varies widely across different network sizes and results from once scenario cannot be applied to those from the other scenario. In all three scenarios, we concluded that performance of the DSR protocol is not good as throughput is very low and the routing load is very high when compared to AODV protocols. AODV exhibits a better performance than DSR protocols in terms of end-to-end delay. This study proves that AODV performs well in terms of end- to-end delay, network load, and throughput with increasing number of mobile nodes.