UAV-IoT collaboration: Energy and time-saving task scheduling scheme

UAVs are capable of providing significant potential to IoT devices through sensors, cameras, GPS systems, and so forth. Therefore, the smart UAV-IoT collaborative system has become a current hot research topic. However, other concerns require in-depth investigation and study, such as resource allocation, security, privacy preservation, trajectory optimization, intelligent decision-making, energy harvesting, and so forth. Here, we suggest a task-scheduling method that splits IoT devices into distinct clusters based on physical proximity and saves time and energy. Cluster heads can apply an auto regressive moving average (ARMA) model to predict intelligently the timestamp of the arrival of the next task and associated estimated payments. Based on the overall expected payment, a cluster head can smartly advise the UAV about its time of next arrival. According to the findings of the simulation, the proposed ETTS algorithm significantly outperforms Task TSIE and TDMA-WS in terms of energy use (67%) and delays (36%).     

Predicting Solubility of Military,Homemade, and Green Explosives in Pure and Saline Water using COSMO‐RS

The conductor‐like screening model for real solvents (COSMO‐RS) has previously been shown to give accurate aqueous solubilities for a range of organic compounds using only quantum chemical simulation data. Application of this method for solid organic explosives, however, faces two difficulties; it requires correction for the free energy of fusion (a generally unknown quantity for these compounds) and it shows considerable error for common explosive classes such as nitramines. Herein we introduce a correction factor for COSMO‐RS that is applicable to a wide range of explosives, and requires no data beyond a quantum chemistry calculation. This modification allows COSMO‐RS to be used as a predictive tool for new proposed explosives or for systems lacking experimental data. We use this method to predict the temperature‐dependent solubility of solid explosives in pure and saline water to an average accuracy of approximately 0.25 log units at ambient temperature. Setschenow (salting‐out) coefficients predicted by this method show considerable improvement over previous COSMO‐RS results, but are still slightly overestimated compared to the limited experimental data available. We apply this method to a range of military, homemade, and “green” explosives that lack experimental seawater solubility data, an important property for environmental fate and transport modeling.