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Venkatesh Sivaprakasam,Vartika Kulshrestha,Godlin Atlas Lawrence Livingston,Senthilnathan Arumugam 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.7
The development of lightweight, low energy and small-sized sensors incorporated with the wireless networks has brought about a phenomenal growth of Wireless Sensor Networks (WSNs) in its different fields of applications. Moreover, the routing of data is crucial in a wide number of critical applications that includes ecosystem monitoring, military and disaster management. However, the time-delay, energy imbalance and minimized network lifetime are considered as the key problems faced during the process of data transmission. Furthermore, only when the functionality of cluster head selection is available in WSNs, it is possible to improve energy and network lifetime. Besides that, the task of cluster head selection is regarded as an NP-hard optimization problem that can be effectively modelled using hybrid metaheuristic approaches. Due to this reason, an Improved Coyote Optimization Algorithm-based Clustering Technique (ICOACT) is proposed for extending the lifetime for making efficient choices for cluster heads while maintaining a consistent balance between exploitation and exploration. The issue of premature convergence and its tendency of being trapped into the local optima in the Improved Coyote Optimization Algorithm (ICOA) through the selection of center solution is used for replacing the best solution in the search space during the clustering functionality. The simulation results of the proposed ICOACT confirmed its efficiency by increasing the number of alive nodes, the total number of clusters formed with the least amount of end-to-end delay and mean packet loss rate.
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Savan K. Raj,Vartika Sharma,Anshul Yadav,Pankaj D. Indurkar,Vaibhav Kulshrestha 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.117 No.-
Materials with ultrahigh adsorption competences are extensively required for effective wastewaterremediation. Unfortunately, leaching and agglomeration of nanomaterial-based adsorbents is a commonproblem to be resolved. Carbon-based hybrid nanocomposite showing enormous capability in the field ofwastewater treatment. Arsenic and fluoride in water have unpropitious effects on people’s health, andremediation of these ions through adsorption is one of the foremost challenges and high priority tothe research. The present study deals with fabricating a novel composite using alumina wrapped carbonmicrospheres (Al-CMs) with high adsorption capacities and investigating the mechanism for removingpentavalent arsenic/arsenate (As(V)) and fluoride (F) at the molecular level. The maximum adsorptioncapacities for As(V) and F calculated from the Langmuir model are 68 and 371.1 mg/g, respectively, comparativelyhigher than other reported nano-adsorbents. Under optimized conditions, Al-CMs are able toremove more than 98% of F and As(V) under wide range of pH (2–12). Further, the interaction energy ofAl-CMs with F and As(V) was examined using density functional theory (DFT). The reported work exhibitsa feasible adsorbent for removing F and As(V) from the wastewater.
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