Sorting algorithmdeals with the arrangement of alphanumeric data in static order.It plays an important roleinthe field of data science. Selection sort is one ofthe simplest and efficient algorithms which can be applied for the huge number of elements it works likeby giving list of unsorted information, the calculation which breaksintotwo partitions. One section has all the sorted information and another sectionhas all thestaying unsorted information. The calculation rehashes itself, by finding the smallestcomponentinside the rundown of unsorted information and swappingitwith the furthest left component, in the end setting everything straight information.This researchpresents the implementationof selection sort usingC/C++, Python, and Rust and measuredthetime complexity. After experiment,we have collectedtheresults in terms of running time, andanalyzed the outcomes.It was observed that python language hasvery smallamount of line of code, and it also consumesless storage and fast running time then other two languages.

An unmanned aerial vehicle communication Networks (UAVCN) is a group of UAVs (unmanned aerial vehicles) that is able to communicate and trigger information to each other for a particular mission. This network node is power limited. However, the routing mechanism should be improved which extend the lifetime and performance of UAV nodes. This manuscript intends to introduce a concept of a cross-layer method for efficient power and routing for UAVCN to increase communication performance. The cross-layer methodology integrates the physical, link and network layer. An efficient power optimized link state routing (EP-OLSR) protocol is proposed which is an extension to optimized link state routing (OLSR) protocol. The proposed routing protocol evaluated by developing scenarios of UAVCN in OPNET modeler 14.5. The simulation is carried out by implementing OLSR and EP-OLSR using different network parameters and compared the routing protocols performance. The results showed that the EP-OLSR maximizes network performance efficiency as compared to OLSR. It minimizes the delay and maximizes the throughput and increases the network performance.