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Unmanned Aerial Vehicles (UAVs) are a promising solution for sensor data collection (DC) in large-scale area. The challenge is to minimize the DC route, which will reduce UAV energy consumption and data latency. The novelty of this paper lies in its innovative approach to optimizing sensor data collection by UAVs. It combines Ant Colony Optimization (ACO) and K-means algorithms to establish an initial shortest route and introduces a unique method for grouping sensor nodes (SNs) along the route based on the UAV's footprint, reducing data latency and energy consumption for both UAV and sensors. First, an initial shortest route that traverses all SNs is established based on the ACO and the K-means algorithms. Second, we group the sensor nodes (SNs) along the initial route using the footprint of the UAV, so that the latter can collect the data of the group in one stop, instead of stopping at each SN. By sequencing the hovering locations, we obtain a (shorter) intermediate route. Third, we shorten this route even further, by applying ACO to the set of hovering locations of the intermediate route. The solution has been implemented fully in Python. The results show that the route length gets shorter progressively with each phase. To evaluate the performance of the solution objectively, we have compared it with four states of the art solutions. The results show vividly that the proposed solution produces a DC route 19.28% shorter than the shortest route produced by the four competitive solutions. Moreover, it demonstrates a remarkable improvement by retaining 44% of energy in most SNs while over 99% energy depletion observed in the five state-of-the-art competitive solutions. [ABSTRACT FROM AUTHOR]

The original K -nearest neighbour (K NN) algorithm was meant to classify homogeneous complete data, that is, data with only numerical features whose values exist completely. Thus, it faces problems when used with heterogeneous incomplete (HI) data, which has also categorical features and is plagued with missing values. Many solutions have been proposed over the years but most have pitfalls. For example, some solve heterogeneity by converting categorical features into numerical ones, inflicting structural damage. Others solve incompleteness by imputation or elimination, causing semantic disturbance. Almost all use the same K for all query objects, leading to misclassification. In the present work, we introduce K NNHI, a K NN-based algorithm for HI data classification that avoids all these pitfalls. Leveraging rough set theory, K NNHI preserves both categorical and numerical features, leaves missing values untouched and uses a different K for each query. The end result is an accurate classifier, as demonstrated by extensive experimentation on nine datasets mostly from the University of California Irvine repository, using a 10-fold cross-validation technique. We show that K NNHI outperforms six recently published K NN-based algorithms, in terms of precision, recall, accuracy and F-Score. In addition to its function as a mighty classifier, K NNHI can also serve as a K calculator, helping K NN-based algorithms that use a single K value for all queries that find the best such value. Sure enough, we show how four such algorithms improve their performance using the K obtained by K NNHI. Finally, K NNHI exhibits impressive resilience to the degree of incompleteness, degree of heterogeneity and the metric used to measure distance. [ABSTRACT FROM AUTHOR]