A threshold‐based sorting algorithm for dense wireless sensor systems and communication networks. (16th January 2023) - Shirvani Moghaddam, Shahriar; Shirvani Moghaddam, Kiaksar
Abstract: Nowadays, time‐varying and high‐density data of wireless sensor systems and communication networks compel us to propose and realise low‐complexity and time‐efficient algorithms for searching, clustering, and sorting. A novel threshold‐based sorting algorithm applicable to dense and ultra‐dense networks is proposed in this study. Instead of sorting whole data in a large data set and selecting a certain number of them, the proposed algorithm sorts a specific number of elements that are larger or smaller than a threshold level or located between two threshold values. First, based on the mean value and standard deviation of the data, a theoretical analysis to find the exact and approximate thresholds, respectively for known (Gaussian, uniform, Rayleigh, and negative exponential) and unknown probability distributions is presented. Then, an algorithm to sort a predefined number of data is realised. Finally, the effectiveness of the proposed algorithm is shown in the view of the time complexity order, the running time, and the similarity measure. To do this, theoretical and numerical analyses are used to show the superiority of the proposed algorithm in known and unknown distributions to the well‐known conventional and gradual conventional versions of Merge, Quick, and K‐S mean‐based sorting algorithms. Abstract : The threshold‐based algorithm proposes a time‐efficient sorting algorithm applicable for time‐varying wireless networks, which sorts a predefined number of elements in a large data set that are larger or smaller than the other or located between two parts. A theoretical analysis for known and unknown distributions is presented, which finds exact and approximate thresholds. The theoretical and numerical analyses show the superiority of the proposed algorithm to the well‐known sorting algorithms in terms of time complexity, running time, and similarity measure.