In order to effectively reduce the redundant information transmission in the network, a data fusion algorithm based on extreme learning machine optimized by bat algorithm for mobile heterogeneous wireless sensor networks is proposed. In this paper, the data fusion process of mobile heterogeneous wireless sensor networks is mainly studied, and regards the nodes of wireless sensor networks as neurons in the neural network of extreme learning machines. The neural network of the extreme learning machine extracts the sensory data collected by mobile heterogeneous wireless sensor network and combines the collected sensor data with the clustering route to greatly reduce the amount of network data sent to the sink node. Aiming at the problem that the extreme learning machine randomly generates the input layer weight and the hidden layer threshold before training, the output result is unstable, affecting the data fusion efficiency and the long delay, a new method of data fusion for mobile heterogeneous wireless sensor networks based on extreme learning machine optimized by bat algorithm is proposed. Simulation experiments are carried out from two aspects: mobile heterogeneous wireless sensor networks and heterogeneous mobile heterogeneous wireless sensor networks. The simulation results show that compared with the traditional SEP algorithm, BP neural network algorithm and ELM algorithm, the proposed BAT-ELM-based data fusion algorithm can effectively reduce network traffic, save network energy, improve network work efficiency, and significantly prolong network's lifetime. INDEX TERMS Mobile heterogeneous wireless sensor networks, data fusion, extreme learning machine, bat algorithm, energy efficient, reliability.
Location estimation is significant in mobile and ubiquitous computing systems. Considering the influence of measurement error caused by time difference of arrival (TDOA)/angle of arrival (AOA) hybrid location algorithm and the nonlinear optimization problem encountered in the location estimation, in this paper, a particle swarm optimization (PSO) algorithm based on the chaos theory is proposed for the hybrid location of mobile location estimation. Taking the TDOA/AOA hybrid location algorithm for mobile location estimation as the object, the proposed algorithm greatly improves the location performance and accuracy of mobile location estimation. First, the estimation function of the mobile station is obtained by the maximum likelihood method, and then the initial population of PSO is generated by using the estimation function of the mobile station as a fitness function. The chaotic optimized particle swarm optimization algorithm (CPSO) is used to solve the optimal solution of the optimal position of the population and obtain the optimal mobile location position estimation, which makes the TDOA/AOA location algorithm have better location performance. The simulation results have demonstrated that the performance of the proposed method compared with the traditional Chan algorithm, the Taylor algorithm, and the TDOA/AOA hybrid location algorithm, the proposed algorithm can reduce the impact of error on the location accuracy, achieve a balance of global and local search capabilities, and have a faster convergence speed and more accurate positioning accuracy. INDEX TERMS Location algorithm, particle swarm optimization, chaos theory, time difference of arrival, arrival angle, TDOA/AOA.
Frequent itemset mining is a fundamental problem in data mining area because frequent itemsets have been extensively used in reasoning, classifying, clustering, and so on. To mine frequent itemsets, previous algorithms based on a prefix tree structure have to construct many prefix trees, which is very time-consuming. In this paper, we propose a novel frequent itemset mining algorithm called DPT (Dynamic Prefix Tree) which uses only one prefix tree. We first introduce the concept of the postconditional database of an itemset, and analyze the distribution of an itemset's post-conditional database in a prefix tree representing a database. Subsequently, we illuminate how DPT adjusts the prefix tree to mine frequent itemsets and give three optimization techniques. An interesting advantage of DPT is that the algorithm can directly output a prefix tree representing all frequent itemsets after slight modifications. Using only one dynamic prefix tree, DPT avoids the high cost of constructing many prefix trees and thus gains significant performance improvement. Experimental results show that DPT remarkably outperforms previous algorithms with respect to running time and memory usage, and that a prefix tree representing all frequent itemsets DPT outputs can be used more efficient than a list representing them previous algorithms output.
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