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.
At present, indoor localization system based on ultra-wideband (UWB) has attracted more and more attention. In UWB system, Time Difference of Arrival (TDOA) and Two-Way Ranging (TWR) are widely used. However, TDOA requires high-accuracy time synchronization between all anchor nodes and even slight noise can cause large localization error. In TWR, although two-way communication between anchor nodes with known location and blind nodes to be located can avoid the time synchronization issue effectively, the clock drift and the number of blind nodes will affect the system performance. To overcome these problems, a new synchronization-free TDOA location algorithm is proposed. Firstly,the clock model is established and the influence of antenna delay is considered. Then, the system signal exchange mechanism and localization model are proposed. In the system, the blind nodes just receive the ranging signals from anchor nodes so that the system has no limit on the number of blind nodes. Finally,the major factor affecting the accuracy of ranging -clock drift, is discussed, and then a clock frequency offset compensation algorithm is proposed. The indoor localization experiment results show that the indoor localization system designed in this paper can achieve 3-D localization.
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