A Simple and Efficient RSS-AOA Based Localization with Heterogeneous Anchor Nodes

Accurate and reliable localization is crucial for various wireless communication applications. Numerous studies have proposed accurate localization methods using hybrid received signal strength (RSS) and angle of arrival (AOA) measurements. However, these studies typically assume identical measurement noise distributions for different anchor nodes, which may not accurately reflect real-world scenarios with varying noise distributions. In this paper, we propose a simple and efficient localization method based on hybrid RSS-AOA measurements that accounts for the varying measurement noises of different nodes. We derive a closed-form estimator for the target location based on the linear weighted least squares (LWLS) algorithm, with each LWLS equation weight being the inverse of its residual variance. Due to the unknown variances of LWLS equation residuals, we employ a two-stage LWLS method for estimation. The proposed method is computationally efficient, adaptable to different types of wireless communication systems and environments, and provides more accurate and reliable localization results compared to existing RSS-AOA localization techniques. Additionally, we derive the Cramer-Rao Lower Bound (CRLB) for the RSS-AOA signal sequences used in the proposed method. Simulation results demonstrate the superiority of the proposed method

Accurate RSS-Based Localization Using an Opposition-Based Learning Simulated Annealing Algorithm

Wireless sensor networks require accurate target localization, often achieved through received signal strength (RSS) localization estimation based on maximum likelihood (ML). However, ML-based algorithms can suffer from issues such as low diversity, slow convergence, and local optima, which can significantly affect localization performance. In this paper, we propose a novel localization algorithm that combines opposition-based learning (OBL) and simulated annealing algorithm (SAA) to address these challenges. The algorithm begins by generating an initial solution randomly, which serves as the starting point for the SAA. Subsequently, OBL is employed to generate an opposing initial solution, effectively providing an alternative initial solution. The SAA is then executed independently on both the original and opposing initial solutions, optimizing each towards a potential optimal solution. The final solution is selected as the more effective of the two outcomes from the SAA, thereby reducing the likelihood of the algorithm becoming trapped in local optima. Simulation results indicate that the proposed algorithm consistently outperforms existing algorithms in terms of localization accuracy, demonstrating the effectiveness of our approach

A Study of the Mechanism of the Congruence of Leader–Follower Power Distance Orientation on Employees’ Task Performance

Based on implicit leadership theory, we examine the congruence effect of leader–follower power distance orientation (PDO) on follower trust in supervisor and work engagement, which in turn influences employees’ task performance. Results of polynomial regressions on 526 dyads supported the congruence effect hypothesis. The results show that (1) the congruence of leader–follower PDO leads to better performance; (2) under the condition of congruence, subordinate task performance is higher when leader–follower PDO matching in low–low ratings congruence than it is in high–high ratings congruence; (3) under the condition of asymmetrical incongruence, the follower had higher task performance when a leader’s PDO is lower than a follower’s PDO; (4) trust in supervisor and the work engagement mediate the effect of congruence of leader–follower PDO on employees’ task performance; (5) trust in supervisor also mediates the effect of congruence of leader–follower PDO on employees’ work engagement

Accurate Theoretical Models for Frequency Diverse Array Based Wireless Power Transmission

Wireless power transfer (WPT) is a well-known problem, and has received wide attention in the next generation industrial applications and consumer electronics. On the other hand, frequency diverse array (FDA) is a new concept with the ability to generate a range-angle dependent beampattern. Therefore, some researchers are engaged in designing WPT systems based on the FDA framework (FDA-WPT) instead of phased arrays. Unlike phased arrays, the FDA beampattern is time-variant. Therefore, existing beam collection efficiency models based on the phased array are not suitable for the FDA-WPT system. More importantly, the time-variant property of FDAs is usually ignored in the literature, and the system configuration of the target area where the power-harvesting end is located does not conform to the actual WPT scenario. In this paper, we derive and present accurate models of the FDA-WPT system. The power transfer performance of the corrected FDA-WPT system is then compared with the phased array based WPT system. Simulation results demonstrate that time-variant consideration in the FDA-WPT model causes difficulty in controlling the main beam direction to focus the power. The accurate FDA-WPT is theoretically investigated, and numerical simulations are implemented to validate the theoretical analysis

Accurate Theoretical Models for Frequency Diverse Array Based Wireless Power Transmission

Wireless power transfer (WPT) is a well-known problem, and has received wide attention in the next generation industrial applications and consumer electronics. On the other hand, frequency diverse array (FDA) is a new concept with the ability to generate a range-angle dependent beampattern. Therefore, some researchers are engaged in designing WPT systems based on the FDA framework (FDA-WPT) instead of phased arrays. Unlike phased arrays, the FDA beampattern is time-variant. Therefore, existing beam collection efficiency models based on the phased array are not suitable for the FDA-WPT system. More importantly, the time-variant property of FDAs is usually ignored in the literature, and the system configuration of the target area where the power-harvesting end is located does not conform to the actual WPT scenario. In this paper, we derive and present accurate models of the FDA-WPT system. The power transfer performance of the corrected FDA-WPT system is then compared with the phased array based WPT system. Simulation results demonstrate that time-variant consideration in the FDA-WPT model causes difficulty in controlling the main beam direction to focus the power. The accurate FDA-WPT is theoretically investigated, and numerical simulations are implemented to validate the theoretical analysis

Informal status and taking charge: the different roles of OBSE, P-J fit, and P-S fit

Status in an organization is considered a significant antecedent to an employee’s work-related behaviors. However, the relationship between knowledge workers’ informal status and “taking charge” has been ignored in previous human resource management research. Based on the self-consistency theory, this study examines the mechanisms underlying the influence of knowledge workers’ informal status on taking charge. Data were collected from 337 dyads of employees and their immediate supervisors in 24 enterprises and companies. The results of moderated-mediation analysis indicate organization-based self-esteem (OBSE) fully mediated the positive relationship between knowledge workers’ informal status and taking charge, whereas person-job fit (P-J fit) and person-supervisor fit (P-S fit) each moderated the relationship between knowledge workers’ informal status and OBSE, in addition to the indirect effect of knowledge workers’ informal status on taking charge. Specifically, the indirect effect was strongest when P-J fit or P-S fit was high. The theoretical and managerial implications of the findings, limitations of the study, and future research directions are discussed

Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation

Service recommendation is key to improving users’ online experience. The development of the Internet has accelerated the creation of many services, and whether users can obtain good experiences among the massive number of services mainly depends on the quality of service recommendation. It is commonly believed that deep learning has excellent nonlinear fitting ability in capturing the complex interactions between users and items. The advantage in learning intricacy relationships enables deep learning to become an important technology for present service recommendation. Recently, it is noticed that linear models can perform almost as well as the state-of-the-art deep learning models, suggesting that capturing linear relationships between users and items is also very important for recommender systems. Therefore, numerous deep learning systems combined with linear models have been proposed. However, existing models are incapable of considering the size of the embedding. When the embedding dimension is too large, it leads to overfitting and thus influences the model’s ability to capture linear relationships. In this paper, a neural network based on two-layer matrix factorization and multi-layer perceptron—Two-layer Matrix factorization and Multi-layer perceptron Neural Network (TMMNN)—is proposed. To solve the problem of overfitting caused by an oversized embedding dimension, multi-size embedding technology has been integrated into the model. Matrix factorization and the multi-layer perceptron are placed in the upper and lower layers respectively, and they both receive embedding vectors dynamically adjusted for dimensions. In the upper layer, the matrix factorization is responsible for receiving the embedding of users and items, capturing linear relationships, and then yielding the generated new vectors as input to the multi-layer perceptron in the lower layer. Compared to other previously proposed models, the experimental results on the standard datasets MovieLens 20M and MovieLens Latest show that the TMMNN model is evidently better in terms of prediction accuracy