With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization, its limitation for indoor environments is well known. Ultra-WideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environment.
Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network’s lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.
In the next generation of mobile communication networks, unprecedented challenges are required to be met, such as much higher data rates and spectrum efficiency, lower latency, and massive connectivity. Non-orthogonal multiple access (NOMA) has recently been proposed as a promising technology to achieve much superior spectral efficiency compared to conventional orthogonal multiple access techniques employed in present communication systems. A salient feature of NOMA is its use of successive interference cancellation (SIC) to decode users’ information when multiple users are allowed to transmit in same time/frequency/code domain. In this paper, we aim to exploit an aspect of SIC, namely the availability of other users’ data to realize a cooperative NOMA system. EXtrinsic information transfer (EXIT) charts are utilized to examine the performance of proposed system in terms of user fairness while employing IRregular convolutional codes (IRCC)s. The EXIT chart using IRCC evaluates the convergence analysis for the proposed system. Further, to evaluate the system performances in cooperative NOMA system, we have derived the expressions for the achievable rates which are obtained independently and utilized them in evaluating the experimental data for the proposed NOMA model.
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