Autonomous 6G-enabled Vehicle Transportation System (VTS) is receiving significant attention from researchers to ensure robust and safe driving operations. 6G-supported communication technologies show remarkable advancements in several vehicular domains, including automated and accurate identification of lanes, vehicles, traffic signs, and obstacles within the vehicle’s proximity. Integrating IoT and AI technologies can leverage the extensive information gathered by Autonomous Vehicles (AVs) for precise vehicle detection. Despite the rapid advancements in object detection for completely visible objects from oncoming vehicles, detecting objects in low-visibility environments remains a challenging task, especially at night. This paper presents an efficient vehicle road cooperation method using automated real-time roadside object identification system by processing the captured video frames from LiDAR sensors. The developed method leverages the strength of the teacher-student-based distilled deep learning architecture for precisely identifying roadside objects. The teacher model utilizes the improved weighting factor to lower the false positives for better feature refinement. Meanwhile, the student model is equipped with convolutional attention and hierarchical feature fusion to capture balanced positional and semantic discriminative and multi-scale feature maps. Further, a gradient-based attention transfer mechanism has been utilized for significant knowledge transfer using attention maps from the teacher-to-student model to capture spatial feature information for night vision. Extensive experimental results demonstrate that the developed method overshadows the state-of-the-art object detection methods by achieving 41.45%, and 55.44% mAP and mAR, respectively. Additionally, the efficacy of the developed method has been validated by incorporating the different use cases in 6G-enabled VTS.

With the advancement of wireless communication, Internet of Things (IoT), and big data, high performance data analytic tools and algorithms are required. Data clustering, a promising analytic technique is widely used to solve the IoT and big-data-based problems, since it does not require labeled datasets. Recently, metaheuristic algorithms have been efficiently used to solve various clustering problems. However, to handle big datasets produced from IoT devices, these algorithm fail to respond within the desired time due to high computation cost. This article presents a new metaheuristic-based clustering method to solve the big data problems by leveraging the strength of MapReduce. The proposed methods leverages the searching potential of military dog squad to find the optimal centroids and MapReduce architecture to handle the big datasets. The optimization efficacy the proposed method is validated against 17 benchmark functions, and the results are compared with five other recent algorithms, namely, bat, particle swarm optimization, artificial bee colony, multiverse optimization, and whale optimization algorithm. Furthermore, a parallel version of the proposed method is introduced using MapReduce [MapReduce-based MDBO (MR-MDBO)] for clustering the big datasets produced from industrial IoT. Moreover, the performance of MR-MDBO is studied on two benchmark UCI datasets and three real IoT-based datasets produced from industry. The F-measure and computation time of the MR-MDBO is compared with the six other state-of-the-art methods. The experimental results witness that the proposed MR-MDBO-based clustering outperforms the other considered algorithms in terms of clustering accuracy and computation times.

Sentiment analysis is an eminent part of data mining for the investigation of user perception. Twitter is one of the popular social platforms for expressing thoughts in the form of tweets. Nowadays, tweets are widely used for analyzing the sentiments of the users, and utilized for decision making purposes. Though clustering and classification methods are used for the twitter sentiment analysis, meta-heuristic based clustering methods has witnessed better performance due to subjective nature of tweets. However, sequential meta-heuristic based clustering methods are computation intensive for large scale datasets. Therefore, in this paper, a novel MapReduce based K-means biogeography based optimizer(MR-KBBO) is proposed to leverage the strength of biogeography based optimizer with MapReduce model to efficiently cluster the large scale data. The proposed method is validated against four state-of-the-art MapReduce based clustering methods namely; parallel K-means, parallel K-means particle swarm optimization, MapReduce based artificial bee colony optimization, dynamic frequency based parallel k-bat algorithm on four large scale twitter datasets. Further, speedup measure is used to illustrate the computation performance on varying number of nodes. Experimental results demonstrate that the proposed method is efficient in sentiment mining for the large scale twitter datasets.