QoS based Radio Resource Management Techniques for Next Generation MU-MIMO WLANs: A Survey

IEEE 802.11 based Wireless Local Area Networks (WLANs) have emerged as a popular candidate that offers Internet services for wireless users. The demand of data traffic is increasing every day due to the increase in the use of multimedia applications, such as digital audio, video, and online gaming. With the inclusion of Physical Layer (PHY) technologies, such as the OFDM and MIMO, the current 802.11ac WLANs are claiming Gigabit speeds. Hence, the existing Medium Access Control (MAC) must be in a suitable position to convert the offered PHY data rates for efficient throughput. Further, the integration of cellular networks with WLANs requires unique changes at MAC layer. It is highly required to preserve the Quality of Service (QoS) in these scenarios. Fundamentally, many QoS issues arise from the problem of effective Radio Resource Management (RRM). Although IEEE 802.11 has lifted PHY layer aspects, there is a necessity to investigate MAC layer issues, such as resource utilization, scheduling, admission control and congestion control. In this survey, a literature overview of these techniques, namely the resource allocation and scheduling algorithms are briefly discussed in connection with the QoS at MAC layer. Further, some anticipated enhancements proposed for Multi-User Multiple-Input and Multiple-Output (MU-MIMO) WLANs are discussed

A Novel Approach for Disaster Victim Detection Under Debris Environments Using Decision Tree Algorithms With Deep Learning Features

Search and Rescue operations for victim identification in an unstructured collapsed building are high-risk and time-consuming. The possibility of saving a victim is high only during the first 48 hours, and then the prospect tends to zero. The faster the response and identification, the sooner the victim can be taken to medical assistance. Combining mobile robots with practical Artificial Intelligence (AI) driven Human Victim Detection (HVD) systems managed by professional teams can considerably reduce this problem. In this paper, we have developed a Transfer Learning-based Deep Learning approach to identify human victims under collapsed building environments by integrating machine learning classification algorithms. A custom-made human victim dataset was created with five class labels: head, hand, leg, upper body, and without the body. First, we extracted the class-wise features of the dataset using fine-tuning-based transfer learning on ResNet-50 deep learning model. The learned features of the model were then extracted, and then a feature selection was performed using J48 to study the impact of feature reduction in classification. Several decision tree algorithms, including decision stump, hoeffiding tree, J48, Linear Model Tree (LMT), Random Forest, Random Tree, Representative (REP) Tree, J48 graft, and other famous algorithms like LibSVM, Logistic regression, Multilayer perceptron, BayesNet, Naive Bayes are then used to perform the classification. The classification accuracy of the abovementioned algorithms is compared to recommend the optimal approach for real-time use. The random tree approach outperformed all other tree-based algorithms with a maximum classification accuracy of 99.53% and a computation time of 0.02 seconds