Abstract-The physical layer of future wireless networks will be based on novel radio technologies such as UWB and MIMO. One of the important capabilities of such technologies is the ability to capture a few packets simultaneously. This capability has the potential to improve the performance of the MAC layer. However, we show that in networks with spatially distributed nodes, reusing backoff mechanisms originally designed for narrow-band systems (e.g., CSMA/CA) is inefficient. It is well known that when networks with spatially distributed nodes operate with such MAC protocols, the channel may be captured by nodes that are near the destination, leading to unfairness. We show that when the physical layer enables multipacket reception, the negative implications of reusing the legacy protocols include not only such unfairness, but also a significant throughput reduction. We present alternative backoff mechanisms and evaluate their performance via Markovian analysis, approximations, and simulation. We show that our alternative backoff mechanisms can improve both overall throughput and fairness.

Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techniques to identify and detect glioma, meningioma and pituitary brain tumors. The aim is to identify the performance of nine pre-trained TL classifiers, i.e., Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2, by automatically identifying and detecting brain tumors using a fine-grained classification approach. For this, the TL algorithms are evaluated on a baseline brain tumor classification (MRI) dataset, which is freely available on Kaggle. Additionally, all deep learning (DL) models are fine-tuned with their default values. The fine-grained classification experiment demonstrates that the inceptionresnetv2 TL algorithm performs better and achieves the highest accuracy in detecting and classifying glioma, meningioma and pituitary brain tumors, and hence it can be classified as the best classification algorithm. We achieve 98.91% accuracy, 98.28% precision, 99.75% recall and 99% F-measure values with the inceptionresnetv2 TL algorithm, which out-performs the other DL algorithms. Additionally, to ensure and validate the performance of TL classifiers, we compare the efficacy of the inceptionresnetv2 TL algorithm with hybrid approaches, in which we use convolutional neural networks (CNN) for deep feature extraction and a Support Vector Machine (SVM) for classification. Similarly, the experiment’s results show that TL algorithms, and inceptionresnetv2 in particular, out-perform the state-of-the-art DL algorithms in classifying brain MRI images into glioma, meningioma, and pituitary. The hybrid DL approaches used in the experiments are Mobilnetv2, Densenet201, Squeeznet, Alexnet, Googlenet, Inceptionv3, Resnet50, Resnet18, Resnet101, Xception, Inceptionresnetv3, VGG19 and Shufflenet.

360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user's familiarity with VR and user's interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.

Underwater Wireless Sensor Networks (UWSNs) have emerged as a remarkable interest for scholars worldwide in terms of various applications such as monitoring offshore oil and gas reservoirs, pollution, oceans for defense, and other applications such as tsunami. Terrestrial Wireless Sensor Networks (TWSN) and UWSNs share many characteristics apart from having different communication medium and working environment as UWSNs face the challenges of low-bandwidth, long latency, and high bit error rate. These have caused for UWSNs many problems such as low reliability, packet retransmission, and high consumption of energy. To alleviate the aforementioned issues, many techniques have been proposed. However, most of them merely consider the issue of hotspot which occurs due to the unbalanced transmission of load on sensor nodes near the surface sink. In this article, we propose a multi-layer cluster-based Energy Efficient (MLCEE) protocol for UWSNs to address the issue of hotspot and energy consumption. There are different stages in MLCEE, first of which is the division of the whole network in layers, the second is clustering of the nodes at same layers. In the last stage of transmission, the cluster head (CH) selects the next hop among the CHs based on greater fitness value, small Hopid and small layer number. To mitigate the issue of hotspot, the first layer remains un-clustered and any node in the first layer transfers data to the sink directly while cluster heads (CHs) are selected based on Bayesian Probability and residual energy. The simulation results of the proposed technique, done using MATLAB, have revealed that MLCEE achieves superior performance than the other techniques with regard to the network lifetime, energy consumption, and data transmission amount.INDEX TERMS Underwater wireless sensor networks (UWSNs), BN Bayesian probability (BN), network lifetime, dynamic clustering, cluster head (CH) selection, energy efficiency. I. INTRODUCTIONRecently, UWSNs are fascinating more considerations from industry and academia due to their comprehensive application fields, for example, auxiliary navigation, ecological observing, resource exploration, and calamities avoidance, etc. Underwater nodes are mostly deployed sparsely in the monitoring area from surface to bottom and these nodes are equipped with an acoustic modem. Communications through optical signals in underwater are not adequate dueThe associate editor coordinating the review of this manuscript and approving it for publication was Fatos Xhafa.