Blood circulation depends critically on electrical activation, where any disturbance in the orderly pattern of the heart’s propagating wave of excitation can lead to arrhythmias. Diagnosis of arrhythmias using electrocardiograms (ECG) is widely used because they are a fast, inexpensive, and non-invasive tool. However, the randomness of arrhythmic events and the susceptibility of ECGs to noise leads to misdiagnosis of arrhythmias. In addition, manually diagnosing cardiac arrhythmias using ECG data is time-intensive and error-prone. With better training, deep learning (DL) could be a better alternative for fast and automatic classification. The present study introduces a novel deep learning architecture, specifically a one-dimensional convolutional neural network (1D-CNN), for the classification of cardiac arrhythmias. The model was trained and validated with real and noise-attenuated ECG signals from the MIT-BIH dataset. The main aim is to address the limitations of traditional electrocardiograms (ECG) in the diagnosis of arrhythmias, which can be affected by noise and randomness of events, leading to misdiagnosis and errors. To evaluate the model performance, the confusion matrix is used to calculate the model accuracy, precision, recall, f1 score, average and AUC-ROC. The experiment results demonstrate that the proposed model achieved outstanding performance, with 1.00 and 0.99 accuracies in the training and testing datasets, respectively, and can be a fast and automatic alternative for the diagnosis of arrhythmias.

Internet of Thing (IoT) is the most emerging technology in which all the objects in the real world can use the Internet to communicate with each other as parts of a single unified system. This eventually leads to the development of many smart applications such as smart cities, smart homes, smart healthcare, smart transportation, etc. Due to the fact that the IoT devices have limited resources, the cybersecurity approaches that relied on complex and long processing cryptography are not a good fit for these constrained devices. Moreover, the current IoT systems experience critical security vulnerabilities that include identifying which devices were affected, what data or services were accessed or compromised, and which users were impacted. The cybersecurity challenge in IoT systems is to find a solution for handling the identity of the user, things/objects and devices in a secure manner. This paper proposes an effective multifactor authentication (CMA) solution based on robust combiners of the hash functions implemented in the IoT devices. The proposed CMA solution mitigates the authentication vulnerabilities of IoT and defends against several types of attacks. Also, it achieves multi-property robustness and preserves the collision-resistance, the pseudo-randomness, the message authentication code, and the one-wayness. It also ensures the integrity, authenticity and availability of sensed data for the legitimate IoT devices. The simulation results show that CMA outperforms the TOTP in term of the authentication failure rate. Moreover, the evaluation of CMA shows an acceptable QoS measurement in terms of computation time overhead, throughput, and packet loss ratio.

Multimedia applications have become an essential part of our daily lives, and their use is flourishing day by day. The area of wireless sensor network is not an exception where the multimedia sensors are attracting the attention of the researchers increasingly, and it has shifted the focus from traditional scalar sensors to sensors equipped with multimedia devices. The multimedia sensors have the ability to capture video, image, audio, and scalar sensor data and deliver the multimedia content through sensors network. Due to the resource constraints nature of WSN introducing multimedia will add more challenges, so the protocols designed for multimedia wireless sensor network should be aware of the resource constraints nature of WSN and multimedia transmission requirement. This paper discusses the design challenges of routing protocols proposed for WMSN. A survey and comprehensive discussion are given for proposed protocols of WMSN followed by their limitations and features.

Vehicles serve as mobile nodes in a high-mobility MANET technique known as the vehicular ad hoc network (VANET), which is used in urban and rural areas as well as on highways. The VANET, based on 5G (5G-VANET), provides advanced facilities to the driving of vehicles such as reliable communication, less end-to-end latency, a higher data rate transmission, reasonable cost, and assured quality of experience (QoE) for delivered services. However, the crucial challenge with these recent technologies is to design a real-time multimedia traffic shaping that maintains smooth connectivity under the unpredictable change of channel capacity and data rate due to handover for rapid vehicle mobility among roadside units. This research proposes a smart real-time multimedia traffic shaping to control the amount and the rate of the traffic sent to the 5G-VANET based on distributed reinforcement learning (RMDRL). The proposed mechanism selects the accurate decisions of coding parameters such as quantization parameters, group of pictures, and frame rate that are used to manipulate the required traffic shaping of the multimedia stream on the 5G-VANET. Furthermore, the impact of the aforementioned three coding parameters has been comprehensively studied using five video clips to achieve the optimal traffic rate value for real-time multimedia streaming on 5G communication. The proposed algorithm outperforms the baseline traffic shaping in terms of peak-signal-to-noise-ratio (PSNR) and end-to-end frame delay. This research will open new comfortable facilities for vehicle manufacturing to enhance the data communication system on the 5G-VANET.