Abstract Performing a coronary disease diagnosis remotely is challenging now‐a‐days. COVID‐19 is a worldwide pandemic, and methods for detecting COVID‐19 are hampered by insufficient data and a lack of validation testing. Internet of Things (IoT) applications that rely on cloud computing (CC) are being studied in an effort to improve e‐Healthcare systems, even though CC presents substantial latency, bandwidth, energy consumption, security and privacy issues and so forth. The extension to CC, fog computing (FC), can overcome these said limitations. This study aims to diagnose COVID‐19 patients to fight the outbreak remotely. This study proposes IFCnCov, which enables remote users to diagnose COVID‐19 disease in real‐time using integrated IoT, FC, and CC principles, as well as ensemble learning (EL) and deep learning (DL). The proposed system is a two‐layered architecture, trained with DL approaches to two different datasets: a symptom‐based dataset and a chest x‐rays imaging dataset obtained from the Kaggle repository employing several evaluative measures. From various experiments, this proposed IFCnCov achieves comparatively enhanced accuracies of 97.71% and 98.64%, precision of 97.38% and 98.52%, sensitivity of 98.19% and 98.92%, specificity of 97.21% and 98.34%, and F1‐scores of 97.79% and 98.72% in the first and second stages respectively, which also outperforms some other considered state‐of‐the‐art works. Additionally, this work is validated in terms of several network parameters, including scalability, energy consumption, network utilization, jitter, processing time, throughput, and arbitration time.

In this research work, a novel IIoT attack detection framework is designed by following four major phases: pre-processing, imbalance processing, feature extraction, and attack detection. The attack detection is carried out using the projected ensemble classification framework. The projected ensemble classification framework encapsulates the recurrent neural network, CNN, and optimized bi-directional long short-term memory (BI-LSTM). The RNN and CNN in the ensemble classification framework is trained with the extracted features. The outcome acquired from RNN and CNN is utilized for training the optimized BI-LSTM model. The final outcome regarding the presence/absence of attacks in the industrial IoT is portrayed by the optimized BI-LSTM model. Therefore, the weight of BI-LSTM model is fine-tuned using the newly projected hybrid optimization model referred as cat mouse updated slime mould algorithm (CMUSMA). The projected hybrids the concepts of both the standard slime mould algorithm (SMA) and cat and mouse-based optimizer(CMBO), respectively.

In current years, drowsy driver detection is the most necessary procedure to prevent any road accidents, probably worldwide. The aim of this study was to construct a smart alert technique for building intelligent vehicles that can automatically avoid drowsy driver impairment. But drowsiness is a natural phenomenon in the human body that happens due to different factors. Hence, it is required to design a robust alert system to avoid the cause of the mishap. In this proposed paper, we address a drowsy driver alert system that has been developed using such a technique in which the Video Stream Processing (VSP) is analyzed by eye blink concept through an Eye Aspect Ratio (EAR) and Euclidean distance of the eye. Face landmark algorithm is also used as a proper way to eye detection. When the driver’s fatigue is detected, the IoT module issues a warning message along with impact of collision and location information, thereby alerting with the help of a voice speaking through the Raspberry Pi monitoring system.

With rapid growth of internet users and frequently emerging communication technology, the issues of using web as a worldwide platform and the requirement to design the smart applications to coordinate, discuss, register, and outline gradually emerges. Information transmission through a wireless network involves the radio signals, the arrangement of information packets, and the network topology. As each segment is correlated to each other, it is very essential to employ security mechanism in these components and real security control must be connected on them. Thus, security plays a critical factor in wireless network. This article highlights security issues in current wireless networks such as mobile ad-hoc network and IoT-supported networks and it also proposes a security-based S-RAID protocol design for security control in cluster based wireless networks. Simulation results show proficiency and better transmission rate of the proposal when it was compared with other similar approaches.

Nanotechnologies guarantee new potential outcomes for maintaining accessibility issues in versatile robots in an automated based Mobile Adhoc Networks (MANETs). In multi-robot frameworks, with a devastation situation, military applications, hunt and save operations and normal hardships, this issue is much more basic. Auspicious correspondence and sending control messages in a MANET which is reasonable for these intense circumstances starts with one robot then onto the next is a critical factor in every one of these operations. This makes it is a security issue in MANETs because of the auto-reconfiguration nature of the system which does not depend on any settled infrastructure-based foundation. There is dynamic change in its topology because of rapidly moving versatile nodes. This article features few security issues emerging because of network matter among robots and proposes a lively MANET-based automated convention called PD-ROBO with a committed Intrusion Detection System (IDS) structure that utilizations portable operator method to stay away from replay assault in mechanical based MANET.

A Mobile Ad Hoc Networks (MANETs) is an assortment of a variety of portable nodes that are linked collectively in a greater number in a wireless medium that has no permanent infrastructure. Here, all the nodes in the network act as both router and host and is in charge for accelerating and forwarding packets to other nodes. Due to the frequent link failure and high speed of mobile nodes, there are many challenges in MANET. As security is the basic challenge in a MANET, this article discusses the various protocols developed for security issues in MANET and IoT and presents a comparative analysis on their performance. After detailed analysis and comparison, it was observed that when security features are imposed at different levels during routing, various protocols perform in different ways. Also, the network performance and network life times vary from one approach to another. So, this article also presents the effect of security features on data drop and delay of prominent protocols after simulation.