Abstract The advancements in the field of Information and Communication Technology (ICT) have led to the development of the Internet of Things (IoT), where ordinary things (e.g. smart meters, sensors etc.) can be connected and controlled over the internet. However, an IoT network is vulnerable to many malicious network attacks due to its inherent properties like limited computational abilities, heterogeneity, massive connectivity, etc. This paper presents a feature selection technique, which uses an amalgamation of LASSO regression and correlation based techniques, to find appropriate features for IoT network attack detection. The effectiveness of the proposed mechanism was evaluated on benchmark IoT network datasets using different machine learning techniques. The results revealed that the Gradient Boosting ensemble learning model achieved a maximum attack detection accuracy of 99.98%, and outperformed the other studied models.

Abstract Multi‐Factor Authentication (MFA) strengthens digital security by necessitating users to verify their identity. It uses various authentication methods like adding an extra layer of protection beyond conventional passwords. Proposed method introduces a novel MFA system that integrates multiple authentication layers, starting with two phase Graphical password with the traditional email‐password and progressing to facial recognition using Convolutional Neural Networks (CNN) and Quick response (QR) code authentication. To prove the robustness of our method, we are considering some test cases and few performance metrics like delay, accuracy, etc. The results are derived for False positive rates, complexity. The success rate is observed to be more than 93% for the proposed model.

Abstract Using traditional non‐virtualized Wireless Sensor Networks (WSNs) efficiently is difficult due to the embedded applications, which make the sensor nodes inaccessible to other applications. The proposed study considered both the node‐level and network‐level virtualization of wireless sensor networks to examine dynamic virtual network embedding. WSNs can leverage their shared sensing capabilities through network virtualization. Infrastructure providers earn more revenue by mapping more virtual network embedding (VNE) onto their substrate networks. VNE must therefore improve its acceptance ratio. The proposed RLE‐SVNE is demonstrated to be more efficient than state‐of‐the‐art in respect to acceptance, recovery, failure recovery delay, and revenue cost through simulation results. It compares the RLF‐SVNE method with C‐SVNE and N‐SVNE to demonstrate its superiority.

Abstract In order to enhance communication while minimizing complexity in 5G and beyond, MIMO‐OFDM systems need accurate channel prediction. In order to enhance channel prediction, decrease Error Vector Magnitude, Peak Power, and Adjacent Channel Leakage Ratio, this study employs the Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). Simplified clipping with filtering (SCF) reduces PAPR data, and this technique employs a PDACNN trained with the reduced data. By combining attention techniques with pyramidal dilated convolutions, the suggested PDACNN architecture is able to extract OFDM channel parameters across several scales. Attention approaches enhance channel prediction by allowing the model to dynamically concentrate on essential information. The primary objective is to make use of the network's ability to comprehend intricate spatial–temporal connections in OFDM channel data. The goal of these techniques is to make channel forecasts more accurate and resilient while decreasing concerns about EVM, Peak Power, and ACLR. To confirm the effectiveness of the suggested CP‐LSMIMO‐OFDM‐PDACNN, we measure its spectral efficiency, peak‐to‐average power ratio, bit error rate (BER), signal‐to‐noise ratio (SNR), and throughput. Throughput gains of 23.76%, 30.45%, and 18.97% are achieved via CP‐LSMIMO‐OFDM‐PDACNN, while bit error rates of 20.67%, 12.78%, and 19.56% are reduced. PAPRs of 21.66%, 23.09%, and 25.11% are also decreased.

Abstract Network intrusion detection refers to detect the threaten behaviors in the network to guarantee the network security. Compared with computer network, Internet of Things (IoT) consists of various devices, including computer, smart phone, smart watch, various sensors etc. The data in IoT may be captured from heterogeneous scenes using various devices. The data may follow from different distributions. Most previous works may fail when they are used in heterogeneous scenes of IoT. In order to overcome this issue, this paper designs a heterogeneous network intrusion detection scheme using attention sharing mechanism to implement domain adaptation for the intrusion detection of the data with heterogeneous distributions. The data from heterogeneous IoT devices is projected into the same sharing space via attention sharing to alleviate the bias between the distributions of data from these devices. Thus, the intrusion detection model learnt from the data from a scene can be migrated to another scene. The experiments and simulation demonstrate that the proposed intrusion detection scheme can adapt the changes of IoT scene.

Abstract Recent advances in Semantic IoT data integration have highlighted the importance of multimodal fusion in emotion recognition systems. Human emotions, formed through innate learning and communication, are often revealed through speech and facial expressions. In response, this study proposes a hidden Markov model‐based multimodal fusion emotion detection system, combining speech recognition with facial expressions to enhance emotion recognition rates. The integration of such emotion recognition systems with Semantic IoT data can offer unprecedented insights into human behavior and sentiment analysis, contributing to the advancement of data integration techniques in the context of the Internet of Things. Experimental findings indicate that in single‐modal emotion detection, speech recognition achieves a 76% accuracy rate, while facial expression recognition achieves 78%. However, when state information fusion is applied, the recognition rate increases to 95%, surpassing the national average by 19% and 17% for speech and facial expressions, respectively. This demonstrates the effectiveness of multimodal fusion in emotion recognition, leading to higher recognition rates and reduced workload compared to single‐modal approaches.

Abstract3GPP introduced carrier aggregation (CA) in Release‐10 to mitigate the increasing bandwidth demand of users. The efficacy of CA in meeting user demand for higher throughput in real‐life scenarios is an important consideration. This letter undertakes a simulation route using NS3 to understand the impact of variations in specific parameter values on the performance of CA in 3GPP‐specified scenarios. This work further establishes an empirical relation between throughput and some varying parameters. The work also shows that maintaining steady cell throughput in a densely populated cell comes at the cost of an individual user throughput. Simulation results exhibit that optimal CA design by tuning physical layer parameters alone cannot provide sustained throughput to the users.

Based on the conceptualization of terrestrial and non-terrestrial integrated networks (TaNTIN), we highlight artificial intelligence (AI), blockchains, tactile Internet, mobile edge computing (MEC)/fog computing, augmented reality and virtual reality, and so forth as the key features to ensure quality-of-service (QoS) guarantee of futuristic collaborative services.

Abstract As society evolves and the aging population grows, there is an increasing demand for healthcare. Traditional medical service models no longer suffice, leading to issues like uneven distribution of medical resources and varying medical standards. Wise Information Technology in Medicine (WITMED) platforms have emerged in response to this challenge. This study addresses the limitations of conventional medical service models, including resource imbalance, cumbersome procedures, and low informatization. Leveraging Medical Internet of Things (MIoT) and blockchain technology, we researched the development and enhancement of WITMED platforms. To assess the effectiveness of MIoT and blockchain‐based WITMED compared to traditional medical platforms, we evaluated data security, data interoperability, platform response time, and user satisfaction scores. The findings indicate that MIoT and blockchain‐based WITMED outperformed traditional platforms with a 0.13 higher user satisfaction score, improved data security and interoperability, and faster response times. WITMED based on MIoT and blockchain enhances efficiency, convenience, and personalization of medical services, ultimately elevating healthcare quality. Optimizing this platform should focus on improving user experience, data processing capabilities, performance, stability, response time, privacy protection, scalability, interoperability, and technical proficiency to provide superior medical services to users.

Abstract This paper employs full duplex (FD) and frequency reuse (FR) to improve the spectral efficiency while considering inter‐carrier interference (ICI) in 5G new radio vehicle‐to‐everything (NR‐V2X) networks. The quality of service of vehicle‐to‐vehicle (V2V) communications in such a system is affected by the self‐interference (SI) caused by FD, the co‐channel interference (CCI) caused by FR, and ICI caused by the Doppler effect. We present a performance analysis by proposing feasibility conditions for implementing FD and FR in NR‐V2X networks. We obtain a closed‐form expression for the maximum link distance between the transmitter and receiver of a link to provide reliable V2V communications. Our simulation results reveal the interplay between FD and FR technologies and demonstrate the performance gains of our proposed scheme compared to existing schemes in the literature.