The Internet of Things (IoT) has risen from ubiquitous computing to the Internet itself. Internet of vehicles (IoV) is the next emerging trend in IoT. We can build intelligent transportation systems (ITS) using IoV. However, overheads are imposed on IoV network due to a massive quantity of information being transferred from the devices connected in IoV. One such overhead is the network connection between the units of an IoV. To make an efficient ITS using IoV, optimization of network connectivity is required. A survey on network optimization in IoT and IoV is presented in this study. It also highlights the backdrop of IoT and IoV. This includes the applications, such as ITS with comparison to different advancements, optimization of the network, IoT discussions, along with categorization of algorithms. Some of the simulation tools are also explained which will help the research community to use those tools for pursuing research in IoV.

In Industry 5.0, predicting electric vehicle energy usage enhances efficiency and sustainability, optimizes charging infrastructure, and meets consumer demands. Leveraging IoT, AI, and analytics enables smart charging that aligns with renewable energy goals, addresses infrastructure limitations, and promotes sustainable transportation by improving user experience, cutting costs, and boosting trust in electric vehicles. This research aims to create a synthetic data set for electric vehicles using an enhanced Generative adversarial network model and, from that, predict the energy for charging electric vehicles using ensemble Machine Learning algorithms. The importance of more detailed features for the best-performing machine learning model has been done utilizing Explainable Artificial Intelligence, specifically, the Shapley Additive Explanations approach, to provide more understanding and derive the inter-dependency among features. The enhanced TemporalCharge Generative adversarial network model provides Skewness and Kurtosis values as -0.51 and -0.182, respectively, for synthetically generated data for the city of Berhampur, Odisha, India, which is very close to four-wheeler electric vehicle charging data of the city. Several plots illustrate the influence of key features on electric vehicle energy consumption during charging, which enhances user optimization, owner empowerment, and ecosystem sustainability.

Continuous monitoring of the chronic stress is an infeasible task for physicians and hence its diagnosis is also nontrivial. Recent development in biological sensor and wireless technology has attracted researchers to carry out research in wireless body sensor networks (WBSN). This has enabled medical science in improving real-time monitoring and maintaining human health in a better way. In this work, a system has been developed for sensing human stress, based on acquisition, processing and analysis of the Galvanic Skin Response (GSR) signal collected from human bodies. The system detects human stress condition using the GSR signal acquired by wearable biological sensing devices when the person is in sitting, standing or sleeping conditions using a wired environment. Also the system is capable of remotely sensing human stress (while the person is in remote place within the range of wireless medium) with wireless sensor network technology. Popular supervised binary classifiers have been used for classifying the GSR data from the physiological dataset available in literature into binary classes, i.e., stress condition or relaxed condition and the results are presented.

In this paper, a secure and efficient protocol for vehicular ad hoc networks has been proposed that ensures both message authentication and privacy preservation. As safety related message may contain life critical information, it is a necessity that the sender as well as the message are authentic. The proposed scheme is based on a secure elliptic curve digital signature algorithm approach. The proposed scheme supports conditional privacy, where the user's location can be revealed at the willingness of the user. Apart from this, the scheme is secure against attacks like DoS, Sybil and Grey/Black Hole attacks. From the comparison with previously proposed schemes, it is found that the proposed scheme as based on elliptic curve discrete logarithmic problem, outperforms existing algorithms based on integer factoring and discrete logarithmic problem.

In this paper a novel offline signature verification scheme has been proposed. The scheme is based on selecting 60 feature points from the geometric centre of the signature and compares them with the already trained feature points. The classification of the feature points utilizes statistical parameters like mean and variance. The suggested scheme discriminates between two types of originals and forged signatures. The method takes care of skill, simple and random forgeries. The objective of the work is to reduce the two vital parameters False Acceptance Rate (FAR) and False Rejection Rate (FRR) normally used in any signature verification scheme. In the end comparative analysis has been made with standard existing schemes.

Due to the exponential increase in work-from-home adoption, Internet of Things (IoT) networks are under threat of constant attacks from internal and external adversaries. Thus, intrusion detection (ID) has become a vital component while designing interconnected networks. Existing ID Models for IoT either work on static attacks or incorporate high-complexity models for the detection of dynamic network attacks. Moreover, most of these models are unable to scale under hybrid attack scenarios. The work suggests creating an effective Ensemble Learning-based attack detection System for the classification of Dynamic attacks to address these problems. Initially, the suggested methodology uses network logs to gather several data samples for various breaches. These samples are represented as multidomain feature sets including Gabor, entropy, wavelet, frequency, and correlation components among other components. A moth flame optimizer (MFO) is used to choose the extracted components and helps identify Feature variance sets with high interclass variances. The selected features are categorized into different attack classes via an ensemble of k nearest neighbors (kNN), support vector machine (SVM), logistic regression (LR), Naïve Bayes (NB), and multilayer perceptron (MLP) algorithms. The results obtained from these classifiers are further tuned via the use of a Q-Learning based dynamic attack identification process. This process identifies micro-attack signatures via explainable artificial intelligence (XAI) to re-train the feature extraction and selection layer, thereby assisting in the classification of hybrid dynamic attacks. The XAI layer is built using a combination of XceptionNet and Transfer learning, which assists in continuous enhancements in attack mitigation even during dynamic attacks. These procedures allow the suggested model can improve attack classification accuracy by 8.5%, precision by 4.9%, and recall by 6.4%, while reducing the complexity by 5.9% when compared with existing attack categorization techniques.