An efficient water supply management system can be one of the applications of the Internet of Things (IoT). Water is a basic physiological need, and smart management of water plays a significant role in a smart city. This paper focuses on a mathematical model and IoT framework that aid in developing a smart city. A framework is developed for water supply management. The efficiency of the water supply can be measured by monitoring leakage conditions, overflow of water, automatic meter reading and online bill payments, and water consumption status of households, community, state, and eventually the whole country as well as the automatic water supply line cut-off. The system where the IoT is being deployed consists of embedded hardware in which sensors and microcontrollers provide messages and gain feedback from each other with the help of the internet, and this process can not only be monitored but also can be controlled from a remote location. The developed framework addresses all these aspects and mathematical equations are used and formulated while developing the IoT application. The mathematical equations are concentrated on consumption level (CL), leakage reporting (LR), and bill amount (BA) based on consumption. These become the point of contact for deploying IoT and eventually a framework is developed. This framework can be useful not only in water supply management but also in the management of road traffic, pollution, garbage, home automation and so on. In a nutshell, this paper illustrates the usage of the IoT framework in water supply management which contributes to developing the smart city.

Malware is any malicious program that can attack the security of other computer systems for various purposes. The threat of malware has significantly increased in recent years. To protect our computer systems, we need to analyze an executable file to decide whether it is malicious or not. In this paper, we propose two malware classification methods: malware classification using Simhash and PCA (MCSP), and malware classification using Simhash and linear transform (MCSLT). PCA uses the symmetrical covariance matrix. The former method combines Simhash encoding and PCA, and the latter combines Simhash encoding and linear transform layer. To verify the performance of our methods, we compared them with basic malware classification using Simhash and CNN (MCSC) using tanh and relu activation. We used a highly imbalanced dataset with 10,736 samples. As a result, our MCSP method showed the best performance with a maximum accuracy of 98.74% and an average accuracy of 98.59%. It showed an average F1 score of 99.2%. In addition, the MCSLT method showed better performance than MCSC in accuracy and F1 score.

In this paper, we propose an enhanced clustering protocol that integrates an improved K-means with a Mobility-Aware Cluster Head-Election Scored (IK-MACHES) algorithm, designed for extending the lifetime and operational efficiency of Wireless Sensor Network (WSN) with mobility. Variety approaches applying Low Energy Adaptive Clustering Hierarchy (LEACH) often struggle to manage optimal energy distribution due to their static clustering and limited cluster head (CH) selection criteria, primarily focusing on the proximity of residual energy or distance. Thus, this paper proposes an algorithm that takes into account both the residual energy of sensor nodes and the distance between the cluster’s central point to the base station (BS), which ultimately enhances the network’s lifetime. Additionally, our approach incorporates mobility considerations, enhancing the adaptability of the mobility environments, such as autonomous vehicular networks. Our proposed method first constructs the cluster’s configuration and then elects the CH applying an improved K-means clustering algorithm—one of the machine learning methods—integrated with a proposed IK-MACHES mechanism. Three CH scoring strategies in the proposed IK-MACHES protocol evaluate the residual energy of the nodes, their distance to the BS and the cluster central point, and relative node’s mobility. The simulation results demonstrate that the proposed approach improves performance in terms of the first node dead (FND) and 80% alive nodes metrics with mobility, compared to other LEACH protocols such as classical LEACH, LEACH-B, Improved-LEACH, LEACH with K-means, Particle Swarm Optimization (PSO), and LEACH-GK protocol, thereby enhancing network lifetime through optimal CH selection.

At present, cyber-attacks have become more critical and familiar, which appeals to a novel line of security defences to defend against them. Cyber Threat Intelligence (CTI) originated as a reputation in the frequently developing cybersecurity landscape, vital in safeguarding digital models. Understanding this domain develops over a complete study of intelligence’s numerous features and sources. Data sharing and analysis centres perform as alarms of collaboration, demonstrating the collective vigilance needed to oppose such developing attacks. Inspecting and gathering data about cyberattacks from tweets can effectively deliver critical perceptions of the threats, their effects, occurrence areas, and probable mitigation tactics. Existing study on cyberattack absences in establishing Artificial Intelligence (AI) based analytic solutions for delivering country-wide cyber-attack intelligence. Cyber planners at a domestic level need AI-based decision support models to determine a country’s cyber attitude or vigilance. This study designs and develops an Enhanced Threat Intelligence for Cybersecurity Using an Ensemble of Deep Leaning Models with Metaheuristic Optimization Algorithm (ETIC-EDLMOA) model. The presented ETIC-EDLMOA model’s main aim is to detect and mitigate network attacks in cybersecurity effectively. Initially, the ETIC-EDLMOA model undergoes a data pre-processing stage to ensure clean and structured input data for analysis. Besides, the Word2vec model is utilized for feature extraction. For the classification process, the ensemble of DL models is employed, including the recurrent neural network (RNN) method, long short-term memory (LSTM) model, and conditional variational autoencoders (CVAE) technique. Finally, the ensemble models’ hyperparameter fine-tuning process is performed using the Wolverine optimization algorithm (WoOA) technique. A comprehensive range of simulation analyses is conducted to ensure the improved performance of the ETIC-EDLMOA method on the CybAttT dataset. The comparison study of the ETIC-EDLMOA method illustrated a superior accuracy value of 98.51% over existing techniques.

One of the applications of neural networks is to predict the fault section results of traffic utilizing the combined model estimation of the fault section and self-learning models with smart sensors. The prediction of the fault section can autonomously develop the internal model of the network to fit the pre-entered “traffic accident” section data and predict the occurrence of traffic accident sections. In this paper, we propose the results of waiting time for traffic accidents in case of traffic accidents by using a neural network and fuzzy expert system, in comparison with existing algorithms and algorithms for determining traffic accidents. It is used to estimate or predict traffic accident reliability as well. Typically, the type of fault data collected is the number of faults (the number of faults recorded during a given time interval) or the time of fault (the time-of-fault data recorded when each fault occurred), and this can be utilized only for group data types, rather than the time-of-fault data type.