IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication, processing, and real-time monitoring across diverse applications. Due to their heterogeneous nature and constrained resources, as well as the growing trend of using smart gadgets, there are privacy and security issues that are not adequately managed by conventional security measures. This review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT ecosystems. The intersection of AI technologies, including ML, and blockchain, with IoT privacy and security is systematically examined, focusing on their efficacy in addressing core security issues. The methodology involves a detailed exploration of existing literature and research on AI-driven privacy-preserving security mechanisms in IoT. The reviewed solutions are categorized based on their ability to tackle specific security challenges. The review highlights key advancements, evaluates their practical applications, and identifies prevailing research gaps and challenges. The findings indicate that AI solutions, particularly those leveraging ML and blockchain, offer promising enhancements to IoT privacy and security by improving threat detection capabilities and ensuring data integrity. This paper highlights how AI technologies might strengthen IoT privacy and security and offer suggestions for upcoming studies intended to address enduring problems and improve the robustness of IoT networks.

Traffic in today’s cities is a serious problem that increases travel times, negatively affects the environment, and drains financial resources. This study presents an Artificial Intelligence (AI) augmented Mobile Ad Hoc Networks (MANETs) based real-time prediction paradigm for urban traffic challenges. MANETs are wireless networks that are based on mobile devices and may self-organize. The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts. This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network (CSFPNN) technique to assess real-time data acquired from various sources within the MANETs. The framework uses the proposed approach to learn from the data and create prediction models to detect possible traffic problems and their severity in real time. Real-time traffic prediction allows for proactive actions like resource allocation, dynamic route advice, and traffic signal optimization to reduce congestion. The framework supports effective decision-making, decreases travel time, lowers fuel use, and enhances overall urban mobility by giving timely information to pedestrians, drivers, and urban planners. Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy, responsiveness, and scalability. Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time, enables proactive traffic management, and aids in creating smarter, more sustainable cities.

Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.

Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item. From ancient times to the present, the security of secret or vital information has always been a significant problem. The development of secure communication methods that keep recipient-only data transmissions secret has always been an area of interest. Therefore, several approaches, including steganography, have been developed by researchers over time to enable safe data transit. In this review, we have discussed image steganography based on Discrete Cosine Transform (DCT) algorithm, etc. We have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman (RSA) method, the Blowfish technique, and the hash-least significant bit (LSB) approach. In this review, a novel method of hiding information in images has been developed with minimal variance in image bits, making our method secure and effective. A cryptography mechanism was also used in this strategy. Before encoding the data and embedding it into a carry image, this review verifies that it has been encrypted. Usually, embedded text in photos conveys crucial signals about the content. This review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data transport. If the message is ever intercepted by a third party, there are several ways to stop this operation. A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms.