Recently, the Internet of Things (IoT) has gained tremendous popularity in several realms such as smart cities, healthcare, industrial automation, etc. IoT networks are increasing rapidly, containing heterogeneous devices that offer easy and user-friendly services via the internet. With the big shift to IoT technology, the security of IoT networks has become a primary concern, especially with the lack of intrinsic security mechanisms regarding the limited capabilities of IoT devices. Therefore, many studies have been interested in enhancing the security of IoT networks. IoT networks need a scalable, decentralized, and adaptive defense system. Although the area of development provides advanced security solutions using AI and Blockchain, there is no systematic and comprehensive study talking about the convergence between AI and Blockchain to secure IoT networks. In this paper, we focus on reviewing and comparing recent studies that have been proposed for detecting cybersecurity attacks in IoT environments. This paper address three research questions and highlights the research gaps and future directions. This paper aims to increase the knowledge base for enhancing IoT security, recommend future research, and suggest directions for future research.

As urbanization and industrial activities accelerate globally, air quality has become a pressing concern, particularly due to the harmful effects of particulate matter (PM), notably PM_2.5 and PM₁₀. This review paper presents a comprehensive systematic assessment of machine learning (ML) techniques for estimating PM concentrations, drawing on studies published from 2018 to 2024. Traditional statistical methods often fail to account for the complex dynamics of air pollution, leading to inaccurate predictions, especially during peak pollution events. In contrast, ML approaches have emerged as powerful tools that leverage large datasets to capture nonlinear, intricate relationships among various environmental, meteorological, and anthropogenic factors. This review synthesizes findings from 32 studies, demonstrating that ML techniques, particularly ensemble learning models, significantly enhance estimation accuracy. However, challenges remain, including data quality, the need for diverse and balanced datasets, issues related to feature selection, and spatial discontinuity. This paper identifies critical research gaps and proposes future directions to improve model robustness and applicability. By advancing the understanding of ML applications in air quality monitoring, this review seeks to contribute to developing effective strategies for mitigating air pollution and protecting public health.

Nowadays, the Internet of Things (IoT) has become immensely popular in various fields like healthcare, smart cities, and industrial automation. IoT networks are expanding rapidly, including different IoT devices with limited capabilities in terms of power and storage which make the IoT security a crucial issue. IoT Network Intrusion Detection System is one of the most famous solutions that used to identify different types of attack and extract their features (e.g. IP addresses of attackers). The IP address is a valuable feature that can identify malicious traffic of an attacker who attempts to access the IoT network. However, IoT Network Intrusion Detection Systems has different limitations: centralization and scalability which easily allow attackers to access the IoT network. Accordingly, this paper aims to address these issues by proposing a novel collaborative framework called Blockchain-based Collaborative Intrusion Detection Systems (BC-IDSs) that utilizes Blockchain technology to connect several IDSs. The BC-IDSs framework (1) creates a list of malicious IP addresses using IDSs; (2) utilizes Blockchain to share and store the Blacklist; (3) creates a function for duplication check in the Blockchain layer. Further, the implementation of a proof of concept for BC-IDSs framework is presented by using Ethereum Blockchain simulators. Compared to previous works, this paper discusses several types of performance metrics that prove BC-IDSs is able to secure IoT networks. BC-IDSs also increases the scalability by 50% when compared to one of the previous defence work.

The accelerated development of wireless network technology has resulted in the emergence of Wireless Body Area Network (WBAN), which is a technology commonly used in the medical field. WBAN consists of tiny sensor nodes that interconnect with each other and set in the human body to collect and transmit the patient data to the physician, to monitor the patients remotely. These nodes typically have limited battery energy that led to a shortage of network lifetime. Therefore, energy efficiency is considered one of the most demanding challenges in routing design for WBAN. Many proposed routing mechanisms in WBAN did not cover the source node energy and energy harvesting techniques. Therefore, this study proposes an Efficient Energy Aware Routing (EEAR) mechanism. This paper constructs a path cost function that considers three parameters: residual energy, number of hops to the sink, and the distance between the nodes. Besides, data aggregation with filtration and hybrid energy harvesting technique are used to extend the network lifetime, reduce the network traffic load, and maintain the source node energy. Extensive simulations using MATLAB have been performed to evaluate the performance of the proposed mechanism. EEAR is contrasted with the two latest schemes, called Priority-based Congestion-avoidance Routing Protocol (PCRP) and Energy Efficient Routing Protocol (EERP). The results show the significant performance of the EEAR mechanism in terms of network lifetime, residual energy, network stability, and throughput.

Locating available on-street parking in busy urban areas is often frustrating and time-consuming, particularly during peak hours. Inefficient parking searches contribute to traffic congestion, increased fuel consumption, and higher carbon emissions, making real-time parking availability information essential for smart urban mobility. This paper presents FedPark, a privacy-preserving crowdsensing system that leverages drivers’ smartphone sensors to monitor parking space occupancy. FedPark employs a Federated Learning (FL) algorithm, enabling the creation of a global model that aggregates insights from multiple local models maintained on individual drivers’ devices. Each local model learns from a user’s driving patterns without sharing personal data, ensuring enhanced privacy protection. Instead of transmitting raw data, only model updates are shared, reducing communication overhead and security risks. FedPark is evaluated under different urban conditions such as different numbers of drivers and destinations. The evaluation demonstrates that FedPark achieves a high level of accuracy at 98.31%, recall of 98.31%, precision of 98.34%, and an F1-score of 98.31% comparable to centralized machine learning models, including XGBoost, Random Forest, CatBoost, CNN, and ensemble classifiers while preserving driver privacy.