The Internet of Things (IoT) enables seamless real-time monitoring and data exchange across distributed and heterogeneous environments with wireless sensor networks (WSNs). The open architecture and resource constraints of wireless sensor networks (WSNs) make them highly vulnerable to internal security threats caused by malicious or compromised nodes, particularly in Internet of Things (IoT) environments. To address this issue, we proposed Dynamic Trust Evaluation Model (DTEM), designed to provide a secure, scalable, and efficient framework for IoT-based WSNs. The proposed model identifies the role of trust management in routing, data aggregation, and intrusion detection, including trust-based protocols. DTEM incorporates a lightweight elliptic curve cryptography (ECC) mechanism to ensure secure communication, protect trust information from manipulation, and enhance overall system reliability. In addition, machine learning techniques are employed to improve malicious node classification accuracy. Component-wise analysis demonstrates that the dynamic trust evaluation forms the core detection mechanism, while ECC enhances communication security and machine learning improves malicious node classification accuracy. A large-scale network simulation is conducted to evaluate DTEM’s performance under various attack scenarios. Results demonstrate improved malicious node detection accuracy, higher packet delivery ratios, reduced energy consumption, and lower communication overheads. The proposed DTEM framework proves to be a robust and scalable solution for securing IoT-based wireless sensor networks, making it suitable for real-world applications.

The Internet of Vehicles (IoVs) has seen rapid development due to advances in advanced communication technologies. The 5-th Generation (5G) systems will be integrated into next-generation vehicles, enabling them to operate more efficiently by cooperating with the environment. The millimeter Wave (mmWave) technology is projected to provide a large bandwidth to meet future needs for more effective data rate communications. A viable approach for transferring raw sensor data among autonomous vehicles would be to use mmWave communication. This paper attracts various research interests in academic, indoor, and outdoor mmWave operations. This paper presents mmWave propagation measurements for indoor and outdoor at 66 GHz frequency for IoVs scenarios. The proposed model examines the equivalent path loss using Free-Space Path Loss (FSPL) based on the transmitter and receiver distances for indoor and outdoor communications of the vehicles. In the indoor scenario, path loss propagation has the lowest penetration loss, but it is ineffective in the outdoor scenario because distance increases as free space path loss increases. The probability of error is increased, concerning the transmitter and receiver distances due to propagation effect, packet collisions, busy receiver, and sensing threshold. The proposed methodology shows a higher packet delivery ratio and average throughput with less delay in the connection during transmission.