In order to address the issue of insufficient task offloading decisions in vehicle networks of transportation cyber-physical systems (TCPS) because of multitasking and resource constraints, this study presents a quasi-Newton deep reinforcement learning-based two-stage online offloading (QNRLO) algorithm. Computer simulation experiments show that the approach performs exceptionally well in terms of convergence under various conditions and parameter configurations. Most of the trials are carried out in a simulated setting, and further real-world scenarios may be required to confirm the algorithm’s efficacy. This methodology initially implements batch normalization techniques to enhance the training process of the deep neural network, subsequently utilizing the quasi-Newton method for optimization to successfully approximate the ideal answer. According to the experimental results, the QNRLO algorithm’s loss function and normalized computation rate have converged after 2,000 iterations, demonstrating the algorithm’s excellent stability and dependability. The findings demonstrate that the computational load and training time can be further optimized by appropriately adjusting certain parameters without compromising convergence performance. Furthermore, the technique incorporates system transmission time allocation into the TCPS model, hence augmenting the model’s practicality. The proposed approach markedly enhances the efficiency and stability of job offloading compared to previous algorithms, effectively addressing task offloading challenges in TCPS and exhibiting considerable applicability and reliability.

The satellite Internet of Things (IoT), utilizing Cyber-Physical Systems (CPS) Communication Networks and various satellite-borne and ground-based terminal devices, enables integrated human-object connectivity and information exchange across space, sky, and ground. A terminal authentication method based on trust evaluation is proposed to ensure the secure and efficient utilization of satellite IoT resources. This method optimizes authentication strategies by evaluating the trustworthiness of terminal devices. A satellite IoT terminal trust evaluation mechanism is proposed for scenarios where a previously strongly authenticated terminal needs re-authentication within a short time or where trust is granted via a strongly authenticated node. This mechanism describes dynamically adjusted trust metrics and evaluation models based on direct and indirect trust. A satellite IoT access authentication protocol based on trust evaluation is also designed, employing corresponding authentication mechanisms according to the device’s trust level. This simplifies the re-authentication process for highly trusted devices, enabling differentiated authentication services for various terminal devices within the same authentication framework. A comprehensive security analysis of the protocol is conducted using informal methods and the Tamarin formal verification tool. Experiments validate the protocol’s effectiveness, showing that it can reduce overall authentication costs and streamline the authentication process through trust-based simplifications.

Artificial Intelligence of Things (AIoT) edge computing has emerged as a critical enabler for consumer technology, easing the load on distribution grid networks through enhanced data transmission and processing capabilities. The Internet of Things (IoT), a modern network paradigm, connects devices using sensors and communication mediums to enable seamless information exchange. However, the limited computational capacity of edge nodes poses challenges in optimizing AIoT edge resources compared to cloud computing. To address this, a cloud-edge three-layer framework for task offloading and edge resource allocation is proposed in the context of consumer technology and distribution grids. This model incorporates random tasks, limited resources, unequal processing power, and high latency requirements. It features two key phases: a resource auction using a multi-round iterative process and compute offloading managed by a Deep Reinforcement Learning (DRL) approach. Enhanced algorithms such as Double DQN and Dueling DQN are integrated into the job offloading process to improve performance. Simulations validate the convergence of the task offloading algorithm and demonstrate that the proposed methods significantly enhance computational efficiency and resource utilization for edge nodes. These advancements offer promising solutions for the dynamic needs of consumer technology and future communication networks.

In response to the path planning problem of using Unmanned Aerial Vehicle (UAV) for blood transportation, with the objective of minimizing the total distance travelled by the UAV, a multi-constraint drone blood transportation path planning model is established. Taking into account the limited number of drone take-off and landing platforms and the safety time intervals for continuous drone take-off and landing, a drone take-off scheduling strategy is designed to reduce the total time spent by UAV completing transportation tasks. Additionally, an Imperial Competition Algorithm based on Imperialist Competitive Algorithm is proposed to solve this problem. This algorithm introduces a sine disturbance strategy and adds Imperial Reform stages to improve the search accuracy of the algorithm. It utilizes acceptance criteria related to solution quality to maintain the diversity of the population. Validation is conducted using benchmark examples and instances of drone blood transportation. The results indicate that the proposed algorithm can provide transportation solutions for drone blood transportation tasks that meet various constraints without any conflicts in drone take-off and landing. The drone take-off scheduling strategy effectively reduces the total time spent by UAV in completing tasks.

In the rapidly evolving field of healthcare 5.0, the Internet of Medical Things (IoMT) is expected to be an enabler that allows smart medical devices to collaborate and communicate with healthcare networks to speed up procedures, enhance care, and improve disease management. However, one of the critical issues for these networks still remains the secure and energy-efficient transmission of sensitive patient data. Thus, a novel security framework is proposed in this work, in which a Public Key Infrastructure- Energy-Efficient Routing Protocol (PKI-EERP) with a Zebra Optimization Algorithm (ZOA) is incorporated in spiking neural networks. The method combines data security robustness of the spiking neural networks to detect anomalies and check for access control purposes, with the PKI encryption to provide safe encryption and key management. The ZOA optimizes energy consumption in WSNs, and as a result transmission energy is significantly reduced up to 35 % compared to other implementations, and the network lifetime is increased by about 30 % through effective load balancing. It enhances both the privacy and energy efficiency that are essential for the safe and reliable operation of IoMT systems in contemporary healthcare environments, thus improving patient outcomes as well as standards of operations.

To overcome the issue of traditional blind signature schemes being vulnerable to quantum computing attacks during interactions over the consumer electronic Internet of Things (CIoT), a new blind signature scheme called CDBS has been developed. The post quantum digital signature technique CRYSTALS-Dilithium, which was standardized by NIST, serves as the foundation for the quantum-resistant blind signature scheme, CDBS, presented in this study. This system is appropriate for a variety of internet application scenarios, including e-ticketing, anonymous payments, and privacy protection, because it integrates the idea of blind signatures, which is not included in typical digital signature schemes. This scheme is designed to resist quantum computing attacks and is based on the post-quantum digital signature algorithm CRYSTALS-Dilithium, which NIST has selected. The whole technique utilizes the Fiat-Shamir signature structure, which consists of five stages: key creation, blinding, signing, unblinding, and verification. The security of the proposed scheme is dependent on the resolution of the Module Learning with Errors (MLWE) problem and the Short Integer Solution (SIS) problem. Additionally, the scheme must satisfy the criteria of correctness, blindness, and unforgeability. According to the test results, the CRYSTALS-Dilithium scheme requires an average of 401.89 μ s for a single round of verification and signing. The CDBS technique takes an average of 657.65 ls for one round of signing and verification since the blinding and unblinding modules are added on top of the original algorithm, increasing the algorithm’s complexity. The software testing results indicate that the average time required to complete a single full blind signature and verification operation is 657.65 microseconds. The suggested system offers a framework for expanding the CRYSTALS-Dilithium digital signature algorithm.