This paper presents a novel approach for wireless Virtual Reality (VR) systems by integrating mobile crowdsensing with Deep Reinforcement Learning (DRL). In modern VR applications, ensuring optimal performance while managing system resources presents challenges including high bandwidth requirements, low Motion-to-Photon delay, and intensive computational demands. We address these challenges by proposing a DRL-based framework that jointly optimizes rendering strategies (local, remote, or collaborative) and user-SBS associations to maximize Quality of Experience while adhering to strict latency constraints. To overcome sparse feedback in complex wireless VR environments, we introduce a two-stage approach combining auction-based IoT-VR pairing with diffusion reasoning-enhanced DRL using the Diffusion Reasoning-based Reward Shaping Scheme (DRESS). Our energy model captures rendering-specific power consumption patterns across different strategies. Simulation results demonstrate that our diffusion-enhanced approach (PPO-RS+DF) achieves superior reward convergence and significantly lower latency compared to baseline methods. The diffusion mechanism effectively propagates sparse reward signals across the state-action space, enabling efficient learning from limited feedback and guiding the learning process toward latency-optimized policies for next-generation wireless VR applications.

With the advancement of 5G networks, the deploy-ment of Internet of Things (IoT) technology has seen significant growth. Blockchain technology, recognized for its strong security features, is increasingly utilized within the IoT domain. However, the current IoT landscape is characterized by challenges such as substantial resource consumption, limited throughput capacity, and insufficient security protocols, which hinder its optimal per-formance. Towards addressing such problems, we propose a con-sensus algorithm called Proof-of-GoS (PoG) based on the grade of service (GoS), in which a node must have a service score over a set score threshold to be allowed to join the consensus process. The correct behavior of a node results in a reward, while any malicious actions result in penalties. Finally, we simulate a network to evalu-ate the performance and security of PoG and compare it with sev-eral existing consensus algorithms. The experimental findings in-dicate that the proposed PoG consensus algorithm retains the fun-damental security properties of blockchain and outperforms the state-of-the-art consensus mechanisms.

Virtual reality (VR) applications are becoming increasingly popular in both entertainment and commercial industries. Improving the immersive experience is essential to meet the expectations of users. To achieve the highest quality of experience (QoE), various factors such as delay, refresh rate, and data rate must be considered. Streaming only the field of view (FoV) of users based on gaze movement is a common method to improve the performance of end-to-end (E2E) delay. In this paper, we propose a novel non-overlapped tile streaming approach with dynamic resource allocation in VR networks to enhance the E2E delay with efficient resource block (RB) utilization. Our approach exploits the existence of overlapping tiles in consecutive FoV requests for reducing the number of tiles that need to be transmitted to users. By combining the non-overlapped tile streaming scheme with the flexible resource allocation, our proposed approach provides VR users with an enhanced QoE, particularly in terms of shorter E2E delay.

Binding calls of embedded scripting engines introduce a serious attack surface in PDF readers. To effectively test binding calls, the knowledge of parameter types is necessary. Unfortunately, due to the absence or incompleteness of documentation and the lack of sufficient samples, automatic type reasoning for binding call parameters is a big challenge. In this paper, we propose a novel operand-variation-oriented differential analysis approach, which automatically extracts features from execution traces as oracles for inferring parameter types. In particular, the parameter types of a binding call are inferred by executing the binding call with different values of different types and investigating which types cause an expected effect on the instruction operands. The inferred type information is used to guide the test generation in fuzzing. Through the evaluation on two popular PDF readers (Adobe Reader and Foxit Reader), we demonstrated the accuracy of our type reasoning method and the effectiveness of the inferred type information for improving fuzzing in both code coverage and vulnerability discovery. We found 38 previously unknown security vulnerabilities, 26 of which were certified with CVE numbers.