Mobile edge computing (MEC), integrated with directed acyclic graph (DAG) blockchain technology, has emerged as a promising paradigm for ensuring secure and efficient resource trading between IoT user equipment (UEs) and edge service providers (ESPs). However, due to the open and heterogeneous nature of MEC networks, ESPs are susceptible to malicious attacks, rendering resource trading information potentially unreliable. While DAG blockchains ensure the reliability of on-chain data, they fail to guarantee the trustworthiness of ESPs and cannot effectively incentivize their participation in resource trading and blockchain consensus. To address these challenges, we develop a trust-driven resource trading framework for DAG blockchain-aided MEC networks. In this framework, we first design an off-chain trust-driven resource pricing mechanism, in which the resource price set by each ESP is positively correlated with its trust value. In addition, we design an on-chain trust-driven consensus mechanism, wherein the on-chain security of transactions published by each ESP is positively associated with its trust level. To enable UEs to better evaluate the on-chain transaction security, we design a novel metric termed transaction security satisfaction and incorporate it into the utility function of UEs. Furthermore, we model the resource trading between UEs and ESPs as a multi-leader multi-follower Stackelberg game, and verify the existence and uniqueness of its equilibrium. To maximize the utilities of both ESPs and UEs, we propose a backward induction-based iterative algorithm to jointly optimize resource pricing, resource demand, and offloading strategy. Numerical simulations validate the effectiveness of our proposed scheme, demonstrating its superior performance compared with baseline schemes.

Blockchain is considered as a promising technology to ensure the security of resource trading between the IoT user equipment (UEs) and the edge service providers (ESPs) in mobile edge computing (MEC) networks. However, blockchain cannot guarantee the trustworthiness of ESPs and cannot effectively incentivize ESPs to participate in resource trading and blockchain consensus. In addition, the high computational resource demands, energy consumption, and the limited transaction throughput of traditional blockchain pose challenges to IoT applications that require frequent micro transactions. To address these issues, we develop an integrated blockchain and MEC framework based on a directed acyclic graph (DAG) ledger to meet the demands of IoT applications. In this framework, we first model the resource trading between the UEs and the ESP as a multi-follower Stackelberg game. To incentivize the ESP to participate in resource trading and blockchain consensus, we design a trust based resource pricing mechanism, wherein the trust of ESP is evaluated by UEs and the ESP with higher trust can set a higher resource price. Additionally, to enable UEs to better assess the security of transactions on the DAG blockchain, we design a metric called transaction security satisfaction and adopt it as the revenue of UEs. Second, we verify the existence and uniqueness of the Stackelberg equilibrium. Furthermore, we propose a backward induction based iterative algorithm to optimize the resource pricing strategy for ESP and the resource demand strategy for UEs, while maximizing the utilities of both ESP and UEs. Numerical simulations demonstrate the effectiveness of our proposed scheme, showing its superiority over benchmark scheme in terms of transaction security satisfaction and the utilities of ESP and UEs.

Monte Carlo ray tracing algorithm is widely used by production quality renderers to generate synthesized images in films and TV programs. Noise artifact exists in synthetic images generated by Monte Carlo ray tracing methods. In this paper, a novel noise artifact detection and noise level representation method is proposed. We first apply discrete wavelet transform (DWT) on a synthetic image; the high frequency sub-bands of the DWT result encode the noise information. The sub-bands coefficients are then combined to generate a noise level description of the synthetic image, which is called noise map in the paper. This noise map is then subdivided into blocks for robust noise level metric calculation. Increasing the samples per pixel in Monte Carlo ray tracer can reduce the noise of a synthetic image to visually unnoticeable level. A noise-to-sample number mapping algorithm is thus performed on each block of the noise map, higher noise value is mapped to larger sample number, and lower noise value is mapped to smaller sample number, the result of mapping is called sample map. Each pixel in a sample map can be used by Monte Carlo ray tracer to reduce the noise level in the corresponding block of pixels in a synthetic image. However, this block based scheme produces blocky artifact as appeared in video and image compression algorithms. We use Gaussian filter to smooth the sample map, the result is adaptive sample map (ASP). ASP serves two purposes in rendering process; its statistics information can be used as noise level metric in synthetic image, and it can also be used by a Monte Carlo ray tracer to refine the synthetic image adaptively in order to reduce the noise to unnoticeable level but with less rendering time than the brute force method.

Watermarking embeds information into a digital signal like audio, image, or video. Reversible image watermarking can restore the original image without any distortion after the hidden data is extracted. In this paper, we present a novel reversible watermarking scheme using an interpolation technique, which can embed a large amount of covert data into images with imperceptible modification. Different from previous watermarking schemes, we utilize the interpolation-error, the difference between interpolation value and corresponding pixel value, to embed bit ¿1¿ or ¿0¿ by expanding it additively or leaving it unchanged. Due to the slight modification of pixels, high image quality is preserved. Experimental results also demonstrate that the proposed scheme can provide greater payload capacity and higher image fidelity compared with other state-of-the-art schemes.