With the widespread integration of large models into embodied intelligent agents, they are evolving from task executors to autonomous decision-makers and expanding into complex collaborative tasks. However, the increasing intelligence and autonomy of these agents pose significant challenges to the robustness of swarm intelligence collaboration. While blockchain can provide trusted collaboration support, traditional energy-intensive architectures are constrained by the energy limitations of embodied intelligent agents, potentially leading to performance bottlenecks. To address this issue, this paper proposes DDSR, a consensus mechanism based on Dynamic Swarm Reputation, to provide trusted service support for embodied intelligence collaboration. DDSR confines the consensus scope within the collaborative swarm and links the agents’ stakes and fault tolerance thresholds to swarm reputation, ensuring trusted collaboration while achieving lightweight consensus. Additionally, DDSR introduces a dynamic fault tolerance strategy based on swarm reputation, allowing the fault tolerance threshold to be adaptively adjusted according to the reputation performance within the swarm, thereby optimizing consensus efficiency and security. Experimental results demonstrate that DDSR enables fair reputation evaluation, ensures the stability and security of the consensus process, and enhances the robustness of swarm collaboration.
With the rapid growth of multimedia content and online gaming, traditional cloud computing faces difficulties in meeting real-time requirements, particularly in optimizing caching strategies, managing dynamic environments, and handling user location uncertainties. To overcome these limitations, we propose a novel AI-Enhanced Edge Cooperation framework based on Multi-Agent Deep Reinforcement Learning (EC-MADRL) to optimize scheduling and resource allocation across edge nodes. This framework enables adaptive caching and replenishment strategies in a cooperative environment, modeled as a multi-agent Markov Decision Process (MDP). By integrating an online MADRL approach, the EC-MADRL algorithm allows edge nodes to collaboratively learn optimal policies for caching and resource distribution. We analyze the time complexity and convergence of the algorithm, demonstrating its effectiveness in improving edge node performance. Experimental results show a 30.12
Black-box attacks in deep reinforcement learning typically involve training substitute policies to imitate the behavior of target policies, crafting adversarial examples, and using these transferable adversarial examples to attack target policies. Previous works primarily study the transferability on non-targeted setting. However, recent studies show defects that lead to the difficulty in generating transferable targeted examples: noise curing. To address the above issues, we introduce a novel approach for targeted attacks that effectively generates more transferable adversarial examples. Our proposed method utilizes the Poincaré distance as a similarity metric, which allows for self-adaptive gradient magnitudes during iterative attacks and helps alleviate issues related to noise curing. Furthermore, we incorporate metric learning into the targeted attack process to steer adversarial examples away from the true action and enhance the transferability of targeted adversarial examples.
There are existing medical high-value consumables traceability solutions based on the SPD model. The centralized storage method has business pain points such as data insecurity, incomplete data, and lagging data reporting. By integrating multiple technologies such as blockchain + RFID + big data analysis, an application solution of blockchain and RFID technology in the traceability of medical high-value consumables is developed. This application not only improves the management efficiency of high - value consumables in hospitals, but also ensures the authenticity and security of data, completes the assetization of data, can better empower the refined operation of hospitals, and proposes a new example of “data element + intelligent management” application. It has been verified through experiments that what would take one working day to enter into the system using traditional manual methods can be completed in just five seconds with the introduction of RFID technology, significantly improving work efficiency.
The existing edge server deployment algorithms predominantly focus on the locations of base stations, often overlooking user experience. To address this limitation, this study proposes an edge server placement algorithm based on spectral clustering and Q-learning (QSC). The algorithm not only considers the locations of base stations but also incorporates the number of users and the geographical positions of base stations, with the aim of achieving a balanced workload distribution across edge servers while minimizing the average user access latency. The process begins with using spectral clustering (SC) to determine initial cluster centers, followed by applying the Q-learning algorithm to refine these centers, which are then designated as the deployment positions for the edge servers. Experimental results demonstrate that, compared to the traditional K-means algorithm, the QSC algorithm reduces access latency by 10.04
