A large-scale Internet of Things (IoT) system based on sharded blockchain technology faces challenges, such as excessively high Cross-Shard Transaction (CST) ratios and imbalanced internal consensus. Current solutions include designing sharding protocols and consensus algorithms. However, these previous approaches solely focus on the outcomes of sharding, neglecting the intricate consensus costs incurred during the sharding process. This seriously affects the transaction latency of the system. Therefore, in this paper, we propose a latency optimization scheme. This scheme includes the sharding algorithm based on Weighting in K-means (W-K-means) clustering (namely WK-shard) and the Best Stable Committee (BSC) algorithm, aimed at addressing challenges in committee formation latency and internal consensus latency during the sharding process. Specifically, the WK-shard algorithm utilizes W-K-means clustering to balance the relationship between CSTs and intra-shard computility allocation. This ensures load balancing across shards while reducing CSTs, providing a strong basis for user nodes to select appropriate shards. Meanwhile, the BSC algorithm utilizes Markov chains to solve for the steady-state committee. The optimal utility of problem is explored through the dynamic state transitions of the schemes selected by different committees. A good selection scheme can reduce the total interval time of the system, effectively resolve the problem of laggards in internal consensus. We analyze the transaction lantency and validity degree of the latency optimization scheme through experiments, and compare it with other algorithms. The experimental results show that the proposed WK-shard algorithm reduces the committee formation latency by 15%, and the average validity degree of the BSC algorithm increases by 0.4 Transaction Per Second (TPS).

Due to their versatility and ease of movement, Unmanned Aerial Vehicles (UAVs) have become crucial tools in data collection for Wireless Sensor Networks (WSNs). While numerous UAV-based solutions exist, the focus often needs to be on optimizing flight trajectories and managing energy use, sometimes neglecting key factors affecting channel quality. In this article, we introduce a collaborative design framework designed to alleviate channel quality degradation caused by UAV flight distance in three-dimensional spaces. Our approach jointly optimizes UAV power schemes, positions, and flight trajectories. Firstly, we start by introducing a novel enhancing power model developed explicitly for rotary-wing UAVs gathering data, utilizing an alternating optimization method to achieve locally optimal solutions. Next, we frame an optimization problem aimed at maximizing the total average collection rate while achieving approximate optimal position relationships among UAVs. Additionally, we propose a new trajectory optimization model based on the Steiner Minimal Tree (SMT) concept, which is called the Circumcircle Steiner Minimal Tree Problem with Neighborhood (CSMTPN). Finally, we confirm our theoretical insights and numerical outcomes through extensive simulations demonstrating our framework’s effectiveness.