In the Industrial Internet of Things (IIoT), wireless sensor network (WSN) technology makes devices that communicate with each other. The information integrated from multiple data sources will be transformed into productivity. However, the clusters close to the base station take a considerable load over multi-hop transmission, and in this case, the lifetime of the industrial WSN is restricted. To solve this problem, a grid-based clustering algorithm via load analysis for IIoT is presented in this paper. First, the network load is quantitatively analyzed and then a load model is constructed. Furthermore, a set of expressions is deduced to indicate the network load distribution. It is concluded that the number of delivered packets in each level is related to the grid length at that level. The optimal grid length is obtained by solving polynomials to achieve the uniform energy consumption of nodes at each level. Finally, the network is partitioned into unequal grids according to the optimal cluster size and all the nodes of a grid are formed into a cluster. Results of the experiments show that compared with ACT, ER-HEED, and RUHEED, our algorithm balances energy depletion effectively and extends the whole network lifetime.

Internet of Things (IoT) enables the devices to exchange data with each other. The wireless sensor network is a key technology for making devices sensible and has been widely concerned. In the clustering routing protocol of wireless sensor networks, the cluster heads have high energy consumption rate since it undertakes data collection, fusion, and forwarding, which causes unbalanced energy consumption. Thus, the network lifetime is limited. In this paper, we present the contribution as follows. First, we propose an improved K-means algorithm to cluster the network and use the weighted evaluation function to optimize the cluster structure. Then, we select to either split or merge the cluster structure according to the evaluation results and in further to obtain a non-uniform clustering structure of the network. Second, in the data transmission phase, the data fusion mechanism is used to improve the energy utilization rate of cluster heads. Given the transmission delay problem caused by the data fusion, we propose a delay-optimized data fusion tree construction-based algorithm. When an active node selects the parent node, the distance and the energy factors are considered. The time slot allocation is optimized through constructing a data fusion tree, and the transmission delay is minimized. Finally, compared with other algorithms in the simulation section, the proposed algorithm can effectively reduce the energy consumption of the network, and the constructing data fusion tree decreases the transmission delay caused by the data fusion process. The service quality of the whole network is therefore improved. The proposed algorithm is suitable for the delay-constraint application of IoT.

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT${}^{*}$. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

The spatial correlation between the main and eavesdropping channels impacts the effectiveness of beamforming technology in securing the IoT communication system. This study proposes an IRS-assisted secure beamforming scheme (IRSSBS) for a multi-user IoT system under imperfect channel state information (CSI). The security issue, constrained by channel spatial correlation, is formulated as a non-convex optimization problem with probabilistic constraints. To address this, we introduce a novel Actor-Critic algorithm combined with a Generative Diffusion Model (AC-GDM). This approach jointly optimizes the base station (BS) precoding matrix and the intelligent reflecting surface (IRS) phase shift matrix, subject to constraints on transmit power and phase shifts. The AC-GDM algorithm utilizes a denoising process to recover the optimal beamforming solution from the Gaussian noise inherent in the multi-user wireless channel environment. Simulation results demonstrate that the minimum achievable secrecy rate of the IRS-SBS outperforms the artificial noise (AN) scheme and the beamforming (BF) scheme by approximately 1.9576 and 2.3596 bps/Hz, respectively. These results validate the effectiveness of IRS-SBS in significantly mitigating the impact of channel spatial correlation on the security of IoT communication systems.

The booming crowdsourced delivery leverages networks of local, part time couriers to meet growing customer demands for increasingly speedy shipping. By conducting city-wide low-cost delivery networks, crowd-sourced enabled instant delivery platforms have fought to figure out a way to offer instant shipping at low price. Nevertheless, limited ubiquitous computing infrastructures impede the discovery of equilibrium between cost reduction and sufficient shipping capacity. In this article, we present our endeavor towards understanding and designing a dynamic pricing framework based on a large-scale food-delivery platform. To reduce delivery fees in capacity overload regions and resolve the decline of service quality caused by insufficient shipping capacity, we address the challenge of quantifying the inter-regional shipping capacity disparities by accordingly applying a variety of techniques, including cooperative game theory, probability analysis and address entity recognition. In particular, we provide an $O(n^{2})$ algorithm for the cooperative game based cost allocation, of which the exponential computational complexity $O(n^{2}2^{n})$ is generally considered a major challenge in real-world applications. City-wide experiments in Nanjing and Shenzhen illustrate that we achieve a 10% decrease in delivery fees under overload shipping capacity and a 3% increase in completion rates in customer locations with poor service quality.