Low-energy adaptive clustering routing protocol is a low-power adaptive hierarchical routing protocol designed for IoT, which has the function of extending network life time. In order to balance the energy consumption of wireless sensor network nodes and extend the life cycle of the whole network, the cluster heads are randomly selected in the classical clustering LEACH algorithm of the wireless sensor network of the Internet of Things, and the residual energy, node position and node density of the nodes are not considered. The problem is solved by the research of LEACH protocol, the selection of cluster head and the communication mechanism between cluster head and Sink. An improved clustering routing algorithm of LEACH protocol is proposed. The simulation experiment proves that the improved LEACH algorithm has more energy consumption than the comparison algorithm, the lowest power consumption of the cluster head node, and better network connectivity and reliability.

This article considers the point-to-point tracking control problem for a class of unknown nonlinear discrete-time systems with output saturation. A novel data-driven finite-iteration learning control algorithm is proposed to achieve bounded tracking errors within limited iteration. First, considering the case that the model of the nonlinear discrete-time system is unknown, the relationship between the output of the system and the control inputs at these given points is derived using recursive evolution in the time domain. Then, the dynamic data-driven model of the system is established using iterative domain dynamic linearization techniques. Second, a finite finite-iteration learning algorithm based on the fractional power of error information is designed, and the finite-iteration convergence of the proposed algorithm is rigorously proven in theory. Finally, the effectiveness of the proposed method is validated by simulation results.

In this paper, a method for rationally allocating energy storage capacity in a high-permeability distribution network is proposed. By constructing a bi-level programming model, the optimal capacity of energy storage connected to the distribution network is allocated by considering the operating cost, load fluctuation, and battery charging and discharging strategy. By constructing four scenarios with energy storage in the distribution network with a photovoltaic permeability of 29%, it was found that the bi-level decision-making model proposed in this paper saves 2346.66 yuan and 2055.05 yuan, respectively, in daily operation cost compared to the scenario without energy storage and the scenario with single-layer energy storage. After accessing IEEE-33 nodes for simulation verification, it was found that the bi-level decision-making model proposed in this paper has a good inhibition effect on voltage fluctuation and load fluctuation after energy storage configuration. In addition, this paper analyzes the energy storage that can be accessed by photovoltaic distribution networks with different permeability and finds that when photovoltaic permeability reaches 45% and corresponding energy storage is configured, the economic and energy storage benefits of the system are the best.

In addressing the cold-start problem in individual location prediction, we introduce a novel bilateral-branch network to enhance prediction accuracy specifically for cold-start users, without sacrificing general performance. This network integrates a main-branch that analyzes mobility behavior across all users and a sampling-branch dedicated to cold-start users, both employing a shared encoder to ensure consistent learning of behavioral patterns. The shared encoder weights play a crucial role, facilitating the robust transfer of insights from active to cold-start users, thus bridging the information gap effectively. Enhanced by an adapter mechanism, this design ensures that cold-start users benefit directly from the established behavioral patterns of active users. Evaluated on two real-world datasets, our model not only exceeded the performance of eight advanced baselines but also achieved a notable 6.5% increase in Acc@5 for cold-start users, demonstrating its ability to ad eptly handle the challenges associated with cold-start user predictions.