Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
Topology control is important for extending networks lifetime and reducing interference. The accuracy of topology identification plays a crucial role in topology control. Traditional passive interception can only identify the connectivity among cooperative sensor networks with known protocol. This paper proposes a novel method called Active Interfere and Passive Interception (AIPI) to identify the topology of non-cooperative sensor networks by using both active and passive interceptions. Active interception uses full duplex sensors to disrupt communication until frequency hopped to acquire distance information, and thus, infer their connectivity and calculate the location after modifying error in a non-cooperative sensor network. Passive interception uses Granger causality to infer the connectivity between two communication nodes after getting the time frame structure in physical layer. Passive interception is applied to conserve power consumption after obtaining physical information via active interception. Simulation results indicate that AIPI can identify the topology of non-cooperative sensor networks with a higher accuracy than traditional method....
To tackle the challenges arising from missing real-time measurement data and dynamic changes in network topology in optimizing and controlling distribution networks, this study proposes a data-driven collaborative optimization strategy tailored for multi-area clusters. Firstly, the distribution network is clustered based on electrical distance modularity and power balance indicators. Next, a collaborative optimization model for multiple area clusters is constructed with the objectives of minimizing node voltage deviations and active power losses. Then, a locally observable Markov decision model within the clusters is developed to characterize the relationship between the temporal operating states of the distribution network and the decision-making instructions. Using the Actor– Critic framework, the cluster agents are trained while considering the changes in cluster boundaries due to topology variations. A Critic network based on an attention encoder is designed to map the dynamically changing cluster observations to a fixed-dimensional space, enabling agents to learn control strategies under topology changes. Finally, case studies show the effectiveness and superiority of the proposed method....
This paper aims to solve the defects of the existing greenhouse environmental monitoring system, and proposes a monitoring scheme relying on the ZigBee wireless sensor network, which realizes the real-time tracking of greenhouse environmental data with the help of hardware and software cooperation. At the hardware level, the ZigBee wireless sensor network architecture is built with a CC2530 chip as the center, covering the sensor node and the sink node. The software level involves the data collection and transmission of the sensor node, the data receiving and forwarding of the sink node, and the monitoring and management of the host computer. After testing and verification, the system is stable and reliable, the overall structure is simple, the layout is flexible, and can effectively achieve the goal of wireless monitoring of greenhouse environmental data....
Wireless sensor networks (WSNs) encountered substantial obstacles in contexts characterized by frequent sensor node failures. Overcoming these obstacles requires a remedy that not only identifies node failures but also improves network self-organization. This work introduces a method that merges the Cuckoo Search Optimization algorithm (CSO) with the suggested Guided and Effective Search (GES) algorithm to improve the network’s ability to self-organize and maintain efficiency during node failures. The method combines CSO’s search capability for finding node configurations with GES’ effectiveness in local searches within the network structure. Together, they establish a system for fault detection network optimization, and improve self-organization, ensuring that the network could adapt and withstand disruptions. Comprehensive simulation results demonstrated the method’s superiority compared to the existing methods. The system demonstrates enhancements in fault detection accuracy, network self-organization, packet delivery rate, and overall energy efficiency. In addition, the simulation results highlight the improved performance of the combined approach compared to the Particle Swarm Optimization algorithm. Integrating CSO and GES marked advancement in creating self-organizing WSNs offers reliability and longevity for networks used in critical applications....
Creating a suitable growing environment is necessary to ensure good plant growth in a plant factory, which requires wireless sensor networks (WSNs) to monitor the environment in real time. However, existing WSN clustered routing methods hardly take into account the network unreliability caused by varying link quality among nodes, resulting in reduced stability and accuracy of environmental monitoring. This study proposes a wireless sensor network system strategy for improving network reliability in large-scale reliable wireless sensor networks suitable for plant factory scenarios. Firstly, a hybrid wireless sensor network was designed and built based on Wi-Fi and ZigBee communication protocols. Secondly, a nonlinear link quality prediction model for plant factory scenarios was developed using a function fitting method, taking into account the interference and attenuation caused by the dense concentration of agricultural facilities and plants in plant factories on the wireless signal propagation. Finally, a network coverage optimization scheme was designed by combining a particle swarm optimization (PSO) algorithm and link quality prediction model, and a reliable cluster routing protocol was designed by combining K-means algorithm. The results indicated that the coefficient of determination (R2) for the prediction model was 0.9962. The impact of agricultural facilities and vegetation on link quality was most significant when the node height was 0.7 m. Under the optimal node deployment, the number of nodes was 33, and the network coverage rate (CR) reached 97.512%. Compared with the traditional clustered routing method, the wireless sensor network designed in this study is more applicable to the field of plant factories; it further enhances data transmission effectiveness and link quality, improves the reliability of the network, and realizes the load balancing of the internal transmission of the network, which in turn ensures the accuracy of environmental monitoring and the stability of the system....
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