Coverage optimization stands as a foundational challenge in Wireless Sensor Networks (WSNs), exerting a critical influence on monitoring fidelity and holistic network efficacy. Constrained by the limited energy budgets of sensor nodes, the imperative to maximize network longevity while sustaining sufficient coverage has ascended to the forefront of research priorities. Traditional deployment methodologies frequently falter in complex topographies and dynamic operational environments, encountering difficulties in striking an optimal equilibrium between coverage quality and energy efficiency. To mitigate these inherent limitations, this paper introduces ACDRL (Adaptive Coverage-Aware Deployment based on Deep Reinforcement Learning)—a novel strategy that enables intelligent, self-optimizing node placement in WSNs through deep reinforcement learning paradigms. Our proposed framework establishes a sophisticated deep reinforcement learning architecture integrating a multi-objective reward mechanism and hierarchical state representation, which innovatively resolves the dual predicaments of coverage optimization and energy balancing in intricate scenarios. Extensive simulation results validate that ACDRL consistently outperforms state-of-the-art approaches by maintaining superior coverage ratios, significantly extending network operational lifespan, and demonstrating enhanced adaptability in high-density deployment scenarios.

With the rapid advancement of agricultural technology, Agricultural Wireless Sensor Network (AWSN) monitoring for crop growth in large-scale fields has become pivotal in smart agriculture. Optimizing AWSN coverage is crucial for enhancing production efficiency and resource utilization. However, traditional optimization algorithms struggle with local convergence and accuracy in large-scale sensor deployment, an NP-hard problem. To address this, we propose a novel Multi-Strategy Pelican Optimization Algorithm (MSPOA), integrating a good point set strategy, a 3D spiral Lévy flight strategy, and an adaptive T-distribution variation strategy. The good point set strategy expands the search range and enhances local search capability, while the 3D spiral Lévy flight strategy improves convergence speed and global search accuracy. The adaptive T-distribution variation strategy further boosts global search ability, and pelican-inspired movement and collaboration strategies enhance adaptability and robustness in diverse agricultural scenarios. Comparative experiments with Improved Artificial Bee Colony Algorithm (IABC), Chaotic Adaptive Firefly Optimization Algorithm (CAFA), Adaptive Particle Swarm Optimization (APSO), and Lévy Flight Strategy Chaotic Snake Optimization Algorithm (LCSO) demonstrate that MSPOA improves network coverage by 5.85%, 11.33%, 21.05%, and 20.66%, respectively. Additionally, MSPOA exhibits strong adaptability and stability in dynamic agricultural environments.

Addressing the challenges of energy imbalance and the difficulty in optimizing cluster head selection in clustering protocols for wireless sensor networks (WSNs), this paper proposes a clustering protocol based on an improved zebra optimization algorithm (IZOACP). The method systematically solves the NP-hard problem of cluster head selection by integrating the zebra optimization algorithm (ZOA), Gaussian mutation strategy, and opposition-based learning mechanism, while optimizing the clustering process based on four key metrics: node residual energy, network density, intra-cluster distance, and communication delay. To further enhance data transmission efficiency, a dynamic adaptive inter-cluster routing mechanism is designed, which achieves path dynamic balancing based on node distance, residual energy, and load status. Experimental results demonstrate that, compared to the LEACH, DMaOWOA, and ARSH-FATI-CHS protocols, IZOACP significantly outperforms the comparison schemes in key metrics such as network lifespan (improved by 97.56%), throughput (improved by 93.88%), and transmission delay (reduced by 10.12%). These results validate its superiority in energy consumption control, topology stability, and large-scale monitoring scenarios, providing an efficient and reliable clustering optimization framework for WSN information monitoring systems.

The current industrial wireless sensor network (IWSN) cluster routing methods suffer from energy inefficiency. Designing efficient cluster-based routing protocols is crucial for improving network performance and energy efficiency. Therefore, this paper first designs a new clustering model to achieve efficient cluster head (CH) selection and data transmission performance by comprehensively considering multiple key factors such as CH energy, base station (BS) distance, packet loss rate, and data delay. Based on this clustering model, a novel cluster routing protocol based on Gaussian mutation adaptive artificial fish swarm algorithm (GAAFSA) is proposed. At the same time, a new Gaussian mutation strategy and an adaptive strategy were introduced to effectively promote the protocol to avoid local optima and prevent premature convergence. The GAAFSA based cluster routing protocol was experimentally compared with five popular schemes, namely CMSTR, D2CRP, EEHCHR, ESCVAD and BAFSA. The results showed that the proposed protocol outperformed the other four schemes in terms of network energy consumption, system lifetime, data transmission reliability, and latency. Specifically, GAAFSA has improved network lifespan by at least 15.68%, BS received packets by at least 7.46%, and reduced packet loss rate by at least 15.28%. Therefore, GAAFSA effectively optimizes network performance and extends network lifespan, greatly reducing energy loss within the network and significantly improving network quality of service (QoS).

Wireless Sensor Networks (WSNs), as a crucial component of the Internet of Things (IoT), are widely used in environmental monitoring, industrial control, and security surveillance. However, WSNs still face challenges such as inaccurate node clustering, low energy efficiency, and shortened network lifespan in practical deployments, which significantly limit their large-scale application. To address these issues, this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm (AC-ACO), aiming to optimize the energy utilization and system lifespan of WSNs. AC-ACO combines the path-planning capability of Ant Colony Optimization (ACO) with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability. By dynamically adjusting the pheromone evaporation factor and heuristic weights, efficient node clustering is achieved. Additionally, a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence. To validate the algorithm’s performance, this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy (LEACH), ACO, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization, network lifetime extension, and communication delay reduction, providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.

With the continuous development of society, agricultural production technology is also improving. Due to the continuous improvement of economic level, the quality of life has also put forward higher requirements, the quality of agricultural products has gradually received more people’s attention. In the picking and sorting process of traditional fragrant pear, the quality control process is not perfect, and the identification and extraction method is not mature enough. In the complex environment of insufficient light or shade of branches and leaves, the pear picking work cannot quickly and accurately locate the target fruit. Therefore, the traditional identification and extraction methods of fragrant pear were deeply explored in this paper. This paper analyzed its work flow and implementation methods, summarized the shortcomings of traditional recognition and extraction methods based on real activities, and put forward suggestions for improvement. In this paper, the orchard was divided into regions according to environmental factors; all kinds of information of fragrant pear growth were collected, and the information data was put into the measurement node for comprehensive calculation. It combined the Internet of Things and image detection technology to obtain the image data of the fragrant pear growing area in the orchard, and used the visual background extractor (Vibe) algorithm to preprocess, denoise and extract features of the fragrant pear image. In order to verify the practical effect of the optimized fragrant pear recognition and extraction method based on the Internet of Things and image detection technology in this paper, a comparative experiment was conducted. The results show that compared with the traditional pear recognition extraction method, the optimized pear recognition extraction method had better prediction effect in the process of pear image processing, and the recall rate and accuracy rate were increased by about 8% and 12.7%, respectively.