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The efficient operation of smart farms relies on the precise monitoring of farm environmental information, necessitating the deployment of a large number of wireless sensors. These sensors must be integrated with their specific locations within the fields to ensure data accuracy. Therefore, efficiently and rapidly determining the positions of sensor nodes presents a significant challenge. To address this issue, this paper proposes a hybrid optimization DV-Hop localization algorithm based on the chaotic crested porcupine optimizer. The algorithm leverages the received signal strength indicator, combined with node hierarchical values, to achieve graded processing of the minimum number of hops. Polynomial fitting methods are employed to reduce the estimation distance error from the beacon nodes to unknown nodes. Finally, the chaotic optimization crested porcupine optimizer is designed for intelligent optimization. Simulation experiments verify the proposed algorithm's localization performance across different monitoring areas, varying beacon node ratios, and assorted communication radii. The simulation results demonstrate that the proposed algorithm effectively enhances node localization accuracy and significantly reduces localization errors compared to the results for other algorithms. In future work, we plan to consider the impact of algorithm complexity on the lifespan of wireless sensor networks and to further evaluate the algorithm in a pH monitoring system for farmland. [ABSTRACT FROM AUTHOR]

To increase the positioning accuracy of Distance Vector-Hop (DV-Hop) algorithm in non-uniform networks, an improved DV-Hop algorithm based on RSSI correction is proposed. The new algorithm first quantizes hops between two nodes by the ratio of the RSSI value between two nodes and the benchmark RSSI value, divides the hops continuously, calculates the average hop distance according to the Minimum Mean Square Error (MMSE) criterion of the best index based on the quantized hops, and then adds hop distance matching factor to the fitness function of each anchor node into the calculation of the hop distance fitness function to weight the fitness function. The change index value is introduced to obtain more accurate hop distance value, and then the estimation error of unknown node (UN) coordinate is modified by using the distance relationship between the UN and the nearest beacon node (BN), and the modified coordination position is further modified by using the triangle centroid to improve the accuracy of node positioning in the irregular network. The experimental results show that compared with the original DV-Hop, improved DV-Hop1, improved DV-Hop2 and improved DV-Hop3, the localization error of the improved algorithm in this paper is reduced by 58%, 45%, 34%, and 29%, respectively, on average, in the two network environments. Without increasing the hardware cost and energy consumption, the improved algorithm has excellent localization performance. [ABSTRACT FROM AUTHOR]

In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the media access control (MAC) layer to avoid collision of packets. Secondly, the discovered one-hop unknown nodes are divided into two sets, i.e. unknown nodes with direct communication, and with indirect communication for energy efficiency. In direct communication, source beacon nodes forward the information directly to the unknown nodes, but a common beacon node is selected for communication which reduces overall energy consumption during transmission in indirect communication. Finally, a correction factor is also introduced, and localized unknown nodes are upgraded into helper nodes for reducing the localization error. To analyze the efficiency and effectiveness of the proposed algorithm, various simulations are conducted and compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

Locating node technology, as the most fundamental component of wireless sensor networks (WSNs) and internet of things (IoT), is a pivotal problem. Distance vector-hop technique (DV-Hop) is frequently used for location node estimation in WSN, but it has a poor estimation precision. In this paper, a multi-objective DV-Hop localization algorithm based on NSGA-II is designed, called NSGA-II-DV-Hop. In NSGA-II-DV-Hop, a new multi-objective model is constructed, and an enhanced constraint strategy is adopted based on all beacon nodes to enhance the DV-Hop positioning estimation precision, and test four new complex network topologies. Simulation results demonstrate that the precision performance of NSGA-II-DV-Hop significantly outperforms than other algorithms, such as CS-DV-Hop, OCS-LC-DV-Hop, and MODE-DV-Hop algorithms. [ABSTRACT FROM AUTHOR]