The shortage of water stands as a global challenge, prompting considerable focus on the management of water consumption and irrigation. The suggestion is to introduce a smart irrigation system based on wireless sensor networks (WSNs) aimed at minimizing water consumption while maintaining the quality of agricultural crops. In WSNs deployed in smart irrigation, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a new localization method for localizing unknown sensor nodes in WSN-based smart irrigation systems using estimated range measurements. The proposed method can accurately determine the positions of unknown nodes, even when they are located at a distance from anchors. It utilizes the Levenberg–Marquardt (LM) optimization algorithm to solve a nonlinear least-squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the proposed method, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes. Specifically, it achieves 19% and 58% improvement in estimation accuracy when compared to distance vector-hop (DV-Hop) and semidefinite relaxation-LM (SDR-LM) algorithms, respectively. Additionally, the method exhibits robustness against measurement noise and scalability for large-scale networks. Ultimately, integrating the proposed localization method into the smart irrigation system has the potential to achieve approximately 28% reduction in water consumption.

The selective mapping (SLM) scheme is one of the most popular peak-to-average power ratio (PAPR) reduction techniques proposed for multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. One of the major disadvantages of this scheme is the need for the transmission of side information (SI) bits to enable the receiver to recover the transmitted data. The authors present a small overhead SLM (s-SLM) scheme for space–time block coded (STBC) MIMO-OFDM systems. This proposed scheme improves the system bandwidth efficiency and achieves a significantly lower bit error rate (BER) than the individual SLM (i-SLM) and direct SLM (d-SLM) schemes. In addition, approximate expressions for the complementary cumulative distribution function (CCDF) of the PAPR and the average BER of the proposed s-SLM scheme are derived. The simulation results show that the proposed s-SLM scheme improves the detection probability of the SI bits and hence gives a better performance than the i-SLM and the d-SLM schemes.