1558-1748 (c) 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2019.2940186, IEEE Sensors Journal > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 Abstract—Wireless sensor networks (WSNs) have received significant attention in the last few years in the agriculture field. Among the major challenges for sensor nodes’ deployment in agriculture is the path loss in the presence of dense grass or the height of trees. This results in degradation of communication link quality due to absorption, scattering, and attenuation through the crop’s foliage or trees. In this study, two new path-loss models were formulated based on the MATLAB curve-fitting tool for ZigBee WSN in a farm field. The path loss between the router node (mounted on a drone) and the coordinator node was modeled and derived based on the received signal strength indicator (RSSI) measurements with the particle swarm optimization (PSO) algorithm in the farm field. Two path-loss models were formulated based on exponential (EXP) and polynomial (POLY) functions. Both functions were combined with PSO, namely, the hybrid EXP- PSO and POLY-PSO algorithms, to find the optimal coefficients of functions that would result in accurate path-loss models. The results show that the hybrid EXP-PSO and POLY-PSO models noticeably improved the coefficient of determination (R 2 ) of the regression line, with the mean absolute error (MAE) found to be 1.6 and 2.7 dBm for EXP-PSO and POLY-PSO algorithms. The achieved R 2 in this study outperformed the previous state-of-the- art models. An accurate path-loss model is essential for smart agriculture application to determine the behavior of the propagated signals and to deploy the nodes in the WSN in a position that ensures data communication without unnecessary packets’ loss between nodes. Index Terms—correlation coefficient, exponential equation, farm field, path-loss model, polynomial equation, PSO, WSN, ZigBee I. INTRODUCTION he deployment of wireless sensor networks (WSNs) in agricultural surroundings needs to consider the radio wave propagation to determine accurately the sensor nodes’ locations in the farm field. In wireless propagation, path loss indicates the energy or power loss along the trajectory between the receiver and transmitter. At present, We indebted to Al-Rafidain University College for the financial support given to this research work. H. M. J. is with Al-Rafidain University College, Baghdad 10064 Iraq. He is now with the Department of Computer and Communication Engineering, Al- Rafidain University College, Filastin, Baghdad 10064, Iraq (e-mail: haider.jawad@ruc.edu.iq). A. M. J. is with the Department of Computer and Communication Engineering, Al-Rafidain University College, Filastin, Baghdad 10064, Iraq (e- mail: aqeel.jawad@ruc.edu.iq). Rosdiadee Nordin, Nor Fadzilah Abdullah and Mahamod Ismail is with the Centre of Advanced Electronic & Communication Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM wireless network engineers are working to improve wireless networks by enhancing path-loss models based on electromagnetic wave propagation. Consequently, several studies on the physical properties of radio channels have been performed to predict signal propagation [1]. Radio wave propagation occurs when the wireless signal travels through communication channels. Many channel impairments can influence the signal strength, which depends on the presence of obstacles in transmitting paths of wireless communication, such as walls, doors, and stairs, depending on environments [2]. Path loss has different models specific to certain scenarios or applications. These models comprise three types, namely, empirical, semi-deterministic, and deterministic models. The advantage of the empirical path-loss model compared with other types of path-loss models is their simplicity of implementation and the mathematical expressions that comprise all surroundings-related elements that can influence the wave propagation in practice. Connectivity to main nodes in stationary WSN diffused in large agricultural surroundings such as orchards and farms is restricted because of the large area of the field and extreme path loss. Further, a huge number of wireless nodes in the farm field and the nature of higher layers of communication procedures in terms of multicasting and power transmission lead to connections lost to those WSNs. In agricultural applications, when the signal is transmitted from the source nodes to the receiver node, it passes via several dense crops. Consequently, an adequate clearance area cannot be guaranteed and will lead to signal propagation attenuation, reflection, absorption, and scattering. In this situation, the communication link will be degraded, particularly when the signal propagates through dense crops. For that reason, the transmission link quality and spatial and temporal variables must be considered when the WSNs are deployed. The propagation performance of WSNs is directly related to the Bangi, Selangor 43600 (e-mail: adee@ukm.edu.my, fadzilah.abdullah@ukm.edu.my & mahamod@ukm.edu.my). Sadik Kamel Gharghan is with the Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq (e-mail: sadik.gharghan@mtu.edu.iq). M. J. Abu-AlShaeer is with the Department of Statistics, Al-Rafidain University College, Filastin, Baghdad 10064, Iraq (e-mail: mahmood.jawad@ruc.edu.iq). Accurate Empirical Path-loss Model Based on Particle Swarm Optimization for Wireless Sensor Networks in Smart Agriculture Haider Mahmood Jawad, Aqeel Mahmood Jawad, Rosdiadee Nordin, Sadik Kamel Gharghan * , Nor Fadzilah Abdullah, Mahamod Ismail, and Mahmood Jawad Abu-AlShaeer T