However, using drones results in other limitations. The third challenge is the consideration of cattle tracking and localization in smart farms based on WSNs [14]. WSNs are used to monitor animal behavior and positions, such as standing, walking, grazing, and lying down [15; 16]. In this context, several problems, such as animal situation and mobility, radio interference, changes in network topology, height of animal collar, signal penetration depth over the animal body, and antennas of access points, can arise and must be considered [17]. Robust tracking and localization techniques, such as smart tracking and localization that rely on intelligent techniques or optimization algorithms, can be adopted to overcome these challenges. The fourth challenge is propagation losses. When WSNs are used in agricultural applications, they must operate in different environments and climate conditions, such as bare land, ground, greenhouses, orchards, complex topography, and farms. These surroundings affect radio propagation performance. Consequently, simple or complex communication topologies in WSNs suffer from serious challenges. Transmission from one sensor node to another in agricultural application requires crossing over dense crops, where the clearance communication channel cannot be ensured. Therefore, communication link quality must be guaranteed when deploying WSNs. The performance of WSNs is associated with the working environment. Hence, an accurate modeling of wireless communication channel path loss must be established to represent propagation features. A path loss model can provide precise optimization and performance evaluation of a network during the distribution design process to improve the power consumption of nodes [18] and enhance localization and target detection [16], thereby ensuring the quality of services and minimizing the number of retransmission events in a network [19]. Reference [13] provided abundant information on the challenges and limitations of WSN deployment in agricultural application. The first challenge, namely, reducing the power consumption and extending the battery lifetime of sensor and router nodes in agricultural application, is addressed in the current study. Several energy-efficient methods and solutions, such as power reduction and energy-harvesting techniques, have been presented in the previous literature to solve the power consumption problem of sensor nodes. Among these solutions, the sleep/wake scheme and the combination of sleep/wake and data mitigation (redundant data) described as the sleep/wake on redundant data (_SWORD_) algorithm in this study are considered to minimize energy consumption and increase the lifetime of nodes in WSNs. Battery-operated radio modules consume the most power in sensor and router nodes. Therefore, the energy consumed by sensor nodes can be improved. Furthermore, battery life can be extended by reducing the amount of communication and minimizing redundant data in a network via the _SWORD_ algorithm. Accordingly, the deployed nodes are expected to work for months or years [20]. In addition, WSNs can be equipped with solar energy (based on a solar cell) to supply nodes with energy and to charge their batteries. The novelty of this study lies in the merging of the sleep/wake scheme with the redundant data on soil moisture, thereby resulting in a new algorithm, namely, _SWORD_. The _SWORD_ algorithm can considerably minimize the power consumption and data communication of a sensor node to 0.1 mA and 86.45%, respectively, relative to traditional operation. In addition, based on our literature review published previously in [13], we discovered that each power reduction techniques used in agricultural applications only relies on one method such as cluster a head, sleep/wake, data mitigation, transmission power control, data compression, data driven, routing protocol, and sink mobility schemes.