Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead to a highly effective, green agriculture. Based on PA management, the same routine to a crop regardless of site environments can be avoided. From several perspectives, field management can improve PA, including the provision of adequate nutrients for crops and the wastage of pesticides for the effective control of weeds, pests, and diseases. This review outlines the recent applications of WSNs in agriculture research as well as classifies and compares various wireless communication protocols, the taxonomy of energy-efficient and energy harvesting techniques for WSNs that can be used in agricultural monitoring systems, and comparison between early research works on agriculture-based WSNs. The challenges and limitations of WSNs in the agricultural domain are explored, and several power reduction and agricultural management techniques for long-term monitoring are highlighted. These approaches may also increase the number of opportunities for processing Internet of Things (IoT) data.

Traditional power supply cords have become less important because they prevent large-scale utilization and mobility. In addition, the use of batteries as a substitute for power cords is not an optimal solution because batteries have a short lifetime, thereby increasing the cost, weight, and ecological footprint of the hardware implementation. Their recharging or replacement is impractical and incurs operational costs. Recent progress has allowed electromagnetic wave energy to be transferred from power sources (i.e., transmitters) to destinations (i.e., receivers) wirelessly, the so-called wireless power transfer (WPT) technique. New developments in WPT technique motivate new avenues of research in different applications. Recently, WPT has been used in mobile phones, electric vehicles, medical implants, wireless sensor network, unmanned aerial vehicles, and so on. This review highlights up-to-date studies that are specific to near-field WPT, which include the classification, comparison, and potential applications of these techniques in the real world. In addition, limitations and challenges of these techniques are highlighted at the end of the article.

This paper aims to determine the distance between the mobile sensor node (i.e., bicycle) and the anchor node (i.e., coach) in outdoor and indoor environments. Two approaches were considered to estimate such a distance. The first approach was based on the traditional channel propagation model that used the log-normal shadowing model (LNSM), while the second approach was based on a proposed hybrid particle swarm optimization-artificial neural network (PSO-ANN) algorithm to improve the distance estimation accuracy of the mobile node. The first method estimated the distance according to the LNSM and the measured received signal strength indicator (RSSI) of the anchor node, which in turn used the ZigBee wireless protocol. The LNSM parameters were measured based on the RSSI measurements in both outdoor and indoor environments. A feed-forward neural network type and the Levenberg-Marquardt training algorithm were used to estimate the distance between the mobile node and the coach. The hybrid PSO-ANN algorithm significantly improved the distance estimation accuracy more than the traditional LNSM method without additional components. The hybrid PSO-ANN algorithm achieved a mean absolute error of 0.022 and 0.208 m for outdoor and indoor environments, respectively. The effect of anchor node density on localization accuracy was also investigated in the indoor environment.

Drones can be used in agriculture applications to monitor crop yield and climate conditions and to extend the communication range of wireless sensor networks in monitoring areas. However, monitoring the climate conditions in agriculture applications faces challenges and limitations, such as drone flight time, power consumption, and communication distance, which are addressed in this study. Wireless power transfer (WPT) can be used to charge drone batteries. WPT using a magnetic resonant coupling (MRC) technique was considered in this study because it allows high transfer power and efficiency with tens of centimeters, power transfers can be achieved in misalignment situations, charging several devices simultaneously, and unaffected by weather conditions. WPT was practically implemented based on a solar cell using a proposed flat spiral coil (FSC) in the transmitter circuit and multiturn coil (MTC) in a receiver circuit (drone) for the alignment and misalignment of two coils at different distances. FSC and MTC improved power transfer and efficiency to 20.46 W and 85.25%, respectively, at 0 cm with the loaded system under alignment condition. In addition, the two coils achieved appropriate transfer efficiencies and power for charging the drone battery under misaligned conditions. The maximum power transfer and efficiency were 17.1 W and 71% for the misalignment condition, at an air gap of 1 cm between two coils when the system was loaded with the drone battery. Moreover, the battery life of the drone was extended to 851 minutes based on the proposed sleep/active strategy relative to the traditional operation (i.e., 25.84 minutes). Consequently, a 96.9% battery power saving was achieved based on this strategy. Comparison results showed that the proposed system outperformed some present techniques in terms of the transfer power, transfer efficiency, and drone battery life. The proposed WPT technique developed in this study has been proven to solve the misalignment issue. Thus it offers a great opportunity as a key deployment component for the automation of farming practices toward the Internet of Farming applications.

Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty that results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using empirical mode decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the artificial neural network (ANN) model was optimised by an up-to-date slime mould algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms a multi-verse optimiser and backtracking search algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand.