A Networked Sensor System for the Analysis of Plot-Scale Hydrology

Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations

This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of 11×11 nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors <2%. We provide a strategy to select the number of nodes to use in a WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error.

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

A smart city is a geographical area that uses modern technologies to facilitate the lives of its residents. Wireless sensor networks (WSNs) are important components of smart cities. Deploying IoT sensors in WSNs is a challenging aspect of network design. Sensor deployment is performed to achieve objectives like increasing coverage, strengthening connectivity, improving robustness, or increasing the lifetime of a given WSN. Therefore, a sensor deployment method must be carefully designed to achieve such objective functions without exceeding the available budget. This study introduces a novel deployment algorithm, called the Evaluated Delaunay Triangulation-based Deployment for Smart Cities (EDTD-SC), which targets not only sensor distribution, but also sink placement. Our algorithm utilizes Delaunay triangulation and k-means clustering to find optimal locations to improve coverage while maintaining connectivity and robustness with obstacles existence in sensing area. The EDTD-SC has been applied to real-world areas and cities, such as Midtown Manhattan in New York in the United States of America. The results show that the EDTD-SC outperforms random and regular deployments in terms of area coverage and end-to-end-delay by 29.6% and 29.7%, respectively. Further, it exhibits significant performance in terms of resilience to attacks.

Feasibility of Harvesting Solar Energy for Self-Powered Environmental Wireless Sensor Nodes

Energy harvesting has a vital role in building reliable Environmental Wireless Sensor Networks (EWSNs), without needing to replace a discharged battery. Solar energy is one of the main renewable energy sources that can be used to efficiently charge a battery. This paper introduces two solar energy harvesters and their power measurements at different light conditions in order to charge rechargeable AA batteries powering EWSN nodes. The first harvester is a primitive energy harvesting circuit that is built using elementary off-shelf components, while the second harvester is based on a commercial boost converter chip. To prove the effectiveness of harvesting solar energy, five EWSN nodes were distributed at a nature reserve (the Audubon Society of Western Pennsylvania, USA) and the sunlight at their locations was recorded for more than five months. For each recorded illumination, the corresponding harvested energy has been estimated and compared with the average energy consumption of the EWSN with the most power consumption. The results show that the daily harvested energy effectively compensates for the energy consumption of the EWSN nodes, and the battery charge capacity of 295 mAh can reliably support their daily dynamic energy consumption.

A Systematic Review of IoT Solutions for Smart Farming

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.