Outdoor node localization using random neural networks for large-scale urban IoT LoRa networks

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m

LoRaWAN Based Indoor Localization Using Random Neural Networks

Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%

Rice Data Systems for Sub-Saharan Africa: Contribution to the Japan-AfricaRice Emergency Rice Project

The rice data system for sub-Saharan Africa, which is a contribution to the Japan-AfricaRice Emergency Rice Initiative, is funded by the government of Japan. The project was coordinated at the regional level by Africa Rice Center (AfricaRice) and implemented at national levels by the national focal points. Throughout the course of the project implementation, the country focal points have made substantial and active contributions to ensure a smooth coordination of the project in-country activities. The national focal points are from the national agricultural research systems (NARS) and the national agricultural statistical services (NASS) of the countries that are members of the Coalition for African Rice Development (CARD). To ensure an effective communication between the project coordination unit and the countries, an intensive networking was established, which included sending of quarterly progress reports by the countries. Overall, the implementation of the project activities went well and it demonstrated the feasibility of building long-term collaborative working relationships between several national stakeholders to sustainably develop a multipurpose rice data systems. The surveys helped the countries develop well-structured rice statistical databases. Overall, the project activities went well and the national surveys were successfully conducted in the majority of the countries leading to the development of up-to-date and accessible rice data and information. In fact, as future project activities, the country teams will work to conduct in-depth analysis of the data collected in this project to update the national rice development strategies (NRDS), conduct rice research priority setting exercises and publish papers and policy briefs. AfricaRice discussed with the main donor in Japan who accepted that in-depth analysis of the data collected can continue after 30 April 2010, to publish the data in Google Map and transform the country reports into final and more comprehensive reports