Practical Experiences of a Smart Livestock Location Monitoring System leveraging GNSS, LoRaWAN and Cloud Services.

Livestock farming is, in most cases in Europe, unsupervised, thus making it difficult to ensure adequate control of the position of the animals for the improvement of animal welfare. In addition, the geographical areas involved in livestock grazing usually have difficult access with harsh orography and lack of communications infrastructure, thus the need to provide a low-power livestock localization and monitoring system is of paramount importance, which is crucial not for a sustainable agriculture, but also for the protection of native breeds and meats thanks to their controlled supervision. In this context, this work presents an Internet of things (IoT)-based system integrating low-power wide area (LPWA) technology, cloud and virtualization services to provide real-time livestock location monitoring. Taking into account the constraints coming from the environment in terms of energy supply and network connectivity, our proposed system is based on a wearable device equipped with inertial sensors, Global Positioning System (GPS) receiver and LoRaWAN transceiver, which can provide a satisfactory compromise between performance, cost and energy consumption. At first, this article provides the state-of-the-art localization techniques and technologies applied to smart livestock. Then, we proceed to provide the hardware and firmware co-design to achieve very low energy consumption, thus providing a significant positive impact to the battery life. The proposed platform has been evaluated in a pilot test in the Northern part of Italy, evaluating different configurations in terms of sampling period, experimental duration and number of devices. The results are analyzed and discussed for packe delivery ratio, energy consumption, localization accuracy, battery discharge measurement and delay

LoRaWAN geo-tracking using map matching and compass sensor fusion

In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate "tracking-on-demand" mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution

Mekanisme Peningkatan Reciprocity Channel Probing pada LoRaWAN Menggunakan Savitzky Golay Filter

Pada komunikasi Wireless seperti LoRa perlu adanya pembangkitan kunci untuk pengamanan data. Hal ini dilakukan agar data yang dikirimkan tidak mudah di serang oleh attacker. Salah satu cara mendapatkan kunci yang tepat adalah dengan pengkondisian nilai koefisien korelasi RSSI yang tinggi. Pada penelitian ini dibuat sebuah sistem untuk meningkatkan Reciprocity Channel Probing agar didapatkan nilai koefisien korelasi yang tinggi. Sistem dirancang dengan menggunakan metode Savitzky Golay Filter. Pengujian dilakukan pada dua kondisi yaitu indoor dan outdoor, dan dengan menggunakan nilai Spreading Factor dari SF-7 sampai SF-10. Hasil koefisien korelasi pengukuran menunjukkan bahwa pada kondisi outdoor lebih baik dibandingkan kondisi indoor. Nilai koefisien korelasi pengukuran tertinggi pada kondisi indoor yaitu 0.51 saat SF-10. Sedangkan pada kondisi outdoor nilai koefisien korelasi pengukuran tertinggi yaitu 0.81 saat SF-7. Metode Savitzky Golay Filter mampu meningkatkan koefisien korelasi sampai dengan 67.52% pada pengujian indoor. Rata-rata persentase kenaikan pada kondisi indoor yaitu lebih dari 30% dan kondisi outdoor lebih dari 15%. Dari hasil tersebut dapat diketahui bahwa metode Savitzky Golay Filter cocok digunakan untuk tahap pra proses karena mampu meningkatkan nilai koefisien korelasi secara signifikan.In wireless communications such as LoRa, it is necessary key generation to secure data. It is intended that the data sent is not easily attacked by attackers. One way to obtain a secure key is a high RSSI correlation coefficient value. In this research, a system was created to improve Reciprocity Channel Probing in order to obtain a high correlation coefficient value. The system is designed using the Savitzky Golay Filter method. The test was carried out in two conditions, namely indoor and outdoor. In addition, the test uses the Spreading Factor value from SF-7 to SF-10. The results of the measurement correlation coefficient in outdoor conditions are better than indoor conditions. The result of the highest measurement correlation coefficient in indoor conditions is 0.51 at SF-10. Meanwhile, in outdoor conditions, the highest measurement correlation coefficient is 0.81 at SF-7. The Savitzky Golay Filter method can increase the correlation coefficient up to 67.52% in indoor testing. The average percentage increase in indoor conditions is more than 30% and outdoor conditions is more than 15%. Therefore, that the Savitzky Golay Filter method is suitable for use in the pre-process stage because it can significantly increase the correlation coefficient

A module placement scheme for fog-based smart farming applications

As in Industry 4.0 era, the impact of the internet of things (IoT) on the advancement of the agricultural sector is constantly increasing. IoT enables automation, precision, and efficiency in traditional farming methods, opening up new possibilities for agricultural advancement. Furthermore, many IoT-based smart farming systems are designed based on fog and edge architecture. Fog computing provides computing, storage, and networking services to latency-sensitive applications (such as Agribots-agricultural robots-drones, and IoT-based healthcare monitoring systems), instead of sending data to the cloud. However, due to the limited computing and storage resources of fog nodes used in smart farming, designing a modules placement scheme for resources management is a major challenge for fog based smart farming applications. In this paper, our proposed module placement algorithm aims to achieve efficient resource utilization of fog nodes and reduce application delay and network usage in Fog-based smart farming applications. To evaluate the efficacy of our proposal, the simulation was done using iFogSim. Results show that the proposed approach is able to achieve significant reductions in latency and network usage

Ganadería de precisión en vacuno de carne

La ganadería de precisión es el conjunto de herramientas que permiten la automatización de las labores de granja y brindan información útil para la toma de decisiones orientadas a la eficiencia productiva del ganado. Esta revisión sistemática identificó las diferentes herramientas de ganadería de precisión probadas en vacuno de carne. Se utilizaron palabras claves que permitieran abarcar las diferentes herramientas existentes en las bases de datos en inglés de Web of Science (WoS) y ProQuest (PQ), utilizándose el gestor bibliográfico EndNote online. De los registros encontrados, se hizo una selección de trabajos relevantes en base al título y el resumen y se accedió posteriormente al trabajo completo de aquellos pre-seleccionados a través del acceso desde la biblioteca de la Universidad de Zaragoza o de búsquedas directas en Google. Finalmente, las 97 publicaciones que se encontraron se clasificaron según la utilidad que ofrecen las herramientas al ganadero en: identificación electrónica, reproducción, peso automático, medidas corporales, rastreo del animal, vallado virtual, monitorización de la salud, bienestar animal, alimentación, rumia, medio ambiente y granjas inteligentes. Según los resultados se pudo concluir que la ganadería de precisión ayuda al ganadero a resolver problemas particulares o más globales de la producción de carne. Sin embargo, es necesario el desarrollo de más estudios para ampliar la información enfocada en ganado vacuno de carne, y desarrollar más herramientas de precisión a nivel comercial o mejorar las existentes, para incentivar la implementación de tecnología en la granja ganadera y que le ayude a producir de manera más sostenible.<br /