Smart Wireless Sensor Network to Detect and Protect Sheep and Goats to Wolf Attacks

The attacks on flocks of goats and sheep are causing great economic losses in some countries. The main causes of these are usually wild animals such as wolves, or thefts of young animals. When an animal feels the presence of people or animals different than the usual ones, it experiences changes in its vital signs. The parameters which are the most altered are the heart rate and body temperature. This paper presents the development of wireless sensor nodes which can monitor vital signs of sheep and goats. These nodes form a network of wireless nodes using IEEE 802.15.4 technology. The network runs a smart algorithm able to detect episodes of collective stress on flocks of goats and sheep caused by any predator attack overnight. When the system detects changes in vital signs of animals, it sends an alarm signal to alert the person in charge of the livestock facility and activates audible and visual alarms able to scare off predators and marauders. Our system can help breeders of goats and sheep to protect their herds avoiding large economic losses that this implies.Sendra Compte, S.; Llario Sempere, F.; Parra Boronat, L.; Lloret, J. (2013). Smart Wireless Sensor Network to Detect and Protect Sheep and Goats to Wolf Attacks. Recent Advances in Communications and Networking Technology. 2(2):91-101. doi:10.2174/22117407112016660012S911012

Sensor Network Proposal for Greenhouse Automation placed at the South of Algeria

[EN] The south of Algeria has a very hard climate. In summer, it is very hot and dry with a very violent sand wind and in winter very cold and dry, from where several plants cannot be cultivated in an open field. With rapid population growth, the production of fruits and vegetables cannot be sufficient. To solve these two major problems, we propose in this paper a new mechanism for the control of the climate inside a greenhouse. The objective of this work is to propose a new design for the greenhouse that can be managed and controlled automatically. The management and the control of this greenhouse are done because of our new proposed algorithms, and the use of new technologies such as sensors, actuators, microcontrollers, and the Internet of things to facilitate the tasks of farmers in the south of Algeria, and to improve the productiveness of the agriculture. We present the results of applying our proposal in a greenhouse during a short period of time and the changes on the environmental parameters inside the greenhouse.This work has also been partially supported by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR.Achouak, T.; Khelifa, B.; García-García, L.; Parra-Boronat, L.; Lloret, J.; Fateh, B. (2018). Sensor Network Proposal for Greenhouse Automation placed at the South of Algeria. Network Protocols and Algorithms. 10(4):53-69. https://doi.org/10.5296/npa.v10i4.14155S536910

ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and Challenges of Confned and Extensive Precision Livestock Production

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confned operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative fve-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This fve-step process acts as a guide to realize anticipated benefts from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confned and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confned operations will beneft from required advances in precision technology and PSMs, ultimately strengthening the benefts from precision technology to achieve short- and long-term goals

Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

In recent years, edge computing has become an essential technology for real-time application development by moving processing and storage capabilities close to end devices, thereby reducing latency, improving response time and ensuring secure data exchange. In this work, we focus on a Smart Agriculture application that aims to protect crops from ungulate attacks, and therefore to significantly reduce production losses, through the creation of virtual fences that take advantage of computer vision and ultrasound emission. Starting with an innovative device capable of generating ultrasound to drive away ungulates and thus protect crops from their attack, this work provides a comprehensive description of the design, development and assessment of an intelligent animal repulsion system that allows to detect and recognize the ungulates as well as generate ultrasonic signals tailored to each species of the ungulate. Taking into account the constraints coming from the rural environment in terms of energy supply and network connectivity, the proposed system is based on IoT platforms that provide a satisfactory compromise between performance, cost and energy consumption. More specifically, in this work, we deployed and evaluated various edge computing devices (Raspberry Pi, with or without a neural compute stick, and NVIDIA Jetson Nano) running real-time object detector (YOLO and Tiny-YOLO) with custom-trained models to identify the most suitable animal recognition HW/SW platform to be integrated with the ultrasound generator. Experimental results show the feasibility of the intelligent animal repelling system through the deployment of the animal detectors on power efficient edge computing devices without compromising the mean average precision and also satisfying real-time requirements. In addition, for each HW/SW platform, the experimental study provides a cost/performance analysis, as well as measurements of the average and peak CPU temperature. Best practices are also discussed and lastly, this article discusses how the combined technology used can help farmers and agronomists in their decision making and management process

Internet of Things Strategic Research Roadmap

Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network

Construction of a system of flow and temperature instrumentation on a porcine farm in the municipality of Marsella, Risaralda

We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm.We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm

Construction of a system of flow and temperature instrumentation on a porcine farm in the municipality of Marsella, Risaralda

We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm.We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm

Construction of a system of flow and temperature instrumentation on a porcine farm in the municipality of Marsella, Risaralda

We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm.We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm

Poisoning Attacks on Learning-Based Keystroke Authentication and a Residue Feature Based Defense

Behavioral biometrics, such as keystroke dynamics, are characterized by relatively large variation in the input samples as compared to physiological biometrics such as fingerprints and iris. Recent advances in machine learning have resulted in behaviorbased pattern learning methods that obviate the effects of variation by mapping the variable behavior patterns to a unique identity with high accuracy. However, it has also exposed the learning systems to attacks that use updating mechanisms in learning by injecting imposter samples to deliberately drift the data to impostors’ patterns. Using the principles of adversarial drift, we develop a class of poisoning attacks, named Frog-Boiling attacks. The update samples are crafted with slow changes and random perturbations so that they can bypass the classifiers detection. Taking the case of keystroke dynamics which includes motoric and neurological learning, we demonstrate the success of our attack mechanism. We also present a detection mechanism for the frog-boiling attack that uses correlation between successive training samples to detect spurious input patterns. To measure the effect of adversarial drift in frog-boiling attack and the effectiveness of the proposed defense mechanism, we use traditional error rates such as FAR, FRR, and EER and the metric in terms of shifts in biometric menagerie