SpaceSheep: Satellite Communications for Ovine Smart Grazing.

The application of IoT-based methods to support pastoralism allows the smart optimization of livestock operations and improves the efficiency of the activity. The use of autonomous animal control mechanisms frees the shepherd to carry out other tasks. However, human intervention is still needed in cases such as system failure, the bad or unpredicted behavior of the animals, or even in cases of danger, the welfare of the animal. This study documents the enhancement of an alarm generation system, initially developed within the scope of the SheepIT project, to monitor animal behavior and equipment, which warns the human operator of the occurrence of undesirable events that require intervention. Special attention was given to the use of case scenarios in places without Internet access, such as rural areas. Therefore, the system was integrated with a satellite interface, as a way of guaranteeing the timely delivery of the alarm messages. To ensure an acceptable operating cost, the system was further optimized in terms of message encoding, considering the cost of this type of communication. This study assessed the overall performance of the system, evaluated its scalability, and compared the efficiency gains from the optimization, as well as the performance of the satellite link.

Selective Content Retrieval in Information-Centric Networking.

Recently, novel networking architectures have emerged to cope with the fast-evolving and new Internet utilisation patterns. Information-Centric Networking (ICN) is a prominent example of this architecture. By perceiving content as the core element of the networking functionalities, ICN opens up a whole new avenue of information exchange optimisation possibilities. This paper presents an approach that progresses the base operation of ICN and leverages content identification right at the network layer, allowing to selectively retrieve partial pieces of information from content already present in ICN in-network caches. Additionally, this proposal enables information producers to seamlessly offload some content processing tasks into the network. The concept is discussed and demonstrated through a proof-of-concept prototype targeting an Internet of Things (IoT) scenario, where consumers retrieve specific pieces of the whole information generated by sensors. The obtained results showcase reduced traffic and storage consumption at the core of the network.

On the Development of a Wearable Animal Monitor.

Animal monitoring is a task traditionally performed by pastoralists, as a way of ensuring the safety and well-being of animals; a tremendously arduous and lonely task, it requires long walks and extended periods of contact with the animals. The Internet of Things and the possibility of applying sensors to different kinds of devices, in particular the use of wearable sensors, has proven not only to be less invasive to the animals, but also to have a low cost and to be quite efficient. The present work analyses the most impactful monitored features in the behavior learning process and their learning results. It especially addresses the impact of a gyroscope, which heavily influences the cost of the collar. Based on the chosen set of sensors, a learning model is subsequently established, and the learning outcomes are analyzed. Finally, the animal behavior prediction capability of the learning model (which was based on the sensed data of adult animals) is additionally subjected and evaluated in a scenario featuring younger animals. Results suggest that not only is it possible to accurately classify these behaviors (with a balanced accuracy around 91%), but that removing the gyroscope can be advantageous. Results additionally show a positive contribution of the thermometer in behavior identification but evidences the need for further confirmation in future work, considering different seasons of different years and scenarios including more diverse animals' behavior.