Wireless Sensor Network for Wildlife Tracking and Behavior Classification of Animals in Doñana

The study and monitoring of wildlife has always been a subject of great interest. Studying the behavior of wild animals is a difficult task due to the difficulties of tracking and classifying their actions. Nowadays, technology allows designing low-cost systems that make these tasks easier to carry out, and some of these systems produce good results; however, none of them obtains a high-accuracy classification because of the lack of information. Doñana National Park is a very rich environment with various endangered animal species. Thereby, this park requires a more accurate and efficient system of monitoring to act quickly against animal behaviors that may endanger certain species. In this letter, we propose a hierarchical, wireless sensor network installed in this park, to collect information about animals’ behaviors using intelligent devices placed on them which contain a neural network implementation to classify their behavior based on sensory information. Once a behavior is detected, the network redirects this information to an external database for further treatment. This solution reduces power consumption and facilitates animals’ behavior monitoring for biologists.Junta de Andalucía P12-TIC-130

Feasibility of wireless horse monitoring using a kinetic energy harvester model

To detect behavioral anomalies (disease/injuries), 24 h monitoring of horses each day is increasingly important. To this end, recent advances in machine learning have used accelerometer data to improve the efficiency of practice sessions and for early detection of health problems. However, current devices are limited in operational lifetime due to the need to manually replace batteries. To remedy this, we investigated the possibilities to power the wireless radio with a vibrational piezoelectric energy harvester at the leg (or in the hoof) of the horse, allowing perpetual monitoring devices. This paper reports the average power that can be delivered to the node by energy harvesting for four different natural gaits of the horse: stand, walking, trot and canter, based on an existing model for a velocity-damped resonant generator (VDRG). To this end, 33 accelerometer datasets were collected over 4.5 h from six horses during different activities. Based on these measurements, a vibrational energy harvester model was calculated that can provide up to 64.04 mu W during the energetic canter gait, taking an energy conversion rate of 60% into account. Most energy is provided during canter in the forward direction of the horse. The downwards direction is less suitable for power harvesting. Additionally, different wireless technologies are considered to realize perpetual wireless data sensing. During horse training sessions, BLE allows continues data transmissions (one packet every 0.04 s during canter), whereas IEEE 802.15.4 and UWB technologies are better suited for continuous horse monitoring during less energetic states due to their lower sleep current

Anahtarlı Boole geri besleme fonksiyonu olan kayan anahtar üreteçleri için gelişmiş saldırı yöntemi

Ultra-lightweight stream ciphers are highly optimized variation of stream ciphers for miniscule hardwares with limited power and calculation resources such as RFID product tags used in retail marketing and Wireless Sensor Network components that are indispensable part of modern SCADA systems. In FSE 2015, Armknecht and Mikhalev presented a unique ultra-lightweight stream cipher design approach defined as Keystream Generators with Keyed Update Function (KSG with KUF) along with a concrete cipher Sprout [1]. This design approach used by recent stream ciphers such as Fruit [2] and Plantlet [3], promises to make use of secret key during state updates in order to maintain security level as well as shorten internal state size to reduce hardware area in conjunction with power consumption. In 2018, definition of KSG with KUF is narrowed by Kara and Esgin [4], with new definition Keystream Generators with Boolean Keyed Feedback Function (KSG with Boolean KFF), on which a generic scope trade-off attack is also mounted. This attack relies on guess capacity definition given in the same article, to eliminate wrong states during exhaustive search operation. In this thesis, we examined this generic Kara and Esgin attack in-depth and accelerated by a factor up to about 60 times. In order to accomplish this speedup, a new guess capacity definition and sieving method are introduced in addition to the improved algorithm which contributes efficiency of the attack in both performance and stability. Improvements are validated with intense performance tests comprising nearly twenty sample feedback functions, including Sprout, with diverse existence of guess capacities.Yazarlık Beyanı ii Abstract iv Öz v Teşekkür vii Şekil Listesi xi Tablo Listesi xii Kısaltmalar xiii Sözlükçe xiv 1 Giriş 1 1.1 Motivasyon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 İlişkin Çalışmalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Katkılarımız . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Tezin Bölümleri (Ana Hatları) . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Temel Kavramlar 10 2.1 Kriptografinin Kısa Geçmişi . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.1 İletişim Yöntemlerinin Gelişimi . . . . . . . . . . . . . . . . . . . . 10 2.1.2 Kriptografi Nedir? . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Kriptografik Algoritmaların Sınıflandırılması . . . . . . . . . . . . . . . . . 11 2.2.1 Antik Dönem Teknikleri . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 Elektronik Dünyaya Geçiş . . . . . . . . . . . . . . . . . . . . . . . 12 3 Dizi Şifreleme 14 3.1 Giriş & Kullanım Alanları . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.1 GSM (2G), UMTS(3G) ve LTE(4G) Güvenliği . . . . . . . . . . . 15 3.1.2 Kablosuz Ağ Güvenliği (WEP and WPA) . . . . . . . . . . . . . . 15 3.1.3 RFID Uygulamaları . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1.4 Kablosuz Sensör Ağları (WSN) . . . . . . . . . . . . . . . . . . . . 16 3.1.5 ZigBee Protokolü . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2 Dizi Şifrelemenin Temel Kavramları . . . . . . . . . . . . . . . . . . . . . . 19 3.3 Tek Seferlik Şifre (One Time Pad) . . . . . . . . . . . . . . . . . . . . . . 19 3.4 Donanımsal Nitelikler ve Performans Ölçütleri . . . . . . . . . . . . . . . . 20 3.4.1 Donanım Boyutu (Kapı Eşdeğeri) . . . . . . . . . . . . . . . . . . . 20 3.4.2 Çıktı Hızı . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.4.3 Yayılım Gecikmesi . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.4.4 Operasyonel Saat Frekansı . . . . . . . . . . . . . . . . . . . . . . . 21 3.5 Lineer Geri Beslemeli Ötelemeli Saklayıcı (LFSR) . . . . . . . . . . . . . . 22 3.6 Lineer Olmayan Geri Beslemeli Ötelemeli Saklayıcı (NLFSR) . . . . . . . 23 3.7 A5/1 Algoritmasına Hızlı Bakış . . . . . . . . . . . . . . . . . . . . . . . . 23 3.7.1 Kayan Anahtar Üretecinin Tasarımı . . . . . . . . . . . . . . . . . 24 3.7.2 İlklendirme Fazı . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.8 Trivium Algoritmasına Hızlı Bakış . . . . . . . . . . . . . . . . . . . . . . 26 3.9 Espresso Algoritmasına Hızlı Bakış . . . . . . . . . . . . . . . . . . . . . . 26 4 Anahtarlı Güncelleme Fonksiyonu olan Kayan Anahtar Üreteçleri 28 4.1 Tanımlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Sprout Algoritması . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2.1 Çıkış Noktası . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.2.2 Tasarım . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.2.3 İlklendirme Fazı . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2.4 Gerçekleştirilen Saldırılar . . . . . . . . . . . . . . . . . . . . . . . 35 5 ABGBF-KAÜ Ailesine Yönelik Genel Kapsamlı Saldırı 36 5.1 Saldırının Açıklaması . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.1.1 Tahmin Kapasitesi (Prg) . . . . . . . . . . . . . . . . . . . . . . . 37 5.1.2 Çıktı Kapasitesi (θ) . . . . . . . . . . . . . . . . . . . . . . . . . . 37 5.1.3 Karavana İhtimali () . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.1.4 Sonlandırma Değeri (αter) . . . . . . . . . . . . . . . . . . . . . . 38 5.1.5 Eşik Değeri (αthr) . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.1.6 İç Durum Zaafiyet Göstergesi (d) . . . . . . . . . . . . . . . . . . . 38 5.2 İç Durum Geri Kazanım Algoritması . . . . . . . . . . . . . . . . . . . . . 38 5.2.1 İDGK Sözde Kodu . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.3 Determine Algoritması . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.4 Check & Guess Algoritması . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.5 Anahtar Geri Kazanım Fazı . . . . . . . . . . . . . . . . . . . . . . . . . . 42 6 Geliştirilmiş Saldırı Algoritması 44 6.1 Mevcut Algoritmadaki Darboğaz Noktaları . . . . . . . . . . . . . . . . . . 44 6.2 Hata Düzeltmesi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.2.1 Sözde Kodlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.3 İyileştirme No:1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.3.1 İyileştirilmiş Algoritma . . . . . . . . . . . . . . . . . . . . . . . . . 46 6.3.2 Sözde Kodlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 6.3.3 İyileştirmenin Performansa Etkisi . . . . . . . . . . . . . . . . . . . 47 6.4 İyileştirme No:3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.4.1 Sözde Kodlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.5 Geliştirilmiş Algoritmanın Nihai Tasarımı . . . . . . . . . . . . . . . . . . 51 6.5.1 Sözde Kodlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 7 Geliştirilmiş Algoritmanın Performans Analizi 53 7.1 Ön Bilgiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 7.1.1 Benzetimin Bilgisayar Ortamında Gerçeklenmesi . . . . . . . . . . 53 7.1.2 Test Sistemi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 7.1.3 Test Senaryosu ve Test Fonksiyonları . . . . . . . . . . . . . . . . . 54 7.1.4 Performans Metrikleri . . . . . . . . . . . . . . . . . . . . . . . . . 56 7.2 Test Sonuçları . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 7.2.1 Grafiklerin Yorumlanması . . . . . . . . . . . . . . . . . . . . . . . 60 8 Sonuç 64 8.1 Yeni Algoritmanın Tasarımı . . . . . . . . . . . . . . . . . . . . . . . . . . 64 8.2 Bulgular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.3 Bilinen Kısıtlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.4 İleriye Yönelik Araştırma Konuları . . . . . . . . . . . . . . . . . . . . . . 65 8.5 Son Yorumlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 A KE Algoritması Bellek Kullanımı Raporu 67 B Benzetim Uygulaması Kaynak Kodları 69 B.1 Geliştirme Süreci . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 B.2 Proje Yapısı . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 B.3 Proje 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 B.4 Proje 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Kaynaklar 7

Pet sense: sistema de monitorização de animais em hospitalização

The observation and treatment of animals in veterinary hospitals is still very dependent on manual procedures, including the collection of vital signs (temperature, heart rate, respiratory rate and blood pressure). These manual procedures are time-consuming and invasive, affecting the animal’s well-being. In this work, we purpose the use of IoT technologies to monitor animals in hospitalization, wearing sensors to collect vitals, and low-cost hardware to forward them into a cloud backend that analyses and stores data. The history of observed vitals and alarms can be accessed in the web, included in the Pet Universal software suite. The overall architecture follows a stream processing approach, using telemetry protocols to transport data, and Apache Kafka Streams to analyse streams and trigger alarms on potential hazard conditions. The system was fully implemented, although with laboratory sensors to emulate the smart devices to be worn by the animals. We were able to implement a data gathering and processing pipeline and integrate with the existing clinical management information system. The proposed solution can offer a practical way for long-term monitoring and detect abnormal values of temperature and heart rate in hospitalized animals, taking into consideration the characteristics of the monitored individual (species and state).A observação e tratamento de animais hospitalizados continua muito dependente de procedimentos manuais, especialmente no que diz respeito à colheita de sinais vitais (temperatura, frequência cardíaca, frequência respiratória e pressão arterial). Estes procedimentos manuais são dispendiosos em termos de tempo e afetam o bem-estar do animal. Neste projeto, propomos o recurso a tecnologias IoT para monitorizar animais hospitalizados equipados com sensores que medem sinais vitais, com hardware acessível, e envio dos dados para um serviço na cloud que os analisa e armazena. O histórico dos valores e alarmes podem ser acedidos na web e incluídos na plataforma comercial da Pet Universal. A arquitetura geral segue uma abordagem de processamento funcional, usando protocolos de telemetria para transportar dados e Apache Kafka Streams, analisando e lançando alarmes sobre potenciais condições de risco de acordo com a temperatura e pulsação. O sistema foi totalmente implementado, embora com sensores de laboratório para simular os dispositivos a serem usados pelos animais. Conseguimos implementar um circuito de colheita e processamento de dados e integrar com o sistema de gestão clínica já existente. A solução proposta oferece uma forma prática de monitorização continuada e de deteção de valores anormais de temperatura e frequência cardíaca em animais hospitalizados, tomando em conta as características do indivíduo monitorado (espécie e estado).Mestrado em Engenharia Informátic