Real-time Internet of Things Architecture for Wireless Livestock Tracking

Automatic livestock tracking is necessary for countries facing stock theft problems, like South Africa and Kenya. This paper presents a conceptual design of architecture for real-time wireless livestock tracking based on Internet of Things paradigm. It is a hierarchical model consisting of three building blocks, where the first block is represented with wireless sensor network. Additionally, we have developed a low-power device for livestock tracking in an outdoor environment. The animal tracking device (AnTrack) is self-sustainable with a watertight solar panel(s), designed as a collar to be worn by the animals. A detailed analysis of the AnTrack power consumption proves that the device is capable to generate enough supply power, even when there is no sunshine for a week. This device can be used as a robust building block of future real-time Internet of Things livestock tracking solutions

Social Internet of Things and New Generation Computing -- A Survey

Social Internet of Things (SIoT) tries to overcome the challenges of Internet of Things (IoT) such as scalability, trust and discovery of resources, by inspiration from social computing. This survey aims to investigate the research done on SIoT from two perspectives including application domain and the integration to the new computing models. For this, a two-dimensional framework is proposed and the projects are investigated, accordingly. The first dimension considers and classifies available research from the application domain perspective and the second dimension performs the same from the integration to new computing models standpoint. The aim is to technically describe SIoT, to classify related research, to foster the dissemination of state-of-the-art, and to discuss open research directions in this field.Comment: IoT, Social computing, Surve

Internet of Things Framework for Home Care Systems

The increasing average age of the population in most industrialized countries imposes a necessity for developing advanced and practical services using state-of-the-art technologies, dedicated to personal living spaces. In this paper, we introduce a hierarchical distributed approach for home care systems based on a new paradigm known as Internet of Things (IoT). The proposed generic framework is supported by a three-level data management model composed of dew computing, fog computing, and cloud computing for efficient data flow in IoT based home care systems. We examine the proposed model through a real case scenario of an early fire detection system using a distributed fuzzy logic approach. The obtained results prove that such implementation of dew and fog computing provides high accuracy in fire detection IoT systems, while achieving minimum data latency

FINKI Scholar, a Publications Database for Faculty of Computer Science and Engineering Scholars

The aim of this paper is to develop a web application where scholars of the Faculty of Computer Science and Engineering (FINKI) at the University of Ss. Cyril and Methodius can display and share their projects and publications. Visitors can view, search through, and filter the authors, projects and publications that can be added and edited by the administrators via the administrator panel. In this paper, we first explain the type of system we are building and go through similar existing systems explaining how they work and what they offer. Then, we go through the programming languages and technologies we decided to use to develop this web application. After that, the development phase follows, where we describe each of the features we implemented. In The Final Product section we finally show images where you can see how the web application works and what it looks like. We finish the paper with a conclusion, briefly summarizing what we have achieved

Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position.The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers.The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be achieved from either accelerometer position.Machine learning techniques can be used for automatic activity recognition, as they provide very accurate activity recognition, significantly more accurate than when keeping a diary. Identification of jogging periods in adolescents can be performed using only one accelerometer. Performance-wise there is no significant benefit from using accelerometers on both locations

Washing Machine Controller with a New Programming Method

In the paper the newly designed at Poznan University of Technology (PUT) washing machine controller is presented. The commonly used in washing machines sensors, drives and other input-output elements are briefly described. The designed at PUT controller is based on 32-bit STM32 microcontroller. The used in this controller modules are described and their input/output signals and basics of operations are presented. The developed in the controller user-machine communication devices, elements and methods are described. The paper presents new washing machine programming methods and implementation software, such as voice recognition and intelligent programming of washing machine that were applied in the new controller