Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

High Accuracy Human Activity Monitoring using Neural network

This paper presents the designing of a neural network for the classification of Human activity. A Triaxial accelerometer sensor, housed in a chest worn sensor unit, has been used for capturing the acceleration of the movements associated. All the three axis acceleration data were collected at a base station PC via a CC2420 2.4GHz ISM band radio (zigbee wireless compliant), processed and classified using MATLAB. A neural network approach for classification was used with an eye on theoretical and empirical facts. The work shows a detailed description of the designing steps for the classification of human body acceleration data. A 4-layer back propagation neural network, with Levenberg-marquardt algorithm for training, showed best performance among the other neural network training algorithms.Comment: 6 pages, 4 figures, 4 Tables, International Conference on Convergence Information Technology, pp. 430-435, 2008 Third International Conference on Convergence and Hybrid Information Technology, 200

Tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi MARA terhadap mata pelajaran Bahasa Inggeris

Kajian ini dilakukan untuk mengenal pasti tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi Mara Sri Gading terhadap Bahasa Inggeris. Kajian yang dijalankan ini berbentuk deskriptif atau lebih dikenali sebagai kaedah tinjauan. Seramai 325 orang pelajar Diploma in Construction Technology dari Kolej Kemahiran Tinggi Mara di daerah Batu Pahat telah dipilih sebagai sampel dalam kajian ini. Data yang diperoleh melalui instrument soal selidik telah dianalisis untuk mendapatkan pengukuran min, sisihan piawai, dan Pekali Korelasi Pearson untuk melihat hubungan hasil dapatan data. Manakala, frekuensi dan peratusan digunakan bagi mengukur penguasaan pelajar. Hasil dapatan kajian menunjukkan bahawa tahap penguasaan Bahasa Inggeris pelajar adalah berada pada tahap sederhana manakala faktor utama yang mempengaruhi penguasaan Bahasa Inggeris tersebut adalah minat diikuti oleh sikap. Hasil dapatan menggunakan pekali Korelasi Pearson juga menunjukkan bahawa terdapat hubungan yang signifikan antara sikap dengan penguasaan Bahasa Inggeris dan antara minat dengan penguasaan Bahasa Inggeris. Kajian menunjukkan bahawa semakin positif sikap dan minat pelajar terhadap pengajaran dan pembelajaran Bahasa Inggeris semakin tinggi pencapaian mereka. Hasil daripada kajian ini diharapkan dapat membantu pelajar dalam meningkatkan penguasaan Bahasa Inggeris dengan memupuk sikap positif dalam diri serta meningkatkan minat mereka terhadap Bahasa Inggeris dengan lebih baik. Oleh itu, diharap kajian ini dapat memberi panduan kepada pihak-pihak yang terlibat dalam membuat kajian yang akan datang

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks