An Anomaly Detection Algorithm Selection Service for IoT Stream Data Based on Tsfresh Tool and Genetic Algorithm

Anomaly detection algorithms (ADA) have been widely used as services in many maintenance monitoring platforms. However, there are numerous algorithms that could be applied to these fast changing stream data. Furthermore, in IoT stream data due to its dynamic nature, the phenomena of conception drift happened. Therefore, it is a challenging task to choose a suitable anomaly detection service (ADS) in real time. For accurate online anomalous data detection, this paper developed a service selection method to select and configure ADS at run-time. Initially, a time-series feature extractor (Tsfresh) and a genetic algorithm-based feature selection method are applied to swiftly extract dominant features which act as representation for the stream data patterns. Additionally, stream data and various efficient algorithms are collected as our historical data. A fast classification model based on XGBoost is trained to record stream data features to detect appropriate ADS dynamically at run-time. These methods help to choose suitable service and their respective configuration based on the patterns of stream data. The features used to describe and reflect time-series data’s intrinsic characteristics are the main success factor in our framework. Consequently, experiments are conducted to evaluate the effectiveness of features closed by genetic algorithm. Experimentations on both artificial and real datasets demonstrate that the accuracy of our proposed method outperforms various advanced approaches and can choose appropriate service in different scenarios efficiently

From Digital Divide to Information Availability: A Wi-Fi-Based Novel Solution for Information Dissemination

Digital divide means unequal access to the people for information and communication technology (ICT) facilities. The developed countries are comparatively less digitally divided as compared to developing countries. This study focuses on District Chitral considering its geographical conditions and high mountainous topography which plays a significant role in its isolation. Aside from the digital divide, the situation in Chitral is even more severe in terms of the absence of basic ICT infrastructure and electricity in the schools. To address this issue, especially in female secondary and higher secondary schools, we designed a project to bridge the digital divide via Wireless Local Area Network on Raspberry Pi3 for balancing the ICT facilities in the targeted area. The Wi-Fi-Based Content Distributors (Wi-Fi-BCDs) were provided to bridge the digital divide in rural area schools of Chitral. The Wi-Fi-BCD is a solar-based system that is used to deliver quality educational contents directly to classroom, library, or other learning environments without electricity connection and Internet wire as these facilities are available by default in it. The close-ended questionnaire was adopted to collect data from the students, teachers, and headmistresses of girl secondary and higher secondary schools in Chitral. The procedure of validity, reliability, regression, correlation, and exploratory factor analysis was used to analyze the obtained data. The technology acceptance model (TAM) was modified and adopted to examine the effects of Wi-Fi-BCD for bridging the digital divide. The relationship of the modified TAM model was examined through regression and correlation to verify the model fitness according to the data obtained. The result analysis of this study shows that the relationship of the modified TAM model with its variables is positively significant, while the analysis of path relationship between model variables and outcomes from the questionnaire shows that it motivates learners to use Wi-Fi-BCD