AbstractAir pollution has emerged as a major concern of the current century. In recent times, fellow researchers have conducted numerous researches in the area of air quality monitoring. Still, air quality monitoring remains an open research area due to various challenges such as sophisticated topology design, privacy and security, power backup, large memory requirements and deployment of such systems at resource-constrained sites. The proposed research work is an attempt to address the issues of communication topology design, assessment of the Quality of Service (QoS) levels against accuracy, sensing throughput and power consumption optimization. In the undertaken work, the proposed IoT based Air Quality Monitoring system has been deployed at indoor and outdoor sites to measure air quality parameters such as PM10, PM2.5, carbon monoxide, temperature and humidity. The proposed system is also tested at variety of quality of service levels at the indoor and outdoor sites. The conducted experiments have also recorded accuracy in terms of reliable delivery of the messages under employed protocol.

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Abstract-Feature selection is always beneficial to the field like Intrusion Detection, where vast amount of features extracted from network traffic needs to be analysed. All features extracted are not informative and some of them are redundant also. We investigated the performance of three feature selection algorithms Chisquare, Information Gain based and Correlation based with Naive Bayes (NB) and Decision Table Majority Classifier. Empirical results show that significant feature selection can help to design an IDS that is lightweight, efficient and effective for real world detection systems.

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