False alarm detection using dynamic threshold in medical wireless sensor networks

Sensor networks suffer from various sensor faults and false measurements in healthcare application and this vulnerability of the delay should handle efficiently and timely response in various application of WSN. For instance, in healthcare application, the false measurements generate false alarms which require to take unnecessary action from the healthcare department. The quality of the health care service can improve in remote healthcare monitoring system by introducing a new approach to identify the true medical condition and differentiate true and false alarms. In this paper, we proposed a novel approach to analysis past historical data collected from various medical sensors to identify the sensor anomaly. The main goal of this approach is to differentiate between true and false alarms effectively. The proposed system analysis the historical data to predicts the sensor value which compares with real sensed values at a time incident. The dynamically adjust the threshold value by comparing the difference between predicted value and historic value to determine the anomaly of sensor value. This system has been worked on huge real-time healthcare dataset and result shows that the new approach has been applied on real healthcare datasets and result of this system shows the detection rate is high and false positive rate is low which conclude that this approach is very useful in WSN application such as health monitoring system and it will be competitive with others.

     

Privacy-Preserving and Energy-Centered QoS for IoT Using XOR-RSA and BM-SSA

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Wireless Personal Communications - Wireless Sensor Network (WSN) and its security concern play a vital part in its effecting functioning. WSN routing layer attacks pose a great threat to...     

Wireless Sensor Network-based Detection of Poisonous Gases Using Principal Component Analysis

This work utilizes a statistical approach of Principal Component Analysis (PCA) towards the detection of Methane (CH₄)-Carbon Monoxide (CO) Poisoning occurring in coal mines, forest fires, drainage systems etc. where the CH₄ and CO emissions are very high in closed buildings or confined spaces during oxidation processes. Both methane and carbon monoxide are highly toxic, colorless and odorless gases. Both of the gases have their own toxic levels to be detected. But during their combined presence, the toxicity of the either one goes unidentified may be due to their low levels which may lead to an explosion. By using PCA, the correlation of CO and CH₄ data is carried out and by identifying the areas of high correlation (along the principal component axis) the explosion suppression action can be triggered earlier thus avoiding adverse effects of massive explosions. Wireless Sensor Network is deployed and simulations are carried with heterogeneous sensors (Carbon Monoxide and Methane sensors) in NS-2 Mannasim framework. The rise in the value of CO even when CH₄ is below the toxic level may become hazardous to the people around. Thus our proposed methodology will detect the combined presence of both the gases (CH₄ and CO) and provide an early warning in order to avoid any human losses or toxic effects.