Intensity Based Event Detection in Sensor Based IoT

Finding an optimum trade-off between event detection and network lifetime is a major problem in the sensor-based Internet of Things framework. Further, reliable, effective, and accurate event detection is a perennial research problem explored in the domain of Sensor Based Internet of Things (SBIoT). Major research problems focusing on event detection depend upon models like Boolean and probabilistic sensing models. However, event detection is practically dependent upon the intensity and persistence of the event. The traditional non-intensity-based event sensing models fix a predefined sensing radius. Any occurrence outside the sensing radius is not considered an event, independent of its severity. The present work argues that the intensity and persistence of the event are also relevant parameters for event detection. This paper proposes two novel event intensity and persistence-based models for detecting different types of events and improving upon the quality of detection. The proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‘Improved’</i> model proves to be more efficient than the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‘Conventional’</i> model. Further, the simulation results indicate the proposed algorithm's efficiency and effectiveness, and compare it with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Non-intensity based</i> models. Additionally, the results are compared in terms of detection accuracy, node activation, and network lifetime to show the efficiency and trade-offs of the proposed scheme.