Nashreen Nesa

TL;DR: This paper deals with detection of occupancy in a room from various ambient sources like temperature, humidity, light, and CO2 and adopts Dempster–Shafer evidence theory for fusing sensory information collected from heterogeneous sensors, assigns probability mass assignments (PMAs) to the raw sensor readings, and finally performs mass combination to derive a conclusion about the occupancy status in aRoom.

TL;DR: A sequence based learning approach for outlier detection that works for both Error and Event is proposed, which considers both Error, that is a result of faulty sensors or an Event, which is an indication of an abnormal phenomenon.

TL;DR: An IoT architecture to detect the occurrence of both Error and Event in a forest environment with the help of four statistical models, i.e., Classification and Regression Trees (CART), Random Forest (RF), Gradient Boosting Machine (GBM) and Linear Discriminant Analysis (LDA).

TL;DR: The chaotic dynamic of the proposed map LCM is thoroughly investigated and its chaotic properties are found to be superior in comparison with the existing maps in the literature and a new encryption algorithm is proposed that is based on LCM.

TL;DR: A dynamic sensor activation algorithm inspired by the popular PageRank algorithm called SensorRank is devised, which dynamically analyses the network topology in terms of relative distances and link qualities between devices and the remaining energies of the devices, based on which the sensors are ranked.