At present, event detection technologies have become an important part in building efficient wireless sensor networks. One of the popular and Excellent event detecting algorithms is the Neighbor-based Fuzzy Logic algorithm, namely NFLE, which belongs to machine learning technology. However, traditional fuzzy logic algorithm cannot work well in high-precision fire detecting networks. In this paper, we propose an improved NFLE (INFLE) which can dramatically increase the precision of fire detection. In NFLE, the final fire confidence of one node is partly determined by the average readings of its neighbors in fuzzy logic system, which may lead to inaccurate fire detection when event occurred in an area that covers only part of its neighbors. In our proposed INFLE, we select some of neighbors, by specific rules, to determine node's final state. The simulation results validate that our proposed INFLE outperforms traditional NFLE in event detecting precision. Keyword: wireless sensor networks, event detection, fuzzy logic A wireless sensor network may contain tens to thousands of wireless sensor nodes to monitor the area and they are used in many applications such as security, surveillance, climatic-change studies, and structural health monitoring. In some poor detecting environment, abnormal events often happen in uncertain time, so we need sensor nodes to set an alarm precisely and punctually if abnormal events happen. Due to the limited computational power, memory, communication range for sensor nodes, designing an ideal event detection algorithm needs to be energy efficient, fault tolerant and robust, resource friendly, and adaptive to multiple event types and environment and it must be accuracy and produces less false alarm [1] . The initial idea of event detection is to use a threshold value, if the value of sensor node exceeds the pre-defined threshold, an event is considered to happen. For example, in fire event detection, if "temperature>=55℃", fire is present. Threshold-based event detection has the following features: a) easy to implement; b) Hard to set an appropriate threshold for some event; c) when events become complicated, false alarms will rise quickly. Model-based event detection techniques are another detecting techniques that model event phenomena in a specific form such as mathematic formulas or maps. Model-based event detection has the following features: a) can handle more complex events than threshold-based, as they represent a non-linear model for different events; b) need professional knowledge to tune the parameter of the models; c) due to complexity of events models, they are always computationally intensive. Pattern matching-based event
Sun et al. REF proposed an improved spatial-based FL event detection algorithm to decrease the probability of false positives.
INFLE: An Improved Neighbor-Based Fuzzy Logic Event Detecting Algorithm for Wireless Sensor Networks
International Journal of Future Generation Communication and Networking
International Journal of Future Generation Communication and Networking
Computer Science