An Adaptive Framework for Event Detection in Wireless Sensor Network
1S.Nalini*, Dept of Computer Applications, UCE, BIT Campus, Anna University, Trichy.
2Dr .A. Valarmathi Dept of Computer Applications, UCE, BIT Campus, Anna University, Trichy.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 5123-5128 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8320118419/2019©BEIESP | DOI: 10.35940/ijrte.D8320.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A wireless sensor network is the new generation technology that holds the capability and it has transformed to interact with the physical environment according to the user’s demands. The features of a sensor network play the major role in various application fields that fit in the broad category for tracking, monitoring, automation and detection and in that category WSN plays the predominant role in event monitoring. Event detection in sensor networks is the process of observing; evaluating an event based on multiple attributes and generates an alarm at the appropriate time. A framework was developed to detect the event in in-door environment. The proposed event detection model comprises of three phases. In the first phase the network id grouped and cluster head is determined and subsequently composite model was developed to determine the event and the final phase involves in the fuzzy rule optimization. A simulation setup has created and the experimental evaluation validates the event detection mechanism through various metrics such as cluster communication, event accuracy are measured and evaluated. The results are compared with the energy efficient HEED algorithm and connected dominating set based cluster MCDS-CCH algorithm further the composite event detection mechanism with optimization rule outperforms than the well-established J48 decision tree classification algorithm.
Keywords: Event Detection, Sensor Network, Event Accuracy, Fuzzy Model, Fuzzy Genetic.
Scope of the Article: Adhoc and Sensor Networks.