Performance Evaluation of Wi-Fi Finger Printing based Indoor Positioning System

Wireless sensor network (WSN) is an integral part of Internet of Things (IoT). The sensor nodes in WSN generate large sensing data which is disseminated to intelligent servers using multiple wireless networks. This large data is prone to attacks from malicious nodes which become part of the network, and it is difficult to find these adversaries. The work in this paper presents a mechanism to detect adversaries for the IEEE 802.15.4 standard which is a central medium access protocol used in WSN-based IoT applications. The collisions and exhaustion attacks are detected based on a soft decision-based algorithm. In case the QoS of the network is compromised due to large data traffic, the proposed protocol adaptively varies the duty cycle of the IEEE 802.15.4. Simulation results show that the proposed intrusion detection and adaptive duty cycle algorithm improves the energy efficiency of a WSN with a reduced network delay.

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Performance Evaluation of Wi-Fi Finger Printing based Indoor Positioning System

Cited by 2 publications

References 8 publications

Indoor Location Estimation Based on Inverse Fingerprints at Multiple Points in Time and Moving Distance

Indoor Location Estimation Based on Inverse Fingerprints at Multiple Points in Time and Moving Distance

A Novel QoS‐Oriented Intrusion Detection Mechanism for IoT Applications

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