1 research outputs found
An Efficient Fog-Assisted Unstable Sensor Detection Scheme with Privacy Preserved
The Internet of Thing (IoT) has been a hot topic in both research community
and industry. It is anticipated that in future IoT, an enormous number of
sensors will collect the physical information every moment to enable the
control center making better decisions to improve the quality of service (QoS).
However, the sensors maybe faulty and thus generate inaccurate data which would
compromise the decision making. To guarantee the QoS, the system should be able
to detect faulty sensors so as to eliminate the damages of inaccurate data.
Various faulty sensor detection mechanisms have been developed in the context
of wireless sensor network (WSN). Some of them are only fit for WSN while the
others would bring a communication burden to control center. To detect the
faulty sensors for general IoT applications and save the communication resource
at the same time, an efficient faulty sensor detection scheme is proposed in
this paper. The proposed scheme takes advantage of fog computing to save the
computation and communication resource of control center. To preserve the
privacy of sensor data, the Paillier Cryptosystem is adopted in the fog
computing. The batch verification technique is applied to achieve efficient
authentication. The performance analyses are presented to demonstrate that the
proposed detection scheme is able to conserve the communication resource of
control center and achieve a high true positive ratio while maintaining an
acceptable false positive ratio. The scheme could also withstand various
security attacks and preserve data privacy.Comment: 11 pages, 5 figure