Abstract:
Internet of Things is a paradigm shift in the advancement of wireless sensor
networks. This has been realized with advanced techniques of service provision and
exchange of data. The concept behind this technology is “anytime, anywhere, any
media”. There is a huge potential in IoT for the development of a large number of
application out of which very small part has been deployed in our lives. Devices
connected through the internet of Things are equipped with smart objects and these
objects are increasingly becoming part of our life. IoT devices are providing services
in almost all walks of life like services in automated industry, medical field, chemical
plants, inventory control and other areas of human life. IoT devices are light weight
with low power and low memory. Therefore, these devices are resource constrained and
data possessed by these devices is very sensitive.
In this research work the author identified a number of trust factors for the
differentiation of the trustworthy and suspicious devices in IoT environments. The
suspicious devices some time behave abnormally due to number of facts like memory
and battery constraints and low processing power. Due to their malfunctioning these
devices compromise the overall trust profile of the IoT network. The author has
proposed a framework through which he described the essential elements for the
identification of malicious nodes. This framework helps in in identifying the suspicious
behavior of giving false feedback as badmouthing against the trustworthy nodes. After
framing the proposed frame work, a model has been designed in which game theory
has been incorporated for the behavior correction of the malicious devices through
carrot and stick policy. On the basis of the game strategy the Nash equilibrium has been
achieved so that a device after choosing its strategy cannot deviate from its strategy
unilaterally. The stated model has been formalized in Microsoft Azure IoT Central, a
software as a service (SaaS) based cloud environment and simulation has been
performed. In the simulation, the deployment of the IoT devices and their feedback
values regarding the tasks and services have also been collected for the identification
of badmouthing in these feedbacks. The trust value of the participating nodes has been
calculated on the basis of the feedback values after passing through the proposed noncooperative game so that our proposed trust model become trustworthy and cooperation
enforcing. Finally, a conclusion regarding the performance of proposed work has been
drawn.
