User-centric service recommendation for Internet of Things (IoT) applications can be defined as identifying, recommending, and provisioning IoT devices that can optimally satisfy any user's personalized service requests. The nature of these IoT services involve users renting out the available IoT devices to either access the sensed data or deploy and execute their applications on these devices. In doing so, users provide their personalized preferences on two different input categories - functional attributes and non-functional attributes. Functional attributes like region of interest or service duration establish the basic requirements of the service. Whereas non-functional attributes like network latency or security personalize the service further and enhance the user's overall experience.

The objective of this project is to research the provisioning of network security as a dynamic parameter during user-centric service recommendation for IoT applications. This is because, different users may have varying security requirements for their services which may also change over time. Furthermore, the strength of network security measures inversely affects network quality of service (QoS) metrics like bandwidth or latency. As such, security should not be provisioned as a predetermined static parameter following a universal one-size-fits-all policy. The research tasks involve doing a comprehensive survey of existing lightweight security protocols proposed for IoT applications, designing the scheme for provisioning dynamic network security, integrating its machine learning algorithm to perform service recommendation, and simulating a dynamic and secure IoT service recommendation scenario using an IoT emulator tool like CupCarbon.