Architecture for Internet of Things to minimize human intervention

Today, smart objects in Internet of Things (IOT) are able to detect their state and share it with other objects across the Internet, thus collaboratively making intelligent decisions on their own. Large number of objects is quietly introduced into the web every day. It is impossible for a user to keep a track of all objects available around him. Therefore a search engine that could find smart objects is used. If an exact object yielding the required service is not present then system requires explicit user inputs to find alternatives such as name of an alternate object that may provide the same service. Humans always find alternatives around them to carry out their work smoothly. Service provisioning in IOT should also be made capable of providing similar or alternate objects that are aligned with user requirements, current context and previous knowledge without any human intervention. We propose an architecture that uses Naive Bayesian prediction on user history and Ant Colony Optimization for accurately predicting objects of user interest so as to reduce the dependency of IOT application on human inputs for finding an object match.