A New Meta-heuristic Approach for Efficient Search in the Internet of Things
Mohammad Ebrahimi, Elaheh ShafieiBavani, Raymond K. Wong, Chi‐Hung Chi · 2015
The number of sensors deployed around the world is growing at a rapid pace when we are moving towards the Internet of Things (IoT). The widespread deployment of these sensors represents significant financial investment and technical achievement. These sensors continuously generate enormous amounts of data which is capable of supporting an almost unlimited set of high value proposition applications for users. Given that, effectively and efficiently searching and selecting the most related sensors of a user's interest has recently become a crucial challenge. In this paper, inspired by ant clustering algorithm, we propose an effective context-aware method to cluster sensors in the form of Sensor Semantic Overlay Networks (SSONs) in which sensors with similar context information gathered into one cluster. Firstly, sensors are grouped based on their types to create SSONs. Then, our meta-heuristic algorithm called Ant Clust has been performed to cluster sensors using their context information. Finally, a few useful adjustments have been applied to reduce the cost of sensor search process. Experiments show the scalability of Ant Clust in clustering sensors and significantly faster runtime on sensor search, when compared with existing systems.