Selective context fusion utilizing an integrated RFID-WSN architecture

The abundance of sensed data and its correlation between wireless entities has recently increased significantly. Understanding the context of each entity in a given environment is not-trivial. Mainly due to the need of realizing an efficient scheme for context fusion over multiple sensing/polling technologies. In this paper we present a selective context fusion model that utilizes an integrated architecture which encompasses both RFID systems and information collected from Wireless Sensor Networks (WSNs). We integrate the identification capabilities of the former with the group intelligence sustained by the latter. A mediator acts as both the reader and relay (RR) node to communicate both technologies respectively. Thus, collecting context information from sensors and tags, then aggregating, filtering and carrying out analysis to selectively enhance the quality of context collected in its vicinity. As such, the network will fuse information over a multiplicity of devices. The goal of this system is to utilize contextual information about the devices generating the data to better the selection process. The filtration process eliminates irregularities in the data as well as redundancy. Moreover, a weighted function stresses the value of data generated by higher-end nodes. Weight is also attributed to log-based evaluation protocols that identify a reliability metric. To further strengthen the fusion approach, local RRs will collect and aggregate context information from neighboring RR nodes, as well as knowledge databases over the Internet. As a load balancing measure, and to avoid resource draining, participating nodes will have an inversely proportional likelihood of participation in providing context information as their contribution count increases. Our system is further elaborated upon via an extensive use case.