Load shedding based resource management techniques for RFID data

Abstract

RFID based systems are enjoying widespread adoption in a variety of application scenarios. Item tracking in a supply chain environment is one such application. From an application perspective, there are two challenges: (a) the data rates for large deployments are growing significantly; (b) the demands placed on the system for query processing in real time are also on the rise. Meeting these challenges in large-scale deployments is non trivial. The hardware base for RFID based systems compound these challenges due to the fact that RFID readers are error-prone and reliable reading of RFID tags is hampered by a number of physical limitations such as environmental conditions, and contents of the items carrying the tags. Naturally, the reliability of these systems become even more questionable when both the data rates increase and the need for real time processing of queries increases. We propose load shedding mechanisms that use the spatial and temporal properties of RFID deployments to combat the challenges due to increased demands for tag and query processing in real time. These mechanisms are piggy-backed on top of a middleware Reliable Framework for Radio Frequency IDentification (RF2ID)that uses redundancy to improve the reliability of RFID deployment. The basic idea in RF2ID is to use the spatial notion of a path taken by items flowing from source to destination. By cumulatively aggregating the tags collected by entities called Virtual Readers (VR) that are placed along the path, the total reliability of the system is enhanced. The VRs cooperatively shed the load under heavy load conditions. The built-in redundancy in the RF2ID system allows the VRs to shed load with reasonable system performance thus enhancing the overall reliability of the deployment. Two different load shedding strategies are proposed in the literature: space based approach and time based approach. These strategies have been implemented in the RF2ID middleware and performance results show the efficacy of these mechanisms for dealing with increased data rates.