Load shedding based resource management techniques for RFID data

Abstract

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 (RF 2 ID)that uses redundancy to improve the reliability of RFID deployment. The basic idea in RF 2 ID 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 RF 2 ID 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 RF 2 ID middleware and performance results show the efficacy of these mechanisms for dealing with increased data rates.

Dynamic Aggregate: An Elastic Framework for QoS-Aware Distributed Processing of RFID Data on Enterprise Hierarchy

“…The SMURF filter proposed by Jeffery et al views RFID streams as a statistical sample of tags and exploits sampling techniques for adaptive cleaning of RFID data by continuously adjusting the sliding window size [20]. Ahmed and Ramachandran exploited spatial and temporal properties of RFID deployments to propose load shedding techniques for growing data rates due to tag readings or query processing demands [21]. While sharing some of the goals as our research, none of the above works provide a comprehensive framework for QoS-aware dynamic aggregation and deaggregation of RFID data streams.…”

GuardianAngel: An RFID-based indoor guidance and monitoring system

“…This allows scaling up the pervasive environment since the behavior of the real and emulated readers are indistinguishable. For more details on the emulated readers please refer to[13).…”