Predictive protocol for the scalable identification of RFID tags through collaborative readers

• Published in: Computers in industry Vol. 63; no. 6; pp. 557 - 573
• Main Authors: Trujillo-Rasua, Rolando, Solanas, Agusti, Pérez-Martínez, Pablo A., Domingo-Ferrer, Josep
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-08-2012; Elsevier Sequoia S.A
• Subjects: Collaboration; Computer information security; Global marketing; Information sharing; Management; Optical bar code readers; Privacy; Protocol; Radio frequency identification; Readers; RFID; Scalable collaboration; Secure information sharing; Studies; Supply chain management; Supply chains; Tags; Trajectory analysis; Scalable collaboration; Privacy; RFID; Secure information sharing; Trajectory analysis
• ISSN: 0166-3615; 1872-6194
• DOI: 10.1016/j.compind.2012.03.005

Radio frequency identification (RFID) is a technology aimed at efficiently identifying products that has greatly influenced the manufacturing businesses in recent years. Although the RFID technology has been widely accepted by the manufacturing and retailing sectors, there are still many issues regarding its scalability, security and privacy.
With regard to privacy, the sharing of identification information amongst multiple parties is also an issue (especially after the massive outsourcing that is taking place in our global market). Securely and efficiently sharing identification information with multiple parties is a tough problem that must be considered so as to avert the undesired disclosure of confidential information. Specially in the context of supply chain management.
In this article, we propose a private and scalable protocol for RFID collaborative readers to securely identify RFID tags. We define the general concepts of “next reader predictor” (NRP) and “previous reader predictor” (PRP) used by the readers to predict the trajectories of tags and collaborate efficiently. We also propose a specific Markov-based predictor implementation. By the very nature of our distributed protocol, the collaborative readers can naturally help in mitigating the problem of sharing identification information amongst multiple parties securely, which is essential in the context of supply chain management. The experimental results show that our proposal outperforms previous approaches.