Neighborhood prediction based decentralized key management for mobile wireless networks

  • Authors:

  • Xiuyuan Zheng;Yingying Chen;Hui Wang;Hongbo Liu;Ruilin Liu

  • Affiliations:

  • Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA 07030;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA 07030;Department of Computer Science, Stevens Institute of Technology, Hoboken, USA 07030;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA 07030;Department of Computer Science, Stevens Institute of Technology, Hoboken, USA 07030

  • Venue:
  • Wireless Networks
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

The wireless data collected in mobile environments provides tremendous opportunities to build new applications in various domains such as Vehicular Ad Hoc Networks and mobile social networks. Storing the data decentralized in wireless devices brings major advantages over centralized ones. In this work, to facilitate effective access control of the wireless data in the distributed data storage, we propose a fully decentralized key management framework by utilizing a cryptography-based secret sharing method. The secret sharing method splits the keys into multiple shares and distributes them to multiple nodes. However, due to node mobility, these key shares may not be available in the neighborhood when they are needed for key reconstruction. To address this challenge, we propose the Transitive Prediction (TRAP) protocol that distributes key shares among devices that are traveling together. We develop three key distribution schemes that utilize the correlation relationship embedded among devices that are traveling together. Our key distribution schemes maximize the chance of successful key reconstruction and minimize the communication overhead. We provide theoretical analysis of the robustness and security of TRAP. Our simulation results, by using the generated data from city environment and NS-2 simulator, demonstrate the efficiency and effectiveness of our key distribution schemes.