Abstract
With the development of wireless communication technologies which considerably contributed to the development of wireless sensor networks (WSNs), we have witnessed ever-increasing WSN-based applications which induced a host of research activities in both academia and industry. Since most of the target WSN applications are very sensitive, security issue is one of the major challenges in the deployment of WSN. One of the important building blocks in securing WSN is key management. Traditional key management solutions developed for other networks are not suitable for WSN, since WSN networks are resource (e.g., memory, computation, and energy) limited. Key pre-distribution algorithms have recently evolved as efficient alternatives of key management in these networks. Secure communication is achieved between a pair of nodes either by the existence of a key allowing for direct communication or by a chain of keys forming a key path between the pair. In this paper, we consider prior knowledge of network characteristics and application constraints in terms of communication needs between sensor nodes, and we propose methods to design key pre-distribution schemes, in order to provide better security and connectivity while requiring less resources. Our methods are based on casting the prior information as a graph. Motivated by this idea, we also propose a class of quasi-symmetric designs referred here to as g-designs. Our proposed key pre-distribution schemes significantly improve upon the existing constructions based on the unital designs. We give some examples and point out open problems for future research.
Original language | English (US) |
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Article number | 7331238 |
Pages (from-to) | 1842-1850 |
Number of pages | 9 |
Journal | IEEE Sensors Journal |
Volume | 16 |
Issue number | 6 |
DOIs | |
State | Published - Mar 15 2016 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Balanced incomplete block design
- graph
- key pre-distribution
- quasi-symmetric design
- sensor networks