LAUSR

Establishing secure transmissions of messages among the resource limited sensor nodes in wireless sensor network (WSN) is a critical issue and requires secret keys to be established among the communicating nodes. Key predistribution is the most commonly used technique and whereby secret keys are preloaded to the sensor nodes before their deployment into a hostile region. A WSN can be structured or unstructured, and sensor nodes may be deployed in an ad-hoc manner or pre-planned manner into the target field. As sensor nodes are low-cost electronic devices equipped with data processing, limited storage, communication and computation power, connectivity, and resiliency are the major focus in designing key predistribution scheme (KPS) for WSNs. Furthermore, we also expect the KPS to be scalable, enabling insertion of a set of new nodes in WSN at any point of time without altering the key setup of the already existing nodes. Combinatorial design is a widely used mathematical tool for the KPS. However, most of the KPS developed by using combinatorial design are not scalable. In this article, rather than using combinatorial techniques, we employ a code-based approach and design a new method for key predistribution by building a communication model and a connectivity model. We exploit the Reed Solomon code to establish our communication model, integrate the binary Goppa code to derive our connectivity model, and skillfully blend these two models to construct our code-based KPS. A C implementation of our KPS confirms the significant performance gain over the existing similar works. Additionally, nodes in our KPS are all self-dependent for communication and do not rely on cluster heads. Furthermore, we have control over the choice of the parameters in the underlying codes which makes our KPS flexible. To be specific, prior knowledge of additional node deployment increases the scalability of our connectivity model. By suitably choosing the parameters of the Goppa code at prior, we can accommodate extra nodes. More interestingly, our communication model is scalable without any previous knowledge of deployment.