P Krishnan

Bayesian indoor positioning systems

In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model eliminates the requirement for training data as compared with existing approaches, thereby introducing the notion of a fully adaptive zero profiling approach to location estimation.

Tackling security vulnerabilities in VPN-based wireless deployments

Current best practice recommendations for enterprise wireless deployments suggest the use of VPNs from a wireless client for both authentication and privacy. In this paper, we demonstrate a security issue with such deployments, which we refer to as the hidden wireless router vulnerability. This vulnerability is inherent in the VPN-based wireless LAN architecture, and leads to unsuspecting clients becoming conduits for an attack, exploiting features readily available in popular operating systems like Windows and Linux. We describe the attack scenario, and possible solutions for both detecting and locating such hidden wireless routers. Our solutions include a range of possibilities stretching from purely passive to active probing methods, and access point-based solutions. We describe our techniques and results of our implementation and experiments.

Location Estimation in Wireless Networks: A Bayesian Approach

We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model drastically reduces the requirement for training data as compared with existing approaches.