Localisation of mobile nodes in wireless networks with correlated in time measurement noise.

Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated

Identity Verification with Speech Recognition : A Study

Since verification and authentication based on speech recognition are important develop-ments in the field of information security, this study revolves around the topic of speech-based Identity verification. The goal was to create a speech recognition system that can be embedded to an existing user authentication system. For this purpose, the study relied on a free speech modelling tool and modelled a limited dictionary for speech recognition. Speech samples were collected for training and testing purposes. The training sample was collected from three respondents and the testing data from seven, including those from previous three respondents. A freely available tool, HTK Toolkit, was used to train the model with speech samples recorded from respondents. The results showed that the accuracy of the model was dependent on whether the training da-taset included the users’ speech or not. The study supports the significance of real use of the model. However, the scope of the study is not large enough for authentication and verification system

Verifiably-safe software-defined networks for CPS

Next generation cyber-physical systems (CPS) are expected to be deployed in domains which require scalability as well as performance under dynamic conditions. This scale and dynamicity will require that CPS communication networks be programmatic (i.e., not requiring manual intervention at any stage), but still maintain iron-clad safety guarantees. Software-defined networking standards like OpenFlow provide a means for scalably building tailor-made network architectures, but there is no guarantee that these systems are safe, correct, or secure. In this work we propose a methodology and accompanying tools for specifying and modeling distributed systems such that existing formal verification techniques can be transparently used to analyze critical requirements and properties prior to system implementation. We demonstrate this methodology by iteratively modeling and verifying an OpenFlow learning switch network with respect to network correctness, network convergence, and mobility-related properties. We posit that a design strategy based on the complementary pairing of software-defined networking and formal verification would enable the CPS community to build next-generation systems without sacrificing the safety and reliability that these systems must deliver