Similarity computation between pairs of objects is often a bottleneck in many applications that have to deal with massive volumes of data. Motivated by applications such as collaborative filtering in large-scale recommender systems, and influence probabilities learning in social networks, we present new randomized algorithms for the estimation of weighted similarity in data streams.

Fingerprint-based location sensing technologies play an increasingly important role in pervasive computing applications due to their accuracy and minimal hardware requirements. However, typical fingerprint-based schemes implicitly assume that communication occurs over the same channel (frequency) during the training and the runtime phases. When this assumption is violated, the mismatches between training and runtime fingerprints can significantly deteriorate the localization performance. Additionally, the exhaustive calibration procedure required during training limits the scalability of this class of methods. In this work, we propose a novel, scalable, multi-channel fingerprint-based indoor localization system that employs modern mathematical concepts based on the Sparse Representations and Matrix Completion theories. The contribution of our work is threefold. First, we investigate the impact of channel changes on the fingerprint characteristics and the effects of channel mismatch on state-of-the-art localization schemes. Second, we propose a novel fingerprint collection technique that significantly reduces the calibration time, by formulating the map construction as an instance of the Matrix Completion problem. Third, we propose the use of sparse Bayesian learning to achieve accurate location estimation. Experimental evaluation on real data highlights the superior performance of the proposed framework in terms of reconstruction error and localization accuracy.

The broadcast nature of wireless networks has been widely exploited by adversaries in order to cause severe denial-of-service attacks. Several algorithms are proposed in the literature for the detection and mitigation of such attacks at the physical and medium access layers. In this work, we combine recent advances in compressed sensing theory, along with a cumulative-sum anomaly-based algorithm, for the detection of physical-layer attacks. The algorithm considers a metric based on the Signal-to-Interference-plus-Noise-Ratio (SINR). Compressed sensing makes feasible the use of far fewer SINR measurements for effective intrusion detection. The performance evaluation based on real experimental data shows that attacks are detected with high accuracy using a small number of measurements.