Distributed Signal Processing Algorithms for Wireless Networks

Distributed signal processing algorithms have become a key approach for statistical inference in wireless networks and applications such as wireless sensor networks and smart grids. It is well known that distributed processing techniques deal with the extraction of information from data collected at nodes that are distributed over a geographic area. In this context, for each specific node, a set of neighbor nodes collect their local information and transmit the estimates to a specific node. Then, each specific node combines the collected information together with its local estimate to generate an improved estimate. In this thesis, novel distributed cooperative algorithms for inference in ad hoc, wireless sensor networks and smart grids are investigated. Low-complexity and effective algorithms to perform statistical inference in a distributed way are devised. A number of innovative approaches for dealing with node failures, compression of data and exchange of information are proposed and summarized as follows: Firstly, distributed adaptive algorithms based on the conjugate gradient (CG) method for distributed networks are presented. Both incremental and diffusion adaptive solutions are considered. Secondly, adaptive link selection algorithms for distributed estimation and their application to wireless sensor networks and smart grids are proposed. Thirdly, a novel distributed compressed estimation scheme is introduced for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is also presented and an algorithm is developed to optimize measurement matrices. Lastly, a novel distributed reduced-rank scheme and adaptive algorithms are proposed for distributed estimation in wireless sensor networks and smart grids. The proposed distributed scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by a reduced–dimension parameter vector

Localization in Spatially Correlated Shadow-Fading Environment

Στην διπλωματική αυτή εργασία ενδιαφέρομαστε για Received Signal Strength (RSS) localization λόγω της ενγενης απλοτιτας του οπου ο καθε δεκτης μετρα ισχυ. Η διατριβή παρουσιάζει τόσο θεωρητικα όσο και πειραματικά αποτελέσματα. Στην αρχη κατασκευαζεται και παρουσιάζεται ένα νέο θεωρητικό όριο για το πρόβλημα εντοπισμού μίας πηγής σε χωρικα συσχέτισομενο περιβαλον, με χρησει conditional measurments και στη συνέχεια να χρησιμοποιείτε για την αξιολόγηση των επιδόσεων. Επιπλέον, παρουσιάζονται ορισμένα θεωρητικά αποτελέσματα στο πιο δύσκολο πρόβλημα του εντοπισμού πολλαπλών πηγών και πάλι για την περίπτωση του χωτικα συσχετιζομενου shadow fading περιβαλοντος. Αυτά τα αποτελέσματα δεν χρεισιμοποιουν contitionla measurments, αλλά δείχνουν πώς η απόδοση σχετιζετε με τον αριθμό των δεκτων, των αριθμό των άγνωστων πηγων, και ο συντελεστής συσχέτισης του περιβάλλοντος. Επιπλέον, δύο πειραματικές εκστρατειες εσωτερικου χωρου περιλαμβάνονται στην παρούσα διατριβή, και στις δύο χρησιμοποιηθηκε η OpenAirInterface (OAI) πλατφόρμα. Ο κύριος στόχος των εκστρατειών ήταν 1) να εξακριβώσει η ύπαρξη χωρικης συσχετισεις του shadow fading για εσοτερικο χωρο και 2) να χρησιμοποιούν ad-hoc τεχνικές και αλγόριθμοι, προκειμένου να επιτευχθεί κάποιο όφελος από την χωρική συσχέτιση υποθέτοντας γνώση contitional measurments.In this thesis we are interested on Received Signal Strength (RSS) localization due to its simplicity as every radio measures power. Thesis presents both theoretical as well as experimental results. We derive and present a new theoretical bound for the single-source localization problem that takes spatial- correlation as well as conditional measurements into account and then uses it to assess performance. Furthermore, presents some theoretical results in the more challenging multi-source localization problem again for the case of correlated shadow-fading environments. These results did not assume prior knowledge of conditional measurements, but show how the localization performance scales with respect to the number of sensors, the number of unknown sources, and the correlation coefficient of the environment. Additionally, two indoor experimental campaigns are included in this thesis, both of them used the OpenAirInterface (OAI) platform. The main target of the campaigns was 1) to verify the existence of shadow-fading in the indoor environment and 2) to use ad-hoc techniques in our localization algorithms in order achieve some gain from spatial correlation assuming knowledge of conditional measurements.