Recent advances of hardware design and radio technologies have opened the way for an emerging category of network-enabled smart physical devices as a result of convergence in computing and wireless communication capabilities. Inspired by biological interactions, distributed processing of data collected by individual devices is now becoming crucial to let the nodes self-learn relevant network-state information and self-organize without the support of a central unit. Focus of this paper is twofold. First, a novel network channel model tailored for dense deployments is developed and validated on real data. The model describes relevant channel features that are representative of site-specific static/dynamic multipath fading and are shared by all links of a network. Second, a new class of distributed weighted-consensus strategies is introduced to support distributed network calibration and localization in device-to-device networks. Network calibration allows the devices to self-learn the common channel parameters, by successive refinements of local estimates and peer-to-peer information exchange. Network-localization enables each node to acquire augmented information about the whole network topology, by distributed learning from local channel observations. The proposed distributed algorithms guarantee a fast convergence and can replace conventional centralized schemes. An experimental case study is discussed in a representative indoor environment for the purpose of system validation. Experimental results show that the proposed method can significantly improve the performance of conventional solutions.

Consensus-based Algorithms for Distributed Network-State Estimation and Localization

SOATTI, GLORIA;NICOLI, MONICA BARBARA;SAVAZZI, STEFANO;SPAGNOLINI, UMBERTO
2017

Abstract

Recent advances of hardware design and radio technologies have opened the way for an emerging category of network-enabled smart physical devices as a result of convergence in computing and wireless communication capabilities. Inspired by biological interactions, distributed processing of data collected by individual devices is now becoming crucial to let the nodes self-learn relevant network-state information and self-organize without the support of a central unit. Focus of this paper is twofold. First, a novel network channel model tailored for dense deployments is developed and validated on real data. The model describes relevant channel features that are representative of site-specific static/dynamic multipath fading and are shared by all links of a network. Second, a new class of distributed weighted-consensus strategies is introduced to support distributed network calibration and localization in device-to-device networks. Network calibration allows the devices to self-learn the common channel parameters, by successive refinements of local estimates and peer-to-peer information exchange. Network-localization enables each node to acquire augmented information about the whole network topology, by distributed learning from local channel observations. The proposed distributed algorithms guarantee a fast convergence and can replace conventional centralized schemes. An experimental case study is discussed in a representative indoor environment for the purpose of system validation. Experimental results show that the proposed method can significantly improve the performance of conventional solutions.
Network localization, Distributed estimation, Consensus algorithms, Channel Modelling
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1000636
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