Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page

Cyber-Physical Co-Design of Wireless Control Systems

Wireless sensor-actuator network (WSAN) technology is gaining rapid adoption in process industries because of its advantages in lowering deployment and maintenance cost in challenging environments. While early success of industrial WSANs has been recognized, significant potential remains in exploring WSANs as unified networks for industrial plants. This thesis research explores a cyber-physical co-design approach to design wireless control systems. To enable holistic studies of wireless control systems, we have developed the Wireless Cyber-Physical Simulator (WCPS), an integrated co-simulation environment that integrates Simulink and our implementation of WSANs based on the industrial WirelessHART standard. We further develop novel WSAN protocols tailored for advanced control designs for networked control systems. WCPS now works as the first simulator that features both linear and nonlinear physical plant models, state-of-art WirelessHART protocol stack, and realistic wireless network characteristics. A realistic wireless structural control study sheds light on the challenges of WSC and the limitations of a traditional structural control approach under realistic wireless conditions. Systematic emergency control results demonstrate that our real-time emergency communication approach enables timely emergency handling, while allowing regular feedback control loops to effectively share resources in WSANs during normal operations. A co-joint study of wireless routing and control highlights the importance of the co-design approach of wireless networks and control

Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Cooperative localization in agent networks based on interagent time-of-flight measurements is closely related to synchronization. To leverage this relation, we propose a Bayesian factor graph framework for cooperative simultaneous localization and synchronization (CoSLAS). This framework is suited to mobile agents and time-varying local clock parameters. Building on the CoSLAS factor graph, we develop a distributed (decentralized) belief propagation algorithm for CoSLAS in the practically important case of an affine clock model and asymmetric time stamping. Our algorithm allows for real-time operation and is suitable for a time-varying network connectivity. To achieve high accuracy at reduced complexity and communication cost, the algorithm combines particle implementations with parametric message representations and takes advantage of a conditional independence property. Simulation results demonstrate the good performance of the proposed algorithm in a challenging scenario with time-varying network connectivity.Comment: 13 pages, 6 figures, 3 tables; manuscript submitted to IEEE Transaction on Signal Processin

Cooperative Synchronization in Wireless Networks

Synchronization is a key functionality in wireless network, enabling a wide variety of services. We consider a Bayesian inference framework whereby network nodes can achieve phase and skew synchronization in a fully distributed way. In particular, under the assumption of Gaussian measurement noise, we derive two message passing methods (belief propagation and mean field), analyze their convergence behavior, and perform a qualitative and quantitative comparison with a number of competing algorithms. We also show that both methods can be applied in networks with and without master nodes. Our performance results are complemented by, and compared with, the relevant Bayesian Cram\'er-Rao bounds