|Wednesday, December 7|
|10:00 - 11:00|
|Keynote: RMN-K1: Angelia Nedic - Distributed Hypothesis Testing on Graphs|
|11:00 - 12:20|
|Keynote: RMN-K2: Pramod Varshney - On Optimization of Sensor Management Policies for Distributed Estimation|
|14:00 - 15:40|
|RMN-1: Distributed Information Processing, Optimization, and Resource Management over Networks I|
|16:10 - 17:30|
|RMN-P1: Distributed Information Processing, Optimization, and Resource Management over Networks Poster|
|Thursday, December 8|
|14:00 - 15:40|
|RMN-2: Distributed Information Processing, Optimization, and Resource Management over Networks II|
We will consider the problem of distributed cooperative non-Bayesian learning in a network of agents, where the agents are repeatedly gaining partial information about an unknown random variable whose distribution is to be jointly estimated. The joint objective of the agent system is to globally agree on a hypothesis (distribution) that best describes the observed data by all agents in the network. Interactions between agents occur according to an unknown sequence of time-varying graphs. We highlight some interesting aspects of Bayesian learning and stochastic approximation approach for the case of a single agent, which has not been observed before and it allows for a new connection between optimization and statistical learning. Then, we discuss and analyze the general case where subsets of agents have conflicting hypothesis models, in the sense that the optimal solutions are different if the subset of agents were isolated. Additionally, we provide a new non-Bayesian learning protocol that converges an order of magnitude faster than the learning protocols currently available in the literature for arbitrary fixed undirected graphs. Our results establish consistency and a non-asymptotic, explicit, geometric convergence rate for the learning dynamics.
Wireless Sensor Networks (WSNs) often operate in environments where available energy and bandwidth are limited. It is imperative that suitable resource management policies be adopted to maximize system performance while prolonging the lifetime of the WSN. This talk will provide a review of the current state-of-the-art of sensor management approaches for distributed estimation problems. This will be followed by a more detailed discussion on optimization of sensor management policies for distributed estimation including sensor selection, sensor scheduling and sensor collaboration. Sensor management for distributed estimation in crowdsourcing based WSNs will also be discussed.
With the rapid advances in sensing, communication, and storage technologies, distributed data acquisition is now ubiquitous in many areas of engineering, biological, and social sciences. For example, the large-scale implementation of advanced metering systems in the smart grids enables real time collection of a huge amount of distributed data (voltages, phases, etc), the understanding of which is critical in improving the overall performance of the future power systems. More examples of distributed data include high-resolution videos from a network of surveillance systems, interactions on a social network, environmental data from sensor networks.Timely and effectively processing of such large amount of distributed, and possibly corrupted and/or online data requires not only novel data processing techniques, but also a deep understanding of the underlying network properties of physical systems, including the network topology, the processing capability of each distributed node, the nature of the data, etc. These sophisticated characteristics bring new challenges for the design and analysis of optimization and resource management algorithms.This symposium aims to bring together researchers and experts in the fields of signal processing, control, optimization, network sciences, cyber-physical systems to address the emerging challenges related to this topic. Emphasis will be given to theories and applications for distributed signal processing systems, cyber-physical systems as well as advanced distributed control and optimization techniques.
Submissions are welcome on topics including:
Prospective authors are invited to submit full-length papers, with up to four pages for technical content including figures and possible references, and with one additional optional 5th page containing only references. Manuscripts should be original (not submitted/published anywhere else) and written in accordance with the standard IEEE double-column paper template. Submission is through the GlobalSIP website at http://2016.ieeeglobalsip.org/Papers.asp.
|Paper Submission Deadline|
|Review Results Announced|
|Camera-Ready Papers Due||September 30, 2016|
Necdet Serhat Aybat, Mingyi Hong and Qing Ling