We consider the problem of decentralized clustering and estimation over multitask networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which agents in their neighborhood belong to the same cluster. We propose a decentralized clustering algorithm aimed at identifying and forming clusters of agents of similar objectives, and at guiding cooperation to enhance the inference performance. One key feature of the proposed technique is the integration of the learning and clustering tasks into a single strategy. We analyze the performance of the procedure and show that the error probabilities of types I and II decay exponentially to zero with the step-size parameter. While links between agents following different objectives are ignored in the clustering process, we nevertheless show how to exploit these links to relay critical information across the network for enhanced performance. Simulation results illustrate the performance of the proposed method in comparison to other useful techniques.
In this article, a survey of techniques for tracking multiple targets in distributed sensor networks is provided and introduce some recent developments. The single target tracking in distributed sensor networks is reviewed. The tracking and resource management issues can be readily extended to MTT. The MTT problem is also briefly reviewed and describe the traditional approaches in centralized systems. Then focus on MTT in resource-constrained sensor networks and present two distinct example methods demonstrating how limited resources can be utilized in MTT applications. Finally, the most important remaining problems are discussed and suggest future directions We examine the design of self-organizing mobile adaptive networks with multiple targets in which the network nodes form distinct clusters to learn about and purse multiple targets, all while moving in a cohesive collision-free manner. We build upon previous distributed diffusion-based adaptive learning networks that focused on a single target to examine the case with multiple targets in which the nodes do not know the number of targets, and exchange local information with their neighbors in their learning objectives. In particular, we design a method allowing the nodes to switch the target they are tracking thereby engendering the formation of distinct stable learning groups that can split up and purse their distinct targets over time. We provide analytical mean stability and steady state mean-square deviation results along with simulations that demonstrate the efficacy of the proposed method. We propose the adaptive control and reconfiguration schemes for mobile wireless sensor networks (MWSN) to achieve timely and accurate mobile multi-target tracking (MMTT) with cost-effective energy consumption. In particular, our proposed schemes can detect the mobile multi-targets' random appearance and disappearance in the clutter environments with high accuracy and low energy cost. We develop the optimal mutual-information based techniques to adaptively control the reconfiguration of the proposed MWSN by designing the Distributed Decentralized Probability Hypothesis Density (DPHD) filtering algorithms. By dynamically adjusting the sensors' states, including their positions and activations, our schemes can efficiently improve the observabilities of the tracked multi-targets. We further analyze the asymptotic performance of our proposed schemes by deriving the upper-bounds of the detection-error probabilities. Also presented are the performance analyses which validate and evaluate our proposed adaptive control and reconfiguration schemes for MWSN in terms of the multi-target states estimation accuracy, the energy-consumption efficiency, and the robustness to the interference noise.