Value-of-Information based Data Collection in Underwater Sensor Networks

Underwater sensor networks are deployed in marine environments, presenting specific challenges compared to sensor networks deployed in terrestrial settings. Among the major issues that underwater sensor networks face is communication medium limitations that result in low bandwidth and long latency. This creates problems when these networks need to transmit large amounts of data over long distances. A possible solution to address this issue is to use mobile sinks such as autonomous underwater vehicles (AUVs) to offload these large quantities of data. Such mobile sinks are called data mules. Often it is the case that a sensor network is deployed to report events that require immediate attention. Delays in reporting such events can have catastrophic consequences. In this dissertation, we present path planning algorithms that help in prioritizing data retrieval from sensor nodes in such a manner that nodes that require more immediate attention would be dealt with at the earliest. In other words, the goal is to improve the Quality of Information (QoI) retrieved. The path planning algorithms proposed in this dissertation are based on heuristics meant to improve the Value of Information (VoI) retrieved from a system. Value of information is a construct that helps in encoding the valuation of an information segment i.e. it is the price an optimal player would pay to obtain a segment of information in a game theoretic setting. Quality of information and value of information are complementary concepts. In this thesis, we formulate a value of information model for sensor networks and then consider the constraints that arise in underwater settings. On the basis of this, we develop a VoI-based path planning problem statement and propose heuristics that solve the path planning problem. We show through simulation studies that the proposed strategies improve the value, and hence, quality of the information retrieved. It is important to note that these path planning strategies can be applied equally well in terrestrial settings that deploy mobile sinks for data collection

Confidence Propagation through CNNs for Guided Sparse Depth Regression

Generally, convolutional neural networks (CNNs) process data on a regular grid, e.g. data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open research problem with numerous applications in autonomous driving, robotics, and surveillance. In this paper, we propose an algebraically-constrained normalized convolution layer for CNNs with highly sparse input that has a smaller number of network parameters compared to related work. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. We also propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. To integrate structural information, we also investigate fusion strategies to combine depth and RGB information in our normalized convolution network framework. In addition, we introduce the use of output confidence as an auxiliary information to improve the results. The capabilities of our normalized convolution network framework are demonstrated for the problem of scene depth completion. Comprehensive experiments are performed on the KITTI-Depth and the NYU-Depth-v2 datasets. The results clearly demonstrate that the proposed approach achieves superior performance while requiring only about 1-5% of the number of parameters compared to the state-of-the-art methods.Comment: 14 pages, 14 Figure

Propagating Confidences through CNNs for Sparse Data Regression

In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance. To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. Furthermore, we propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. Comprehensive experiments are performed on the KITTI depth benchmark and the results clearly demonstrate that the proposed approach achieves superior performance while requiring three times fewer parameters than the state-of-the-art methods. Moreover, our approach produces a continuous pixel-wise confidence map enabling information fusion, state inference, and decision support.Comment: To appear in the British Machine Vision Conference (BMVC2018