A Decentralized Architecture for Active Sensor Networks

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms

An Analysis and Enumeration of the Blockchain and Future Implications

The blockchain is a relatively new technology that has grown in interest and potential research since its inception. Blockchain technology is dominated by cryptocurrency in terms of usage. Research conducted in the past few years, however, reveals blockchain has the potential to revolutionize several different industries. The blockchain consists of three major technologies: a peer-to-peer network, a distributed database, and asymmetrically encrypted transactions. The peer-to-peer network enables a decentralized, consensus-based network structure where various nodes contribute to the overall network performance. A distributed database adds additional security and immutability to the network. The process of cryptographically securing individual transactions forms a core service of the blockchain and enables semi-anonymous user network presence

Cooperative Air and Ground Surveillance

Unmanned aerial vehicles (UAVs) can be used to cover large areas searching for targets. However, sensors on UAVs are typically limited in their accuracy of localization of targets on the ground. On the other hand, unmanned ground vehicles (UGVs) can be deployed to accurately locate ground targets, but they have the disadvantage of not being able to move rapidly or see through such obstacles as buildings or fences. In this article, we describe how we can exploit this synergy by creating a seamless network of UAVs and UGVs. The keys to this are our framework and algorithms for search and localization, which are easily scalable to large numbers of UAVs and UGVs and are transparent to the specificity of individual platforms. We describe our experimental testbed, the framework and algorithms, and some results

Cooperative Air and Ground Survaillance

Unmanned aerial vehicles (UAVs) can be used to cover large areas searching for targets. However, sensors on UAVs are typically limited in their accuracy of localization of targets on the ground. On the other hand, unmanned ground vehicles (UGVs) can be deployed to accurately locate ground targets, but they have the disadvantage of not being able to move rapidly or see through such obstacles as buildings or fences. In this article, we describe how we can exploit this synergy by creating a seamless network of UAVs and UGVs. The keys to this are our framework and algorithms for search and localization, which are easily scalable to large numbers of UAVs and UGVs and are transparent to the specificity of individual platforms. We describe our experimental testbed, the framework and algorithms, and some results

Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions

Cooperative multi-robot teams need to be able to explore cluttered and unstructured environments while dealing with communication dropouts that prevent them from exchanging local information to maintain team coordination. Therefore, robots need to consider high-level teammate intentions during action selection. In this letter, we present the first Macro Action Decentralized Exploration Network (MADE-Net) using multi-agent deep reinforcement learning (DRL) to address the challenges of communication dropouts during multi-robot exploration in unseen, unstructured, and cluttered environments. Simulated robot team exploration experiments were conducted and compared against classical and DRL methods where MADE-Net outperformed all benchmark methods in terms of computation time, total travel distance, number of local interactions between robots, and exploration rate across various degrees of communication dropouts. A scalability study in 3D environments showed a decrease in exploration time with MADE-Net with increasing team and environment sizes. The experiments presented highlight the effectiveness and robustness of our method.Comment: 8 pages, 7 figure

Vulnerability Analysis of Modern Electric Grids: A Mathematical Optimization Approach

Electrical power must be transmitted through a vast and complicated network of interconnected grids to arrive at one’s fingertips. The US electric grid network and its components are rapidly advancing and adapting to the advent of smart technologies. Production of electricity is transitioning to sustainable processes derived from renewable energy sources like wind and solar power to decrease dependence on nonrenewable fossil fuels. These newly pervasive natures of smart technology and the variable power supply of renewable energy introduce previously unexamined vulnerabilities into the modern electric grid. Disruption of grid operations is not uncommon, and the effects can be economically and societally severe. Thus, a vulnerability analysis can provide decision makers with the ability to characterize points of improvement in the networks they supervise. This thesis performs a vulnerability analysis of electric grid operations including storage. This vulnerability analysis is achieved through a set of numerical experiments on a multi-period optimal power flow model including storage and variable demand. This model resulted in an analysis indicating storage is helpful in increasing resilience in networks with excess generation, no matter how severe the disruption. Networks with constrained generation benefit little, if at all, from storage. This analysis allows us to conclude careful implementation is the best way to improve electric grid security in the face of widespread use of renewable energy and smart technology

Societal assessment overview

The decision to proceed with SPS depends on a political determination that commitment of the economic, institutional, and social energies required for its implementation is a worthwhile investment. This determination is national (and international) in scope and is based on knowledge of the environmental and societal impacts of the SPS, its projected economics and technological risks, expressed through the influence of contending segments of society. To assist the decision makers, an assessment of societal issues associated with the SPS was undertaken as part of the Concept Development and Evaluation Program. Results of the assessment are reported. The primary societal assessment objectives are to determine if the societal ramifications of an SPS might significantly impede its development, and to establish an information base regarding these issues. Estimates regarding SPS impacts commensurate with its stage of development and the needs of the decision makers are provided