Theme 1. Driver Applications

This theme consists of three applications that emphasize key requirements of physically-coupled cognitive perceptual systems. They serve as key integrator and demonstrator of technical advances pursued throughout the CONIX team. Each application was chosen to highlight unique operating points in each CONIX design dimension.


Mixed Reality Systems

This application aims to merge physical and virtual elements in AR/VR systems with a collaborative digital teleportation application. Unlike existing AR telepresence applications, we will focus on tightly integrating physical components in the user’s space, including their own bodies and facial expressions, with the virtual world providing two-way remote interaction. This requires extremely low-latency local control mixed with low-latency or latency-masking wide-area connectivity.


Autonomous Systems

This application will focus on creating on-demand live sensory information feeds for decision-makers from a large-scale swarm of cooperating drones and ground-based autonomous systems controlled by an individual or small group of human agents. This application will highlight the ability of the CONIX distributed architecture to support real-time collaboration among humans, algorithms, and machines in a safe, robust, and secure manner in an information-rich, rapidly-evolving tactical environment.


Smart Cities

This application will look at mechanisms for managing and processing millions of sensors’ feeds in urban environments. This will involve deploying highly reconfigurable CONIX edge devices that will monitor and visualize the flow of pedestrians through urban spaces. At scale, this technology would aid public safety officials, urban planners, and stakeholders interested in infrastructure management.


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Theme 2. Hardware/Software Platforms

Theme 2 will create the hardware support for mixed reality applications, networking, and reliable and long-lifetime sensor platforms. In Task 2.1, the team will develop highly-constrained energy harvesting devices, whereas Task 2.2 is dedicated to mobile and wearable platforms with advanced communication and sensing capabilities and platforms (such as gateways) to support the challenges of deploying reliable and long lifetime sensors.

2.1 Near-Zero Power Platforms (Dutta, Gupta, Lucia)

The result of this task will be hardware platforms and design methodologies for extremely constrained M-class processor systems with the goal of perpetual energy-harvesting operation. These platforms will expose interfaces that facilitate mapping from high-level application constraints and behavioral policies to hardware platforms that carry out a computation, under extreme environmental constraints, such as energy intermittency.

2.2 Sensing and Interaction (Gupta, Harrison, Hoe, Rabaey, Rowe)

This task will focus on the class of algorithms and hardware platforms for next generation sensing systems for application-class processors. This includes several interaction technologies, such as wearable speech processing systems, human and scene capture technologies, and mobile interface platforms like AR/VR headsets, drones and other wearables.


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Theme 3. Security

The overarching goal of Theme 3 is to develop the foundations for enabling secure and privacy- preserving technologies for future CONIX-enabled applications. To this end, Theme 3 tries to tackle fundamental challenges that encompass policy abstractions for expressing these capabilities, platforms for enforcing these policies, and capabilities for learning to tackle advanced adversarial strategies.

3.1 Trustworthy Components (Parno, Sekar, Srivastava, Stefan)

The focus of this task will be to develop the tools and methodologies CONIX needs in order to construct a secure distributed foundation that comes with formal end-to-end guarantees of correctness, security, and performance.

3.2 Resilient and Secure Networks (Culler, Parno, Sekar, Stefan)

This task will develop the platforms and algorithms CONIX needs in order to construct resilient network security capabilities to handle a continuously evolving attack landscape.

3.3 Secure Programming (Parno, Srivastava, Stefan)

In this task, CONIX will develop the tools and mechanisms that will allow developers to build and safely deploy secure, privacy-preserving applications atop the CONIX platform.

3.4 Learning for Security and Privacy (Bilmes, Sekar)

The focus of this task will be to develop the tools, algorithms, and methodologies to model advanced security and privacy threats and adaptive adversaries in a CONIX deployment.


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Theme 4. Machine Learning

The primary goal of this Theme is to add intelligence to the network and to integrate machine learning, AI, and intelligent control capabilities, throughout the nodes of a network and throughout the various other Themes in the center. This theme is divided into two taks, where one is dedicated to develop and apply machine learning techniques and the other is focused on foundations that will lead to the next generation of ML systems.

4.1 Learning-Enabled Systems (Li, Rabaey, Srivastava)

Our approach is to refine existing and develop novel and specialized machine learning approaches to provide smart algorithms throughout the CONIX infrastructure and that can be applied in many Themes. For example, in-network distributed summarization (see below) will allow massive amounts of information to flow throughout a network without saturating capacities. DDoS attacks will be met with distributed ”defense of service” procedures, where smart actors perpetually monitor network state and behave collectively by acting locally. Our philosophy is that the network is the brain — this means that global behavioral changes in a network can be achieved through local integrated intelligence that is distributed everywhere.

4.2 ML Foundations (Bilmes, Smith)

Problems involving massive unstructured multi-modal streaming real-time data are best served using modern machine learning (ML) and artificial intelligence (AI) approaches, where humans code indirectly (i.e., write algorithms to learn other algorithms) rather than directly. Our contribution will develop ML and AI foundations within the CONIX networked and distributed computing substrate. This work will include stream processing, data summarization and federated machine learning.


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Theme 5. Communication, Positioning and Control

This theme will address the necessary wireless communication, localization and timing primitives that are necessary for the correct and efficient operation of distributed perception-cognition-action applications.

5.1 Positioning, Navigation and Timing (Dutta, Govindan, Rowe, Srivastava)

The Task will develop building blocks for providing applications with access to spatial and temporal information and associated services, particularly those needed by the driver applications in Theme 1. This includes multiple indoor localization systems ranging from visible light communication and ultrasound to Ultra-Wide Band ranging radios and RF beamforming approaches.

5.2 Wireless Communication (Cabric, Dutta, Rowe, Wawrzynek)

This task will address some of the communication building blocks for wireless platforms. We plan to expose low-level radio functionality to the software stack and develop a software-defined radio infrastructure for low-power wide-area networking. This will enable better network resource usage and increased capabilities. This task will also work closely with ComSenTer in order to identify system solutions for future high-bandwidth communication channels including localization, hand-off and networks where beamforming and antenna arrays can be managed by autonomous agents.


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Theme 6. Programming and Resource Management

The CONIX context presents a unique challenge in the development of robust distributed systems with a high degree of flexibility and programmability, extreme heterogeneity of components, and coordination mechanisms. Theme 6 will tackle this challenge.

6.1 Programmer-visible Abstractions (Lucia, Bodik, Gupta, Srivastava, Stefan, Govindan, Dutta)

This task defines macroprogramming abstractions for single-tier programming of the distributed system at scale, spanning from sensors on the edge to compute servers in the core. The abstractions will include new hybrid information flow mechanisms to ensure security (see Task 3.2).

6.2 Coordination and Mapping (Srivastava, Dutta, Bodik, Govindan)

This task will define and implement the system support to map programs written in our novel application-level abstractions onto the abstractions of the target virtual platforms that support key CONIX applications, translating a specification into functionality. This task is closely related to Task 6.1, which permits a logically centralized specification of application logic and correctness checking; our task permits high-level specification of performance goals and optimization hints. Today’s cloud systems have pioneered similar approaches but cannot be easily extended to a multi-tier distributed computing substrate with heterogeneity in computing and communication capabilities across tiers.


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