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1. CyPhyHouse: A programming, simulation, and deployment toolchain for heterogeneous distributed coordination

   https://doi.org/10.1109/ICRA40945.2020.9196513  

   Ghosh, Ritwika; Jansch-Porto, Joao P.; Hsieh, Chiao; Gosse, Amelia; Jiang, Minghao; Taylor, Hebron; Du, Peter; Mitra, Sayan; Dullerud, Geir (May 2020, ICRA, 2020)

   Programming languages, libraries, and development tools have transformed the application development processes for mobile computing and machine learning. This paper introduces CyPhyHouse-a toolchain that aims to provide similar programming, debugging, and deployment benefits for distributed mobile robotic applications. Users can develop hardware-agnostic, distributed applications using the high-level, event driven Koord programming language, without requiring expertise in controller design or distributed network protocols. The modular, platform-independent middleware of CyPhyHouse implements these functionalities using standard algorithms for path planning (RRT), control (MPC), mutual exclusion, etc. A high-fidelity, scalable, multi-threaded simulator for Koord applications is developed to simulate the same application code for dozens of heterogeneous agents. The same compiled code can also be deployed on heterogeneous mobile platforms. The effectiveness of CyPhyHouse in improving the design cycles is explicitly illustrated in a robotic testbed through development, simulation, and deployment of a distributed task allocation application on in-house ground and aerial vehicles. 

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2. Open storm: a complete framework for sensing and control of urban watersheds

   https://doi.org/10.1039/C7EW00374A  

   Bartos, Matthew; Wong, Brandon; Kerkez, Branko (January 2018, Environmental Science: Water Research & Technology)

   Leveraging recent advances in technologies surrounding the Internet of Things , “smart” water systems are poised to transform water resources management by enabling ubiquitous real-time sensing and control. Recent applications have demonstrated the potential to improve flood forecasting, enhance rainwater harvesting, and prevent combined sewer overflows. However, adoption of smart water systems has been hindered by a limited number of proven case studies, along with a lack of guidance on how smart water systems should be built. To this end, we review existing solutions, and introduce open storm —an open-source, end-to-end platform for real-time monitoring and control of watersheds. Open storm includes (i) a robust hardware stack for distributed sensing and control in harsh environments (ii) a cloud services platform that enables system-level supervision and coordination of water assets, and (iii) a comprehensive, web-based “how-to” guide, available on open-storm.org, that empowers newcomers to develop and deploy their own smart water networks. We illustrate the capabilities of the open storm platform through two ongoing deployments: (i) a high-resolution flash-flood monitoring network that detects and communicates flood hazards at the level of individual roadways and (ii) a real-time stormwater control network that actively modulates discharges from stormwater facilities to improve water quality and reduce stream erosion. Through these case studies, we demonstrate the real-world potential for smart water systems to enable sustainable management of water resources. 

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3. VIVE: Virtualization of Vehicular Electronics for System-level Exploration

   Kabir, R; Mishra, N; Ray, S. (January 2021, 24th IEEE International Conference on Intelligent Transportation (ITSC 2021))

   null (Ed.)

   We develop a virtual prototyping infrastructure for modeling and simulation of automotive systems. We focus on exercising and exploring use cases involving system-level coordination of vehicular electronics, sensors, and software. In current practice, such use cases can only be explored late in the design when all the relevant hardware components are available. Any design change, e.g., for optimization or security or even functional errors found during the exploration, incurs prohibitive cost at that stage. Our solution is a flexible, configurable prototyping platform that enables the user to seamlessly add new system-level use cases. Unlike other related prototyping environments, the focus of our platform is on communication and coordination among different components, not the computation of individual Electronic Control Units. We report on the use of the platform for implementing several realistic usage scenarios on automotive platforms and exploring the effects of their interaction. In particular, we show how to use the platform to develop real-time in-vehicle communication optimizers for different optimization targets. 

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4. SwitchLog: A Logic Programming Language for Network Switches

   https://doi.org/10.1007/978-3-031-24841-2_12  

   Mehta, Vaibhav; Loehr, Devon; Sonchack, John; Walker, David (January 2023, Practical Aspects of Declarative Languages)

   Hanus, Michael; Inclezan, Daniela (Ed.)

   The development of programmable switches such as the Intel Tofino has allowed network designers to implement a wide range of new in-network applications and network control logic. However, current switch programming languages, like P4, operate at a very low level of abstraction. This paper introduces SwitchLog, a new experimental logic programming language designed to lift the level of abstraction at which network programmers operate, while remaining amenable to efficient implementation on programmable switches. SwitchLog is inspired by previous distributed logic programming languages such as NDLog, in which programmers declare a series of facts, each located at a particular switch in the network. Logic programming rules that operate on facts at different locations implicitly generate network communication, and are updated incrementally, as packets pass through a switch. In order to ensure these updates can be implemented efficiently on switch hardware, SwitchLog imposes several restrictions on the way programmers can craft their rules. We demonstrate that SwitchLog can be used to express a variety of networking applications in a mere handful of lines of code. 

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5. Cloud Is Closer Than It Appears: Revisiting the Tradeoffs of Distributed Real-Time Inference

   Sharma, Pragya; Qiu, Hang; Srivastava, Mani (August 2025, IEEE)

   The increasing deployment of deep neural networks (DNNs) in cyber-physical systems (CPS) enhances perception fidelity, but imposes substantial computational demands on execution platforms, posing challenges to real-time control deadlines. Traditional distributed CPS architectures typically favor on-device inference to avoid network variability and contention-induced delays on remote platforms. However, this design choice places significant energy and computational demands on the local hardware. In this work, we revisit the assumption that cloud-based inference is intrinsically unsuitable for latency-sensitive control tasks. We demonstrate that, when provisioned with high-throughput compute resources, cloud platforms can effectively amortize network and queueing delays, enabling them to match or surpass on-device performance for real-time decision-making. Specifically, we develop a formal analytical model that characterizes distributed inference latency as a function of the sensing frequency, platform throughput, network delay, and task-specific safety constraints. We instantiate this model in the context of emergency braking for autonomous driving and validate it through extensive simulations using real-time vehicular dynamics. Our empirical results identify concrete conditions under which cloud-based inference adheres to safety margins more reliably than its on-device counterpart. These findings challenge prevailing design strategies and suggest that the cloud is not merely a feasible option, but often the preferred inference location for distributed CPS architectures. In this light, the cloud is not as distant as traditionally perceived; in fact, it is closer than it appears. 

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