Demo Abstract: RIAPS - A Resilient InformationArchitecture Platform for Edge Computing

William Emfinger∗, Abhishek Dubey∗, Scott Eisele∗, Peter Volgyesi∗, Janos Sallai∗, Gabor Karsai∗∗ Institute for Software-Integrated Systems, Vanderbilt University, Nashville, TN, USA

I. OVERVIEW

The emerging CPS/IoT ecosystem platforms such as Beagle-bone Black, Raspberry Pi, Intel Edison and other edge devicessuch as SCALE [3], Paradrop[6] are providing new capabilitiesfor data collection, analysis and processing at the edge [5](also referred to as Fog Computing). This allows the dynamiccomposition of computing and communication networks thatcan be used to monitor and control the physical phenomenacloser to the physical system. However, there are still a numberof challenges that exist and must be resolved before wesee wider applicability of these platforms for applications insafety-critical application domains such as Smart Grid andTraffic Control.

Two of the foremost challenges are related to resourceuncertainty and network uncertainty. The resource uncertaintyarises due to (a) the dynamism and heterogeneity of availableresources, and (b) the increased likelihood of failure becausethe computing resources at the edge are not operating in acontrolled environment, such as data centers. The networkuncertainty is caused, e.g., by the shared wireless channelsused at the edge that lead to varying network bandwidth.Additionally, security and resilience become important criticalrequirements and challenges at the edge, given that it ispossible that multiple applications will share the computingand sensing resources, and, unlike in cloud computing, virtualmachine- and container-based isolation are still not an easyoption to adopt given the resource-constrained nature of theedge-computing nodes.

Solving these challenges require us to develop novel ap-plication programming paradigms that help developers writeadaptive code to tolerate uncertainty, as well as to build aresilient application platform that not only provides core ser-vices like time synchronization, distributed data managementand coordination, but also provides mechanisms to deploy andremotely manage the distributed applications that have a longlifecycle. II. THE RIAPS PLATFORM

Our team is developing the core architecture, algorithms andprogramming paradigms for such a computing platform calledRIAPS (Resilient Information Architecture Platform for SmartGrid) [2]. The software platform provides services for a widevariety of software applications where the services include(but are not limited to): distributed time management, plannedand adaptive scheduling of computing and communicationresources, a robust fault management, communication andcoordination among concurrent activities, access to and control

Fig. 1: A RIAPS Node

of physical system devices (i.e. sensors and actuators), andprotection against and sustained operation of the system undersecurity attacks.

Developers will be enabled by the platform to implementadaptive intelligent applications that monitor, observe, control,and manage the physical system, while interacting with it atvarious physical locations, and on different levels of a sensingand control hierarchy.

Our immediate target for the platform is applications for theSmart Grid. However, as we will show in this demonstration,this architecture can also be used to design, deploy and manageapplications for other domains as well. The demonstrationdescribed below will illustrate how the RIAPS platform canbe used for implementing distributed, intelligent traffic lightcontrollers, and how these can be tested on a Hardware-In-the-Loop (HIL) testbed.

III. DEMONSTRATION SCENARIO AND THE TEST BED

This section presents the traffic intersection control demoscenario and Cyber-Physical System (CPS) testbed used fortesting and evaluation.

Testbed Architecture: The functions of a generic CPStestbed are 1) (re-)configurability with respect to the CPS,2) execution of the CPS software on a realistic computingplatform, 3) simulation or emulation of the communicationnetwork that connects the nodes, and 4) real-time simulation ofthe behavior of the sensors/actuators and the physical system.In addition to these concerns, the CPS testbed should behavesimilarly to the real system operating under fault conditions.

Because of these requirements, our CPS testbed was devel-oped as a Hardware-in-the-Loop Emulation platform, whereembedded computing nodes attached to emulated communi-

Fig. 2: Diagram showing the architecture of the CPS HIL testbed

cation network(s) and are connected to one or more real-timesimulators. The simulation hosts provide the embedded com-puters the ability to sense and control the (simulated) physicalsystems in which they will be deployed. The configurablenetwork emulator (through which the embedded computerscommunicate) is more accurate than a network simulator,as the hosts are using the same protocol stack and networkinterfaces as in the real world. For more information aboutthis testbed please see [4]. An architectural diagram of theCPS testbed is shown in Figure 2.

Scenario: As an example of edge computing devices inter-acting with each other and the real world (as a CPS), considera network of advanced traffic intersection controllers. Thesecontrollers have an array of local sensors connected to themby which they determine the state of the world around them.These sensors may be inductive loop sensors embedded in theroad, proximity sensors for wireless devices, and cameras forimage-based traffic monitoring. Furthermore, these devices donot simply control traffic lights, but also provide feedback andcooperative control to the vehicles on the roadways. Finally,these edge controllers communicate with each other to form acooperative control group both in their local area and as partof a larger control hierarchy.

To enable the evaluation of such a complex system onthe testbed, we had to find a suitable simulator that simu-lates with some fidelity the complex interactions. Since thecontrollers interact not only with each other but also thevehicles, pedestrians, and other edge computing nodes withinproximity, the simulator must allow the configuration of andinteraction with these simulated entities. Such a simulationallows the validation of edge computing devices and softwareapplications for a new kind of Smart City.

Cities:Skylines: Cities:Skylines [1] provides the mecha-nism for simulation of the people and objects within anentire city, handling many hundreds of thousands of civilianssimultaneously as they travel throughout the city by walking,driving, or taking some form of public transportation (or acombination of all three). Because Cities:Skylines was devel-oped with a rich modding API, developers are free to createadditions or replacements for the entities and the behaviorsin the simulation. Specifically, for this demo scenario, we de-velop a HIL simulation for edge-computing-based, intelligent

Fig. 3: Scenario showing the smart traffic lights and how they are controlledby embedded boards in the testbed with the rest of the system simulated byCities: Skylines

traffic lights that sense and communicate with the vehicles,pedestrians, and other local traffic lights to provide morecoordinated and cooperative control of the intersections. In thisscenario, the smart traffic light control algorithms execute onthe embedded hardware of the testbed, while the physics andbehavior of the vehicles and people of the city are simulatedwithin Cities: Skylines. The scenario makes use of our customnetwork interface mod for Cities: Skylines which provides theAPI over the network; this network API enables the controllercode on the testbed to query and configure the state of thetraffic lights and query the information about the vehicles andthe pedestrians (see figure 3).

Acknowledgment: The information, data, or work pre-sented herein was funded in part by the Advanced ResearchProjects Agency-Energy (ARPA-E), U.S. Department of En-ergy, under Award Number DE-AR0000666 and in part by agrant from Siemens, CT. The views and opinions of authorsexpressed herein do not necessarily state or reflect those of theUnited States Government or any agency thereof or Siemens,CT.

REFERENCES

[1] Cities skyline. http://www.citiesskylines.com.[2] Riaps. https://riaps.isis.vanderbilt.edu/redmine/projects/riaps.[3] K. Benson, C. Fracchia, G. Wang, Q. Zhu, S. Almomen, J. Cohn,

L. DâAZarcy, D. Hoffman, M. Makai, J. Stamatakis, et al. Scale: Safecommunity awareness and alerting leveraging the internet of things. IEEECommunications Magazine, 53(12):27–34, 2015.

[4] P. Kumar, W. Emfinger, and G. Karsai. Testbed to simulate and analyzeresilient cyber-physical systems. In Rapid System Prototyping, 2015. RSP’15., October 2015.

[5] L. M. Vaquero and L. Rodero-Merino. Finding your way in the fog:Towards a comprehensive definition of fog computing. SIGCOMMComput. Commun. Rev., 44(5):27–32, Oct. 2014.

[6] D. Willis, A. Dasgupta, and S. Banerjee. ParaDrop: A Multi-tenant Plat-form to Dynamically Install Third Party Services on Wireless Gateways.In Proceedings of the 9th ACM workshop on Mobility in the evolvinginternet architecture, pages 43–48. ACM, 2014.

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