Controlling Internet of Things devices with Read-Write Linked Data Interfaces using Data-Driven Work ows

Abstract

We present a rst rough prototype for our approach to specify and execute agents on Read-Write Linked Data that are given as data-driven work ows, speci cally we build on Guard-Stage-Milestone work ows. We showcase our approach in a setting from the Internet of Things, speci cally building automation. { Microservices [10] give access to business functions { Internet of Things devices with web interfaces (see e. g. the W3Cs Web of Things e ort1) allow for enacting change using actuators { Decentralised social networks such as SoLiD2 allow for standards-based digitised social interaction Overall, we see that besides the uniform interaction model of REST [4], or HTTP3, also semantic descriptions using the uniform data model of RDF4 nd considerable uptake. For this data-driven environment called Read-Write Linked Data [1], we want to specify and execute behaviour. Hence, we are currently investigating data-driven approaches such as Guard-Stage-Milestone [5] to workow modelling for their application as agent speci cations for Read-Write Linked Data. Data-driven approaches de ne the course of action using data, i. e. system state, whereas traditional ow-based approaches use the nishing of activities to de ne the course of action. Work ows in data-driven approaches are considered more exible than ow-driven approaches, e. g. it is easier in a data-driven approach to cover all exceptions and corner cases. We think that the data-driven 1 https://www.w3.org/2016/07/wot-ig-charter.html 2 https://solid.mit.edu/ 3 https://tools.ietf.org/rfc/rfc7230.txt 4 https://www.w3.org/TR/rdf11-concepts/

Introduction

The web can be argued to be at a stage where hypermedia agents could be applied [ 2 ], as its foundational technologies also for write access nd widespread use, for instance: approach provides a much better t to the web architecture than the ow-driven approach for two reasons: (1) in both, data-driven work ows and on the web, system state is the rst-class citizen (cf. REST { Representational State Transfer). (2) compared to traditional environments for work ow management, systems are typically more open and less under central control on the web. Thus, exibility, ie. the ability to cope with unforseen but possible execution paths in a work ow, is a desired property in a work ow approach for the web. For an example of data-drivenness and exibility, see our walk-through in Section 3.

The challenge is that properties of the environment play a major role in which work ow approaches are applied in what settings [ 3 ]. Therefore, we need to look at the assumptions of our environment Read-Write Linked Data when applying work ow technologies. Speci cally, we found that Read-Write Linked Data is fundamentally di erent from traditional environments where work ow technologies are applied, e. g. databases, in the following regards: The absence of events in HTTP In HTTP, there is no way for subscribing to events, which a controller could track for changes in the environment. Reasoning and querying under the Open-World Assumption Under the open-world assumption, we cannot consider information we cannot derive as false, i. e. we cannot rely on negation when specifying the controller.

In previous works, we have been working towards this challenge: As a basis for applying work ow approaches for specifying behaviour, we de ned ASM4LD, a model of computation for the environment of Read-Write Linked Data [ 7 ]. Thus, we can specify agent behaviour using rules. Based on this model of computation, we provided an approach to specify ow-driven work ows [ 8 ] and presented a corresponding demo [ 9 ]. In this demo, we present a data-driven approach instead.

Also, there is previous work in work ow management. Hull et al. developed the Guard-Stage-Milestone approach [ 5 ] for central data bases instead of the web architecture. Pautasso investigated how to retro t REST into ow-based work ows [ 11 ], whereas we t an approach for data-driven work ows into REST.

The demo shows our work on two main parts, a work ow ontology and operational semantics for the Guard-Stage-Milestone work ow language in the ASM4LD model of computation [ 7 ]. The contribution of the demo is a rst rough implementation for a simple use-case from the Internet of Things. This submission is accompanied by a web page5. We published a paper on the formal aspects of the approach [ 6 ].

The paper is structured as follows: In Section 2, we present the architecture of our demo and the set-up. Next, in Section 3, we describe how a visitor to our demo booth can interact with our demo. Last, in Section 4, we conclude. 2

Architecture and Set-Up

5 http://people.aifb.kit.edu/co1683/2019/gsm/semantics-demo/ Mock/Visualization Server http://mock-server.lan:3000 / → main page with info & links

Get mock state (/mock)

Linked Data-Fu When all servers run:

start with $ draft/run-curl.sh $ draft/run.sh

Get Workflow Instances

/ldbbc/[n] Submit LDBBC’s IP

/iphs Get sensor data Post speaker text

LDBBC http://tok450s.lan:8080/ldbbc

Get/Put Workflow Instances

/ldbbc/[n]

IoT http://t2-rest- … .lan component families: (1) A Linked Data Platform container to maintain work ow models and instances, (2) an execution engine for the work ows, (3) a grapical user interface (4) the actual devices to control. All components are connected via a network switch that can be coupled with a notebook or a wi router. We will now in turn provide more detail on the components: Linked Data Basic Basic Container (LDBBC) The LDBBC is an implementation6 of a Linked Data Platform Container[ 12 ]. We use LDBBC to maintain data about work ow models and instances as Linked Data. LDBBC is written in Java and we deploy it on Eclipse Jetty, a Servlet Container. Linked Data-Fu Linked Data-Fu7 is a Linked Data processing engine written in Java [ 13 ]. Linked Data-Fu can be programmed in a subset of the Notation3 language's syntactical features to interact with and reason over Linked Data. Speci cally, we deploy rules for our prototypical operational semantics of a work ow language8 on Linked Data-Fu.

Visualization We wrote a component in Node.js and Express that allows to visualize the work ow's state stored on the LDBBC. This visualisation reads the Linked Data about the work ow instance state and shows it as a simplied GSM work ow model with small annotations. The left part of Figure 2 shows a screenshot of this UI.

IoT Devices We have a number of physical and virtual IoT devices in our demo. For the physical devices, we use a set of Tessel 29 IoT boards with modules for RFID sensors, light sensors, and loudspeakers. Each board runs a Node.js/Express server providing a REST API to get state information with measurements as Linked Data, or to receive messages to be read out using the speaker. For the virtual devices, we rely on a similar implementation as 6 https://github.com/kaefer3000/ldbbc 7 https://linked-data-fu.github.io/ 8 https://github.com/nico1509/data-driven-workflows 9 https://tessel.io/ the visualisation UI (see the right part of Figure 2). Running out of boards, we implemented a counter using this UI.

Walkthrough

The scenario of our demo will be an evacuation work ow. The work ow represents the programming of an automatic evacuation control system. When approaching our stand, the visitor will see: a set of IoT devices with RFID sensors attached to interact with the work ow (used for representing alarm buttons and doors that have to be closed), speakers for noti cations by the work ow (used for giving evacuation instructions), and a screen showing status information about work ow instances (used for visualising the evacuation progress and for explanations on the overall system).

We show the work ow on the left side of Figure 2. Each activity, called stage in Guard-Stage-Milestone consists in an action, ie. here: an HTTP request that changes something, a guard, ie. a condition on the system state that must hold before the action is executed, and a milestone, ie. a ag that is set after the action nished. The guards and the milestones re ect the data-drivenness of this work ow approach: They determine the course of action based on system state, in contrast to ow-driven approaches such as BPMN, which only take the nishing of other activities in the arrows into account. As guards and milestones can get invalidated, deviations in the course of action, which call for exibility in the models, can easier be covered in a data-driven approach:

The control system then works as follows. The visitor initiates the evacuation work ow by placing an RFID card on the reader that represents a re alarm button. On the speaker, the work ow then announces a re alarm, and that (1) emergency will arrive, people have to be (2) evacuated and (3) counted. The visitor then can ful l stages by placing and releasing RFID cards on corresponding readers. The operational semantics poll the system state including whether cards are on the RFID readers. The presence of cards on readers is then part of our conditions. One of the features of the data-driven approach is that stages can be invalidated, and thus need to be re-executed or are halted. For instance, if the initially placed alarm card is removed, the whole emergency procedure is stopped. 4

Conclusion

Using this demo, we want to show our work-in-progress in applying data-driven work ows for controlling Read-Write Linked Data resources to the Semantic Web community. We present a rst rough implementation of our approach that ports the Guard-Stage-Milestone approach to the web architecture. We base our demo in the setting of Internet of Things, speci cally building automation, where currently a W3C community group is investigating web technologies for application10. 10 https://w3c-lbd-cg.github.io/bot/

References

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