PROtEUS++: A Self-managed IoT Workflow Engine with Dynamic Service Discovery

Abstract

Despite ofering various advantages, the usage of Business Process Management technologies to orchestrate workflows in the Internet of Things (IoT) is still in its infancy. In this work, we demonstrate an extended version of our PROtEUS process execution system for IoT. Besides the processing of sensor streams and interacting with humans, PROtEUS++ is capable of dynamic service invocation as well as selfmanagement to detect and repair errors that happen during process execution. We show the system executing various dynamic and errorprone processes in the Smart Home.

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Introduction

PROtEUS Base System PROtEUS++: A Self-managed IoT Workflow Engine

Process Execution Engine

Distribution

Manager

Process

Manager

Human Task

Handler

Local Service Platform

Service Service Service Actuator

WebSocket Server

Service Invoker

CEP Adapter

Remote

Engine Client

Management

Client

Interactive

Client

Service Sensor Actuator

Semantic

Access

Layer

Service

Actuator Sensor Sensor

PROtEUS

CEP Engine

Process Goals Feedback Service Monitor Analyzer Planner Executor Knowledge Base PROtEUS is extended with a Semantic Access Layer (SAL) [ 2 ] as shown in Fig. 2. The core of this service is a knowledge base that contains information about all IoT sensors and actuators, their capabilities and contexts, as well as their associated IoT services and interfaces. User defined SPARQL queries can be sent to the SAL from a process activity to find and invoke IoT services in a specified context at runtime. If an IoT service can be found as a process resource, a call to our IoT middleware (here: OpenHAB) is issued to execute this service.

Process

Model

(SPARQL) PROtEUS SAL

Semantic Query

Lifted Response

Lowered Invoke Response

IoT-Service

Sensor

IoT-Service

Actuator

Knowledge Base

(Interface,

Capabilities, Context)

Cyber World

Physical World

The base system can be used in combination with the Feedback Service [ 3 ], which adds self-management (here: self-healing) in the form a cyber-physical feedback loop to PROtEUS (cf. Fig. 1). This service implements the MAPE-K loop for autonomous systems applied to the process execution [ 1 ]. This loop enables the linking of the execution of process activities to their physical efects: Monitoring

Ronny Seiger et al. gathers relevant sensor data from the environment and execution system; Analysis analyzes the data regarding the fulfillment of the process goals; Planning searches for compensations (here: alternative actuators or services) in case of unexpected errors; and Execution executes these compensations. All components use the Knowledge Base to store and retrieve relevant information (e. g., for finding replacement services). This loop is executed until a process goal is reached or cancelled if errors cannot be compensated. These Process Goals are specified on the activity, subprocess or process level. They contain the paths to relevant sensor data, a condition defining the successful execution, and a condition defining the need for entering the Planning phase due to an error [ 4 ]. 5 We present several real-world use cases of PROtEUS++ executing three example processes. 1) A process demonstrating the base system and the dynamic service selection via the SAL–a health monitoring process asking the user for its wellbeing in case of a detected emergency and calling an ambulance if the user is unresponsive. 2) A process demonstrating the base system interacting with the Feedback Service. A continuous process controls the light levels in a room to be within certain thresholds–selecting an alternative light source or notifying the user if the lights fail. 3) A process demonstrating the execution and feedback control of a distributed process on a service robot–repeating the subprocess on another robot if it fails during the driving to diferent locations. Along with these processes, we present the modelling tool [ 5 ] as well as a mobile control center app to interact with the workflows [ 6 ].

Acknowledgements This research has received funding under the grant number 100268299 by the European Social Fund (ESF) and the German Federal State of Saxony.

References

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2. Huber , S. , Seiger , R. , Schlegel , T. : Using semantic queries to enable dynamic service invocation for processes in the internet of things . In: ICSC . pp. 214 - 221 ( Feb 2016 )

3. Seiger , R. , Huber , S. , Heisig , P. , Assmann , U. : Enabling Self-adaptive Workflows for Cyber-physical Systems , pp. 3 - 17 . Springer International Publishing ( 2016 )

4. Seiger , R. , Huber , S. , Schlegel , T. : Proteus: An integrated system for process execution in cyber-physical systems . In: Enterprise, Business-Process and Information Systems Modeling, LNBIP , vol. 214 , pp. 265 - 280 ( 2015 )

5. Seiger , R., Keller, C., Niebling , F. , Schlegel , T. : Modelling complex and flexible processes for smart cyber-physical environments . JOCS 10 , 137 - 148 ( 2015 )

6. Seiger , R. , Lemme , D. , Struwe , S. , Schlegel , T. : An interactive mobile control center for cyber-physical systems . In: UbiComp Adjunct Proceedings . pp. 193 - 196 . UbiComp '16, ACM , New York, NY, USA ( 2016 )