The application of Business Process Management (BPM) technologies to automate processes in Cyber-physical Systems (CPS) promises various advantages including a higher exibility and simplied programming, a more e cient resource usage, and an easier integration of devices. However, these new areas also introduce novel challenges for Work ow Management Systems (WfMSes) especially related to the physical world interactions, constraint resources, complex devices and unpredictable actors that make up CPS. WfMSes used in classical BPM domains are able to address these new challenges only partially. We propose a new modelling language and WfMS designed to extend classical processes into CPS Work ows. We put a special focus on the processbased interactions with the physical world via sensors, actuators, things and humans. An autonomic manager based on the MAPE-K control loop adds self-management to WfMSes to deal with unanticipated situations and cyber-physical state inconsistencies. A smart home case study shows the feasibility and high coverage of new CPS-related requirements.
processes involving physical and virtual participants. The key actors in these
CPS work ows are physical actuators, sensors, smart objects and humans, and
virtual software components that interact with each other. CPS introduce the
new dimension of interaction with the physical world and new properties a
WfMS for CPS has to cope with: cyber-physical interactions; hierarchical devices
of varying complexity and availability; limited physical resources; new
physical error sources; and context-dependant and safety-critical behaviour{and with
these the increased need for autonomy. Based on these new properties we
investigate research questions along the BPM lifecycle [
Following a discussion of existing BPM systems and related research, we propose new concepts to address novel requirements and bridge identi ed research gaps. First, we introduce a CPS work ow notation for modelling the interactions among all CPS entities as well as success and error criteria for the execution on the process-level. We then present an architecture of a WfMS called \PROtEUS" to execute the processes. Subsequently, the integration of a feedback loop to increase exibility and resilience of the process execution is shown{also as a general framework for adding self-* capabilities to WfMSes. The evaluation of our concepts is conducted in a smart home case study and complemented by a discussion of the requirements coverage, advantages and limitations. 2
Solution: The PROtEUS Work ow Manament System
The PROtEUS system [
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
Feedback Service
Monitor PGroocaelsss Analyser
Planner
Executor Result
Knowledge
Base
{ Process Execution Engine to execute process instances according to their
underlying process models and communicate with all other components.
{ Process Manager to control and monitor the execution of processes.
{ CEP Engine to enable complex event processing (CEP) within sensor event
streams for detecting explicitly de ned event patterns.
{ Local Service Platform to enable the local deployment and discovery of
web services based on OSGi and REST.
{ Service Invoker to call various types of web services invoking CPS device
functionality based on standard or proprietary protocols.
{ Human Task Handler to distribute manual tasks that are part of a process
requiring human interactions on an end-user device (e. g., a smartphone).
{ Distribution Manger to enable the distributed execution of subprocesses
on remote peers in a hierarchical network managed by super-peers.
{ Web Socket Server to enable the bi-directional communication and
interaction with users and other instances of the WfMS via Pub/Sub and RPC.
PROtEUS interacts with the Semantic Access Layer (SAL) to dynamically
discover and invoke IoT services based on required functionality and context
constraints of mobile and resource-constraint IoT devices at runtime [
Fig. 2. Synchronization to Maintain Cyber-physical Consistency during Execution. { Knowledge: The Knowledge Base is a central database (ontology) storing all information regarding the CPS entities and process execution. { Monitor: Monitoring agents update sensor data in the Knowledge Base continuously and forward relevant changes (Symptoms) to the Analyser. { Analyse: The Analyser uses the criteria de ned in the objectives to check the execution for success{terminating the MAPE-K loop if positive{or error{ sending a Change Request to the Planner if positive. { Plan: The Planner uses the determined mismatch and an extensible Compensation Repository to nd Compensation Actions and replacement resources based on their capabilities and context. { Execute: The change plan is enacted by the Executor instructing the respective CPS actuators and e ectors. The MAPE-K loop is repeated. With the Feedback Service implemented as a standalone micro-service, we can use the MAPE-K loop as framework to retro t existing WfMSes. We propose two ways of retro tting: 1) Invasive{requiring extensions to the process metamodels to specify goals and objectives, and modifying the execution logic to call the Feedback Service in parallel to the managed process step; and 2) Noninvasive{requiring the existing process models to be extended with an explicit parallel call to the Feedback Service having the goal as input parameter. 3
Evaluation and Contributions
To evaluate the new concepts and prototypes we conducted an extensive
proof-ofconcept case study in a smart home{modelling and executing two main scenario
processes that assist residents with their morning routines and in emergency
cases, and special processes based on these examples. In general, we link the
execution of the individual process steps to the corresponding e ects in the physical
world via external sensor data{and vice versa. In case the Feedback Service is
involved, we also link the executions of the MAPE loops with the process
execution and physical e ects. The PROtEUS system shows expected behaviour
and fast execution times for virtual computations and synchronous invocations
of services. The major contributors to the overall execution times are activities
involving (asynchronous) actions and changes in the physical world, which are
much slower by nature. A discussion of the coverage of new CPS-related
requirements shows advances of the PROtEUS system compared to related approaches{
including the interaction with sensors, dynamic actuators, humans and other
WfMSes as well as an increased level of runtime adaptivity. With this thesis, we
present a comprehensive approach of introducing work ows to CPS and IoT [