=Paper=
{{Paper
|id=Vol-1415/CAISE2015DC07
|storemode=property
|title=Integrating the Internet of Things with Business Process Management: A Process-aware Framework for Smart Objects
|pdfUrl=https://ceur-ws.org/Vol-1415/CAISE2015DC07.pdf
|volume=Vol-1415
|dblpUrl=https://dblp.org/rec/conf/caise/Meroni13
}}
==Integrating the Internet of Things with Business Process Management: A Process-aware Framework for Smart Objects==
https://ceur-ws.org/Vol-1415/CAISE2015DC07.pdf
Integrating the Internet of Things with Business
Process Management: A Process-aware
Framework for Smart Objects
Giovanni Meroni1
Politecnico di Milano – Dipartimento di Elettronica, Informazione e Bioingegneria
Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy
[name].[surname]@polimi.it,
Supervisor: Pierluigi Plebani1
Abstract. Due to the achievements in the Internet of Things (IoT)
field, Smart Objects are often involved in business processes. However,
the integration of IoT with Business Process Management (BPM) is far
from mature: problems related to process compliance and Smart Objects
configuration with respect to the process requirements have not been
fully addressed yet; also, the interaction of Smart Objects with multiple
business processes that belong to different stakeholders is still under
investigation. My PhD thesis aims to fill this gap by extending the BPM
lifecycle, with particular focus on the design and analysis phase, in order
to explicitly support IoT and its requirements.
Keywords: Business Process Management System, Internet of Things,
Process compliance, Process monitoring, Smart Object, Business Process
Management, Multimodal Transportation, Smart Container
1 Introduction
During the last years, the growing interest for the Internet of Things (IoT) has
been manifested by both the academic and industrial world. The IoT is based
on the idea of Smart Objects, which are devices that decentralize computation
and data acquisition by moving them into the physical world. Because of their
diffusion, solutions for executing business processes relying on Smart Objects
are becoming more and more common.
However, as stated by Haller et al. [1], the integration of IoT with business
processes is far from trivial: data collected by sensors may be unavailable or have
inconsistent quality and, since part of the process execution is delegated to Smart
Objects and often involves multiple actors, it is difficult to assess the compliance
of a process. It is also worth noting that Smart Objects differ from traditional
services as they have reduced computational power and limited battery life.
In such a scenario, mechanisms for configuring Smart Objects according to the
process requirements and the capability of assessing the compliance of the control
2
and data flows with respect to the process definition would significantly ease
integration tasks.
According to Weske [2], the Business Process Management lifecycle can be
divided into four phases: (i) design and analysis, where business processes are
modeled according to real world requirements; (ii) configuration, where business
processes are implemented by a software solution; (iii) enactment, where business
processes are instantiated and their executions logged; (iv) evaluation, where
process logs are analyzed to assess the consistency between process models and
their execution.
During my PhD I aim to investigate the integration of the Internet of Things
with business processes by developing process-aware Smart Objects and by ex-
tending the Business Process Management lifecycle in order to explicitly support
Smart Objects.
The rest of this document is structured as follows. Section 2 outlines the main
research questions that I want to answer. Section 3 focuses on the multimodal
trasport domain to show the importance of the research questions for a significant
application domain. Section 4 proposes a solution that will support process-aware
Smart Objects. Section 5 analyzes the state of the art. Finally, Section 6 outlines
a tentative schedule for my research activities.
2 Research Questions
The adoption of the IoT can impact all the phases of the Business Process
Management lifecycle:
Design and analysis The process model will allow the user to define for each
business activity which data will be collected by Smart Objects, which condi-
tions will determine the start and end of the activities, and which constraints
on sensor data must be satisfied to consider activities successfully completed.
Configuration Smart Objects will be configured to collect data related to pro-
cess activities with the specified quality level, according to the process model
definition.
Execution Smart Objects will be process-aware by being able to identify and
log the execution order of business activities thank to their starting and
ending condition. They will also constantly check data constraints in order
to log whenever they are not satisfied.
Evaluation The process compliance will be assessed by analyzing the process
trace logged by Smart Objects to identify control and data flow violations.
Initially, I will focus on the design and analysis phase by enriching current
process modeling notations with constructs able to explicitly define Smart Ob-
jects, their roles, and their needs inside business processes. Subsequently, I will
also extend the other phases to support, take advantage of, and validate the
newly introduced process model notations.
To reach such achievements, I will investigate the following research ques-
tions:
3
RQ1 - How can we monitor the process execution? I aim to monitor the
process execution flow by determining which activities are running. I also
want to reach such achievement without relying on explicit start and termi-
nation messages addressed to specific activities, but instead inferring such
conditions by analyzing events captured by Smart Objects (i.e., when their
position is within a specific area).
RQ2 - How can we define requirements on activity data? I aim to sup-
port the definition of requirements on sensor data related to process activi-
ties. In this way, the business process will drive the configuration of sensors,
thus guaranteeing that sensor data needed for the correct execution of ac-
tivities will be available and with a quality matching the requirements. If
sensors are managed by external gateways (i.e., other embedded computing
devices), requirements could also affect the computation done at node level.
RQ3 - How can we identify process execution violations? I aim to iden-
tify process violations by both checking the correct execution order of process
activities and the compliance of activity-related data with constraints speci-
fied during the process design phase. I also want to do that directly on each
Smart Object.
RQ4 - How can we support multiple actors? I aim to support the concur-
rent execution of processes that are designed by multiple actors and could
partially or totally overlap during execution. Such a question is not trivial,
since different actors might have different process definitions, constraints,
and/or requirements on activities running at the same time. Therefore, I
will define process merge and conflict resolution mechanisms.
3 Case Study: Multimodal Transportation
My main case study, which I will use for the problem identification and motiva-
tion, refers to multimodal transportation, since most of the research questions
will directly address the currently unfulfilled needs of the stakeholders involved
in such a domain.
Multimodal transportation concerns the planning and enactment of trans-
portation of goods via multiple means of transport, each one typically belonging
to different shipping companies, for each single shipping. Moreover, goods often
belong to different manufacturers and/or are addressed to different customers.
Such a task is far from trivial, since each stakeholder needs to track the sta-
tus of the goods (i.e. position, conditions, etc.) during each shipping phase that
involves its participation.
To fulfill these needs, research efforts have been spent in putting some intel-
ligence into shipping containers, which are often used to aggregate goods during
multimodal shipping, turning them into Smart Containers, that is, Smart Ob-
jects. Such Smart Containers are usually equipped with sensor networks, a Sin-
gle Board Computing (SBC) device, and a communication device for exchanging
data with information systems.
However, such solutions are usually based on a static approach: the sensor
network configuration does not change during the transportation process, the
4
nature of shipped goods is not taken into account, and they are usually tailored
to a specific business process often involving a single stakeholder. In the real
world this is not the typical case. Several factors, such as the content of the
container, the capabilities of the sensor network, and the current phase of the
shipping process may determine a variation on the requirements on sensed data.
Moreover, as previously said, the nature of multimodal shipping involves the
active participation of multiple stakeholders. Each party has its own business
processes with different requirements on sensor data according to each specific
process activity. Therefore, the compliance of each shipping process with respect
to the data and control flows defined by stakeholders in their business processes
cannot be taken for granted, and its assessment is far from trivial.
4 Solution
As discussed in the previous section, with particular focus on the multimodal
transportation, current solutions based on Smart Objects lack the capability
of dynamically configuring sensors with the precision required. Each activity of
the business process must take into account the currently involved stakeholders.
Moreover, mechanisms able to assess process compliance have not been intro-
duced yet.
I envision a scenario in which Smart Objects are autonomous elements able to
communicate with external entities. These external entities are the stakeholders
that can: ask for the status of a Smart Object, and inform the Smart Object
about the process in which it is involved. In order to do so, Smart Objects must
be aware of the currently running process activities, and, for each activity, they
must know the requirements on sensor data that have to be fulfilled.
To support this scenario, a Smart Object have to be equipped with: (i) a
sensor network, (ii) a Single Board Computing (SBC) unit, and (iii) a com-
munication interface. The sensor network collects information concerning the
environment in which the Smart Object operates; the SBC executes a complete
software stack, and different applications are installed; finally, the communica-
tion interface allows the interaction with external systems.
Among the others, the SBC will run a lightweight Business Process Manage-
ment System (BPMS), a sensor configuration manager, a sensor data evaluator,
and sensor interface modules, as shown in Figure 1.
The BPMS is the core of the solution: it will be responsible for keeping
track of all processes belonging to each involved stakeholder, thus allowing them
to orchestrate the Smart Object. In order to do so, it will be able to figure
out which activities are currently running, to activate a proper configuration
of the monitoring system. However, as conditions that determine the execution
of activities rely on events that can be external, the BPMS will also deal with
process choreography. It is worth noting that in many application contexts, such
as multimodal transportation, some of the actual involved stakeholders and their
business processes are known only at run-time. For this reason, each time a new
5
Fig. 1. Software modules.
stakeholder is involved, its business process definitions have to be downloaded
and taken into account. Such a component will therefore answer RQ1 and RQ4.
The sensor configuration manager, on the other hand, will be responsible
for determining stakeholders’ requirements on sensor data. It will extract and
interpret requirements from the process definition provided by the BPMS, and
it will opportunely instruct the sensor interfaces to provide data that meet such
requirements. Such a component will answer RQ2.
Finally, the sensor data evaluator will be responsible for verifying the compli-
ance of sensor data to the constraints defined for the currently running activities,
and for reporting violations of such constraints. Such a component will answer
RQ3.
In order for these modules to automatically understand the process defini-
tions and their specifications on data, I propose to extend such business process
definitions with the following annotations on activities:
Start and termination conditions Such annotations will specify which con-
ditions on process data determine the beginning or the end of a specific
activity. This will allow the BPMS to implicitly infer the process trace (i.e.
the sequence of activities carried out during process execution), and therefore
to identify violations in the control flow.
Data requirements Such annotations will instruct the sensor configuration
manager to provide data with the specified quality requirements, thus en-
forcing process compliance with respect to the data flow.
Data constraints Such annotations will impose constraints on data by speci-
fying which conditions should or should not happen, thus allowing the sensor
data evaluator to detect violations related to process data.
6
5 Related Work
Some research efforts have been spent on integrating the Internet of Things with
business processes. Meyer et al. [3] propose to extend the BPMN 2.0 notation
to model smart devices as process components. This approach keeps the process
knowledge on the information system, and no process fragments are introduced
on smart devices.
Thoma et al. [4] propose to model the interaction with Smart Objects in
BPMN 2.0 as activity invocations for simple objects, or as message exchanges
with pools representing the whole Smart Object for more complex ones. This
way one can distribute parts of the process on Smart Objects. The limitation of
this work is the lack of details concerning how to deal with data uncertainty or
how to define data requirements.
Tranquillini et al. [5] propose a framework that employs BPMN for driving
the configuration of a Wireless Sensor Network (WSN). Since BPMN is used
only at design time for defining the business process, and then it is converted
into binary code executable by the WSN, introducing changes in the process def-
inition at runtime is difficult. Also, simultaneously supporting multiple processes
within the WSN is not feasible with this framework.
Schief et al. [6] propose a centralized framework that extends the process
design and execution phases of BPM by taking into account events generated by
Smart Objects. Furthermore, this framework provides data quality mechanisms
for evaluating events and sensor data. My proposal differs from this contribution
by distributing process knowledge, which will be directly embedded in Smart
Objects, and by explicitly defining requirements on sensor data, to better enforce
and validate process compliance with respect to both the process execution and
the data flows.
Concerning process compliance, such a topic has been widely studied dur-
ing the last decade. However, as stated by Kharbili et al. [7], very few process
compliance solutions exist that extend compliance checking beyond control flow.
They do not consider data flows and the timeliness of activity data, aspects that
are critical for the research questions. Awad et al. [8] try to address these open
issues by proposing an extension of the BPMN notation, named BPMN-Q, able
to define constraints also on the data exploited by business process activities.
Ly et al. [9] consider the usage of data flow constraints in their framework for
checking compliance during the whole business process lifecycle.
Some process compliance solutions determine the execution status of each
activity by means of explicit notifications by the activity itself. Other solutions
try instead to assess the execution status by analyzing the message flow between
the business process and the activities, often considering the execution of an
activity as a service invocation. Weidlich et al. [10], on the other hand, propose a
framework for detecting process execution violations that exploits complex event
processing techniques on process data to infer the execution order of process
activities.
These solutions address the research questions only partially, since no solution
covers all of them. In particular, the support for multiple actors is absent or very
7
limited: no solution support the definition of processes belonging to multiple
actors, the overlapping of different processes having activities in common and,
more importantly, concurrent and possibly conflicting constraints on the same
activity data defined by different actors.
Concerning the freight transportation domain, during recent years research
efforts have been put in developing Smart Container solutions ([11], [12], and
[13] just to name a few). However, all these solutions are based on the require-
ments and business processes of a single stakeholder, and are not thought to
promptly react to changes in the involved stakeholders and/or in their business
processes, requirements, and data. Such limitations are particularly important
for the multimodal transportation, since changes in the involved stakeholders
or in the business process definition are frequent and can happen during the
shipment enactment phase, thus requiring a proper reconfiguration of the Smart
Container.
6 Research Methodology
During the PhD, I plan to carry on design and research activities in parallel,
as suggested by Wieringa et al [14]. More in detail, the design activity will deal
with requirements analysis and definition of a possible solution. The research
activity, on the other hand, will deal with the review of the literature to be
aware of the state of the art in current technologies and use that as starting
point for my work. Research activity will also deal with the validation of the
results with respect to case studies to prove their soundness.
Concerning the research methods, for RQ1, RQ3 and RQ4 I plan to follow an
experimental research approach. In fact, to validate the solution answering such
research questions, I will build a prototype and test it possibly in the real world
or in a simulated environment. For RQ2, on the other hand, I plan to follow an
empirical research approach. Indeed, I will collect and analyze case studies to
better understand requirements on sensor data and, having done this, I will use
them as input to properly design a model that addresses all such requirements.
In order to achieve my goals, I plan to structure the research work around
the following phases:
1. I will concentrate on answering RQ1 and RQ3 first. The output of this phase
will be a process modeling notation that will allow one to model the start and
termination of activities, and conditions that violate their execution based
on events generated by activity data. I will also propose a methodology for
integrating Smart Objects with traditional business processes by generat-
ing IoT process models from traditional process definitions, and a tool for
modeling processes with the proposed notation.
2. I will then try to answer RQ2 by extending the notation defined in the pre-
vious phase, to support the definition of requirements on activity data. The
output of this phase will be an extension of the process modeling notation,
a BPMS capable of running processes modeled with such notation, and a
8
prototype of the sensor configuration manager module. The BPMS will also
be able to produce a process trace that will allow one to assess process
compliance with respect to both process and data flows.
3. I will finally try to answer RQ4 by investigating problems related to the
simultaneous execution of multiple business processes having conflicting re-
quirements. The output of this phase will be a prototype of the proposed
framework that will support multiple actors and will run on a SBC device.
Currently, the first phase of the research work has started, and I plan to
conclude it by the end of 2015. I then plan to start the second phase and conclude
it by the fourth quarter of 2016. Finally, I plan to start the third phase and
conclude the whole research work by the end of 2017 with the publication of my
PhD thesis.
Acknowledgments
This work has been partially funded by the Italian Project ITS Italy 2020 under
the Technological National Clusters program.
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