=Paper=
{{Paper
|id=Vol-3673/paper7
|storemode=property
|title=Towards Robustness of IoT devices in BPMNE4IoT (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3673/paper7.pdf
|volume=Vol-3673
|authors=Pascal Schiessle,Yusuf Kirikkayis,Manfred Reichert
|dblpUrl=https://dblp.org/rec/conf/zeus/SchiessleKR24
}}
==Towards Robustness of IoT devices in BPMNE4IoT (short paper)==
https://ceur-ws.org/Vol-3673/paper7.pdf
Towards Robustness of IoT devices in BPMNE4IoT
Pascal Schiessle1 , Yusuf Kirikkayis1 and Manfred Reichert1
1
Institute of Databases and Information Systems, Ulm University, 89081, Germany
Abstract
Integrating the Internet of Things into Business Process Management has gained traction for several
years to improve smart applications (e.g., smart homes, Industry 4.0). Different frameworks have been
proposed to integrate IoT in all stages of the BPM lifecycle. However, current frameworks lack proper
support for dealing with issues related to error handling, like sensor faults, fallback strategies, and
redundancies. Therefore, it has to be assessed to what extent error handling should be included in
the abstraction layer of IoT-aware processes to increase both robustness and reliability. This paper
discusses five research questions on the challenges regarding the integration of error handling in existing
frameworks throughout the BPM lifecycle.
Keywords
BPM, IoT, Error handling, IoT-aware business processes
1. Introduction
The Internet of Things (IoT) is an important part of digitization in the personal space, like
smart homes or connected cars, as well as in the industrial space with Industry 4.0 and smart
logistics. These complex systems combine a wide range of IoT devices to form highly automated
systems, relying on sensors for collecting data from the physical world (e.g., temperature, air
quality, humidity) as well as actuators (e.g., motors), for altering the physical world by either
mechanical motion (e.g., movement or rotation) or environmental changes (e.g., light, humidity,
pressure) [1, 2]. Utilizing data generated from IoT devices can improve IoT-aware business
processes (BP) [3, 4]. Integrating IoT devices into Business Process Management (BPM) systems
shows the potential of merging both areas [5, 6]. BPM is a paradigm to model, implement,
execute, monitor, and analyze business processes [7]. Providing support for IoT capabilities in
BPM systems can enhance the awareness of the physical world by connecting the digital to
the physical world via IoT devices along the entire BPM life-cycle. Besides optimized business
processes and better decision-making, the added IoT capabilities can be used to automate physical
tasks, like turning on/off lights or tracking the location of physical devices [6]. Modeling IoT-
aware business processes has been extensively studied in literature [2]. In this paper, we consider
five research questions to assess the extent of IoT-related errors in an IoT-aware business process.
Besides understanding common IoT errors, the goal is to investigate different strategies on
how to include error handling into IoT-aware business processes to provide a set of recovery
16th Central European Workshop on Services and their Composition, 2024, Ulm, Germany
Envelope-Open pascal.schiessle@uni-ulm.de (P. Schiessle); yusuf.kirikkayis@uni-ulm.de (Y. Kirikkayis);
manfred.reichert@uni-ulm.de (M. Reichert)
Orcid 0000-0002-0793-4178 (P. Schiessle); 0000-0001-6536-0785 (Y. Kirikkayis); 0000-0003-2536-4153 (M. Reichert)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
S. Böhm and D. Lübke (Eds.): 16th ZEUS Workshop, ZEUS 2024, Ulm, Germany, 29
February–1 March 2024, published at http://ceur-ws.org
CEUR
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Workshop ISSN 1613-0073
Proceedings
Schiessle et al.: Towards Robustness of IoT devices in BPMNE4IoT
strategies, and to study the effects these strategies have on the business process. We developed a
holistic framework - BPMNE4IoT - for modeling, executing, monitoring, and logging IoT-aware
processes [5]. The framework can already detect faulty sensors and actuators and visualize
them in the BPMNE4IoT monitoring tool. However, the IoT-aware processes that can be built
with our framework have not yet matched the needed resilience regarding IoT errors. More
research is needed to not only detect, but also to cope with a wide range of errors, like failing
sensors, sensor drifts, and stuck actuators. Considering the research questions presented in this
paper, we plan to investigate possible improvements to the BPMNE4IoT framework to increase
its resilience.
The paper is structured as follows. Section 2 outlines the overall problem statement and
provides a real-world example. In Section 3, we define a set of research questions, that need
to be answered in the context of the above problem statement. In 4, we present related work.
Finally, Section 5, presents a summary and an outlook.
2. Problem Statement
Error handling is a multi-phase problem. Besides modeling, systems should be able to cope
with errors during IoT-aware process execution. Here, we focus on approaches based on the
combination of BPMN with IoT, however, other modeling approaches (e.g., Petri-nets or state
machines) do exist. BPMN 2.0 does not provide the tools and notation to design IoT-aware
processes [3, 5]. By introducing a holistic framework, i.e. BPMNE4IoT for modeling, executing,
monitoring, and logging IoT-aware processes, the involved IoT devices can be incorporated with
the business process. However, the framework contains a limited set of features to increase the
robustness and reliability of IoT-related errors. We want to illustrate this based on the BPMN
model, depicted in Figure 1.
Figure 1: An example of an IoT-aware business process
In this scenario, different IoT-related errors may occur. To illustrate these errors, we focus on
two scenarios—a faulty robot, which is an IoT object, comprising different types of sensors and
actuators, and a faulty light barrier. For example, the robot might malfunction and refuse to
start in step 3 of the IoT-aware process depicted in Figure 1. Currently, the process will be stuck
forever in this step until a worker intervenes and manually handles the malfunction, alerted by
the monitoring. Depending on the specific robot, calibrating or restarting the robot may fix the
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Schiessle et al.: Towards Robustness of IoT devices in BPMNE4IoT
error, however, this is often not supported by BPM systems, like BPMNE4IoT, which we use as
an example. Here, an IoT device could include strategies to handle common errors automatically
and report their state (e.g., re-calibration, restarting). Depending on the domain, this might
decrease the amount of human intervention and the time consumed to treat the malfunction.
Note that there might also be life-threatening errors. In a second scenario, we consider the
effects of a malfunctioning light barrier. When the robot is working on a piece and is standing
still, waiting for a workpiece to cool down, the worker might believe the robot has finished its
task and walk into the security zone. If the light barrier does not trigger, the robot is not aware
of the human in range which could harm him significantly.
From these two scenarios we may conclude, robustness and reliability concerns need to be
investigated for IoT-aware processes. Moreover, process execution opportunities to increase
safety, reduce downtime, and increase availability.
3. Research Questions
Although BMPNE4IoT [5] offers a promising approach to model IoT devices in IoT-aware
business processes, further research is needed. Currently, faulty sensors can be represented in
the execution stage of the BPMN4IoT pipeline. However, BPMNE4IoT has not yet considered
robustness and reliability issues regarding IoT devices. We plan to investigate if including IoT
devices down to the hardware is the appropriate abstraction level for IoT-related error handling.
Furthermore, we need to assess in future work, if changes to the handling of IoT devices in
BPMNE4IoT are necessary to include and aggregate reliability information. The following
research questions (RQs) must be explored to gain a deeper understanding.
RQ1: How can the appropriate abstraction level for a given IoT device be determined
when integrating into IoT-aware BPs?
In BPMNE4IoT, devices can be represented either as a single device or as a group of devices
or as a complex object with multiple types of devices. Defining error handling techniques is
influenced by the access to the device. Low-level access to IoT devices in IoT-aware BPs enables
more complex error handling by the process, however, this is not always desired [8]. Developing
metrics to determine the appropriate abstraction level on a process level should be pursued.
RQ2: Can monitoring on an IoT device level contribute to identifying error-prone
IoT devices across processes?
Monitoring IoT devices on a process level may provide domain experts with more detailed
insights into which situations specific IoT devices produce errors. Currently, IoT devices in
BPMNE4IoT are represented multiple times in the modeling stage (10 and 12 in 1), even though
they are the same physical device. Connecting data from different IoT-aware BPs by a shared
device pool may provide a better understanding of the reliability of specific IoT devices.
RQ3: Is the added complexity of IoT-related error handling over native BPMN 2.0
error handling techniques justifiable to enhance expressiveness?
Including IoT-related error handling in IoT-aware BPs could yield the benefit of a more
descriptive modeling stage. Based on RQ1, implementation-specific logic can be pushed to
the execution or device level by choosing a higher abstraction degree for the IoT device in the
modeling stage.
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Schiessle et al.: Towards Robustness of IoT devices in BPMNE4IoT
RQ4: Which patterns for dealing with self-healing and recovery can be realized in a
framework like BPMNE4IoT?
Currently, failing IoT devices lead to a stuck or terminated BP. Patterns to include self-healing
capabilities, providing the ability to detect and compensate for IoT-related errors, are desired
in BPMNE4IoT. It should be investigated which techniques (e.g., MAPE-K feedback loops) for
dealing with self-healing can be incorporated into BPMNE4IoT.
RQ5: How can IoT device redundancies be integrated into IoT-aware BPs?
Especially in critical applications, redundant IoT devices are used to ensure the reliability
of the system, however, they come at a price (e.g., device cost, and complexity). It should
be investigated if IoT device redundancies should be incorporated on a BP level. Defining
redundancies, receiving detailed insights while monitoring, and refining processes based on
redundancy data linked to specific tasks could lead to an easier optimization of processes.
However, it is open for debate which of these abilities should be controllable on a process level,
especially due to redundancies being highly dependent on the context, software, and hardware.
4. Related Work
Several approaches have been proposed to include IoT-awareness into the BPM lifecycle [5, 9,
10, 2, 11, 12, 13]. uBPMN extends BPMN 2.0 by defining five task types, sensor task, reader task,
image task, audio task, and collector task, to represent incoming and outgoing data streams as
well as their respective context [14]. BPMNE4WSN specifically aligns with Wireless Sensor
Networks (WSNs) [15] by introducing WSN task, WSN pool, and WSN performance annotations.
BPMNE4CPS extends the BPMN 2.0 service task to provide dedicated support for web services,
embedded services, and cloud services [16]. While these approaches enable IoT-aware processes,
they do not specifically focus on robustness or reliability issues. Outside the BPM context,
work was conducted to investigate IoT robustness and reliability [17, 18, 19, 20]. [21] provides
a methodology for measuring task reliability by block reduction. For IoT-aware processes, [22]
and [23] investigated the reliability of the BPMN extension relyBPMN when associating human
and IoT-related information resource information for given tasks. [24] and [25] investigated
the use of self-adaptive processes in distributed workflows. By using a modified MAPE-K
feedback loop, known from self-driving frameworks to detect and compensate for errors in
distributed processes. Finally, [26] proposes an approach to fault management for distributed
sensor networks on the edge.
5. Conclusion
As discussed, robust and reliable IoT-aware processes should be pursued. As shown in two
scenarios, handling IoT-related errors not only increases availability and decreases cost, but also
increases safety by managing safety-related topics (e.g., redundancy). The paper introduced five
research questions to assess and improve the extent of error handling of IoT-aware business
processes in BPMNE4IoT. Existing approaches, like relyBPMN, do improve error handling in
IoT-aware BPs for specific scenarios, however, a holistic approach is still missing.
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Schiessle et al.: Towards Robustness of IoT devices in BPMNE4IoT
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