On the Interplay Between Business Process Management and Internet-of-Things

Identification Hu et al. (2014) propose a dynamic integration mechanism of BPs to adapt to the dynamic characteristics of IoT, redirect and restructure the process logic using rules that can be edited at runtime and design a dynamic integration algorithm to implement this mechanism. Schief et al. (2011) propose a centralized framework that extends the process design and execution phases of BPM considering events generated by smart objects.

Modeling Based on the analysis, we have categorized the studies according to several key areas: Integration of IoT concepts in BPMN 2.0Gao et al. (2011) propose a BPMN 2.0 extension with sensor and smart device business functions to enhance the integration between the physical world and BPs. Meyer et al. (2011) extend the BPMN 2.0 to explicitly consider IoT basic concepts, enabling the modeling and development of IoT Services in terms of BPs. Meyer et al. (2013) integrate IoT devices as BP resources in the form of dedicated lanes and extend the BPMN 2.0 specification to include such IoT devices. Meyer et al. (2015) explore the representation of the IoT domain component “Thing” in a process model, introducing potential BPMN extension for IoT concepts. Verification and validation of IoT-BPs Graja et al. (2019) extend BPMN to sustain the various cyber-physical system concepts and a verification approach at design time to detect the mistakes in specifying the CPS process. Senderovich et al. (2016) derive events from sensor data, aligning them with process activities. Hornsteiner and Schönig (2023) extend BPMN to map security aspects and thus obtain security- and IIoT-aware BPs. Context management and IoT adaptation Schönig et al. (2018) propose a set of concepts for an IoT-enhanced process model re-engineering, addressing model changes and adaptation tasks. Suri et al. (2018) propose configuration concepts for handling IoT resource variability in the Configurable Process Model. IoT-aware BP decentralization and distribution Domingos et al. (2015) present an automatic approach to decentralize IoT-aware BPs defined using the BPMN 2.0. Jain and Tata (2017) have designed a distributed IoT application with annotated components and location information. IoT event integration and management Chadli et al. (2022) define the role of the IoT concept of smart business processes modeling SBPM, using an ad hoc approach with active help to detect data flow anomalies. Li et al. (2021) propose a business user-oriented BP modeling method supporting IoT event stream integration, with a framework that separates CEP and BP execution. Wang et al. (2022) monitor and model IoT sensing data in real-time, transforming it into IoT services for BP systems. Kirikkayis et al. (2023b) model IoT-driven events using BPMN 2.0 extension and DMN concepts. Kirikkayis et al. (2022b) introduce a BPMN 2.0 extension with IoT artifacts, enabling data acquisition and actuator control. Other aspects related to IoT and BPMZanfack et al. (2015) present a GSM⁴-IoT framework which aims to represent the physical workflows in logistics, extending them by adding physical workflow concepts and considering particularities of IoT smart entities and their mutual interactions. Mottola et al. (2019) propose a BPMN extension for wireless sensor network context modeling. Bocciarelli et al. (2017) model and manage resource management during runtime. Hasić et al. (2020) compare standard BPMN and BPMN + DMN for IoT process modeling. Grefen et al. (2019) introduce a time and space specification for IoT-aware collaborative BPs. Hou et al. (2016) suggest fragmenting IoT-aware BP for performance improvement. Grambow et al. (2021) propose context-awareness integration in IIoT BPs, visual AR support, and enhanced BPMN modeling.

Analysis Antonius and Dachyar (2020) model and simulate the cardiac remote patient monitoring process, identifying weaknesses needing improvement. Vitali and Pernici (2016) present an approach based on the temporal events analysis for building a graph of dependencies between the whole set of events, unveiling interconnections between processes of cooperating organizations. Seiger et al. (2023) develop an interactive method for analyzing low-level IoT data to detect higher-level process activity executions based on sensor-actuator-activity patterns. Diamantini et al. (2023) discuss the role of semantic models when integrating IIoT data streams into an Industry 4.0 context. They propose a process-aware knowledge graph that enriches sensor data using ontological descriptions of IIoT sensors, processes, and KPIs. Gómez-Valiente et al. (2023) present the DOMINIoT architecture to address scheduling, resource allocation, and state management challenges.

Redesign The redesign phase is often applied across other BPM life cycle stages. In Ruiz-Fernández et al. (2017), a redesign of a given clinical process is presented, while in Ismaili-Alaoui et al. (2018), interdisciplinary healthcare processes are redesigned to ensure direct and real-time interaction between the patient and the medical staff. Song et al. (2022) mainly address this phase by proposing an IoT-enabled Context-aware BPM (IoT-CaBPM) framework, which facilitates the evolution of BPs in combination with IoT advances.

Implementation Chen et al. (2012) propose to extend WS-BPEL⁵ with IoT characteristics for a new process definition language. Friedow et al. (2018) implement and write adapters using the Bosch IoT Things service to connect IoT devices and BPs via event influence. Kikuchi et al. (2018) develop a cloud-IoT orchestration framework with IoT device. Kunz et al. (2011) provide novel components and architectural concepts for improved IoT communication. These include a scalable discovery service and a decentralized control structure, cloud EPCIS,⁶ which facilitates access to RFID⁷ data, and middleware that enables flexible adaption of object-specific event processing rules. Marrella and Mecella (2017) automate process adaptation in cyber-physical domains during the occurence of exceptions and exogenous events. Mass et al. (2016) propose a system architecture enabling continuous, delay-tolerant BP execution on mobile nodes. Muhsin et al. (2016) present a mobile workflow management system for seamless and accurate data exchange between mobile devices and remote hubs. Seiger et al. (2018) propose an IoT workflow management system with dynamic service selection, complex event processing, human interactions and self-adaptation. Varga et al. (2018) propose a supporting system for service chaining and workflow execution using a Petri Net-based method, similar to a recent article that provide further insights into this area (Kozma et al. 2019). Wieland et al. (2008) define and implement two smart workflows and provide an architecture for transforming sensor data into business-level information. Zhu et al. (2014) design and implement a dynamic adaptation framework using an adaptive algorithm, allowing the system to infer the process when the environment changes. Di Martino et al. (2022) provide a methodology for the smart management of irrigation systems using ontologies, BPMN semantic annotation, and logical inference. Park et al. (2018) propose modeling and implementation methods for IoT execution design. Pastor et al. (2022) integrate IoT sensors for improved user comfort in shared spaces, emphasizing semantic representation and process mining (PM) techniques. Valderas et al. (2023) apply the SoC principle for the interdisciplinary development of IoT-enhanced BPs using BPMN, ontology-based technologies, and a microservices architecture.

Monitoring and Mining Seiger et al. (2020) propose a framework correlating IoT sensor streams with process events and activities using Complex Event Processing. Shamsuzzoha et al. (2014) discuss collaborative BP monitoring across organizations using IoT and cloud-based data repositories. Wombacher (2011) monitors workflow-state correlation with sensor data for online conformance checking. de Leoni and Pellattiero (2021) propose a technique to discover readable human-habit models for optimizing human experience with IoT systems. Janssen et al. (2020) present a technique that combines clustering and PM to discover activities and process models from motion sensors. Koschmider et al. (2020) present a framework to discover activities and process models from event location sensor data. van Eck et al. (2016) map sensor measurements to human activities, group them into process instances and convert them into an event log as input for any PM technique. Sora et al. (2017) transform raw movement measurements into actions and adapt sensor logs to apply BPM techniques. Bertrand et al. (2021) propose a model bridging the gap between IoT and PM using IoT ontologies and BP context models. Elali et al. (2022) enhance process models derived from PM with contextual information from sensor data. Elkodssi et al. (2022) present a PM approach for discovering and analyzing IoT-aware BPs and transforming sensor logs into structured event logs enriched with IoT concepts using a BPMN Ontology.

More phases A BPMN 2.0 IoT-enabled extension for modeling, executing and monitoring IoT-aware BPs is proposed by Gallik et al. (2022), while Kirikkayis et al. (2023a) also present a framework for IoT-driven business rules. Kirikkayis et al. (2023c) present a BPMN 2.0 extension for IoT, enabling real-time interaction with devices which aids decisions through collected data but lacks high-level aggregation consideration. Kirikkayis et al. (2022a) present a web-based framework integrating IoT data into BPs, enhancing real-time decision-making and monitoring.

Topic 1 – Event mining from sensors Knowledge extraction from sensor data has gained significant attention in the BPM community. Process mining techniques can greatly enhance BPM management when applied to analyze and transform raw data into usable information. The information provided by the sensors can be expressed through a functional business view, as demonstrated in Gao et al. (2011), representing the classification, storage, and distribution procedure of floral products within a flower company. This model in BPMN is connected to a functional model to import an ontology of the sensors and related instance data and then integrate the information modeled using the Semantic Sensor Network ontology with BPs. Similarly, various frameworks address the correlation between workflow states, sensor data, and events generated by IoT devices that initiate instances of BPs (Seiger et al. 2020; Wombacher 2011; Ismaili-Alaoui et al. 2018; Ruiz-Fernández et al. 2017; Ruppen and Meyer 2013; Chadli et al. 2022; Diamantini et al. 2023; Pastor et al. 2022). Koschmider et al. (2020) detect high-level events from raw event data and discover BPs from derived instances. Zhu et al. (2014) present a multi-agent framework that chooses the best step to move in the BP when an event caused by agents occurs. In addition, Sora et al. (2017) present an approach to discover process models from activities in the field of smart spaces where sensors must turn the sensor log into an event log consisting of human actions. Another interesting challenge when applying PM techniques in the context of sensor data is mapping sensor measurements to human activities and grouping activities into process instances (van Eck et al. 2016; de Leoni and Pellattiero 2021). The problem of mapping sensor data to event logs based on process knowledge is solved by Senderovich et al. (2016), which maps location-based events to activities by recognizing interactions between various agents and transforming historical sensor data registers into standardized event logs. Another contribution to translating sensor data into higher-level ones comes from Janssen et al. (2020). They discretize sensor data into activities using unsupervised learning through clustering, while Seiger et al. (2023) analyze sensor data to detect higher-level process activities.

Topic 2 – Physical resource management This topic tackles the specification and management of the resources associated with the BPs supporting Cyber-Physical Systems (CPSs). Graja et al. (2019) propose a verification framework to support the various CPS concepts and properties, which enables the designer to handle CPS process features. To integrate and utilize smart devices as BP resources to support the modeling, a service-oriented BPM system architecture was developed by Wehlitz et al. (2017). The resource management during runtime of a system to model a CPS-aware resource associated with a BP activity is proposed by Bocciarelli et al. (2017), while the DOMINIoT architecture is designed by Gómez-Valiente et al. (2023). In scenarios where physical resources are exchanged, knowing how a resource owned by a party is managed on anothers party’s premises is impossible. Thus possible misalignments can be detected too late. In Montali and Plebani (2017), the authors investigated an approach for compliance checking that mixes commitments and smart devices. Suri et al. (2017) developed a framework that describes IoT resources (e.g. extending the BP models with energy cost parameters to enable the energy-aware management of IoT resources in BPs).

Topic 3 – Context-aware execution A context-aware execution environment involves processes continuously observed and adapted according to the model specified when required by context changes. Contextual information enhances process execution, incorporating situation-aware insights for improved effectiveness. Such information is often gathered from sensors within intelligent environments, enabling pervasive execution known as intelligent workflows (Wieland et al. 2008). Frameworks that enhance process awareness and self-adaptation are proposed (Seiger et al. 2019; Pryss et al. 2015; Friedow et al. 2018; Malburg et al. 2020; Engels et al. 2018). To support context variables and sensors as well as communication paradigms for IoT, Domingos et al. (2014) extend the WS-BPEL workflow language, dynamically selecting IoT services based on availability, functionality, and context (Seiger et al. 2018; Elali et al. 2022). Other studies focused on the adaptability of BPs during the execution phase. A dynamic integration mechanism for coping with changes in BPs is proposed by Hu et al. (2014); Domingos et al. (2010); Tôn and Lê (2019). An application to the predictive process monitoring field using IoT events as a process context and developing a predictive model to predict the next activity is provided by Elhami et al. (2020). A new trend in running the IoT-aware BP is running on fog computing.⁸ In this context, Cheng et al. (2018) dealt with IoT-aware BPs at the execution level, where they introduced a new intermediate layer consisting of a set of distributed fog nodes to perform certain parts of the process.

Topic 4 – IoT-aware process modeling To create IoT-aware BPs and exploit the full potential of IoT and BPM, this must be integrated into process models. Modeling strategies for IoT-aware BPs fall into two categories (Brouns et al. 2018): those utilizing known modeling languages and those proposing new domain-specific languages. The former includes integrating BPMN with ontologies (Valderas et al. 2022; Di Martino et al. 2022; Valderas et al. 2023; Bertrand et al. 2021; Elkodssi et al. 2022) and extending BPMN with IoT devices (Chiu and Wang 2015; Kirikkayis et al. 2022b; Hornsteiner and Schönig 2023), as well as using Petri Nets (Varga et al. 2018), or DMN⁹ (Hasić et al. 2020; Kirikkayis et al. 2023b). In the latter, Chen et al. (2012) provide a new process definition language for IoT-enabled BPs, encapsulating physical devices as SOA services. IoT device interaction is critical. To this end, the application of mixed reality as a new interaction paradigm to facilitate the modeling and configuration of processes among IoT devices was elaborated on Seiger et al. (2021). From an access IoT data perspective, Cherrier and Deshpande (2017) present a gateway for transferring IoT events to BPs, managing device heterogeneity. Meyer et al. (2013) map IoT concepts to process models, and Martins and Domingos (2017) use BPMN to model IoT device behaviours. Other studies focus on collaborative structures to improve physical and digital collaboration between multiple actors to achieve a common goal (Maamar et al. 2020; Shamsuzzoha et al. 2014; Li et al. 2021; Wang et al. 2022). For example, Grefen et al. (2019) discuss synchronizing physical objects for successful IoT-aware digital processes.

Healthcare Most healthcare applications are related to remote patient monitoring systems, within hospitals or at home, and constantly enriched with IoT devices (Antonius and Dachyar 2020; Cheng et al. 2018; Jain and Tata 2017; Pryss et al. 2015; Seiger et al. 2018; Vitali and Pernici 2016; Ruiz-Fernández et al. 2017; Ruppen and Meyer 2013; Tôn and Lê 2019; Kirikkayis et al. 2022b; Gómez-Valiente et al. 2023). Maamar et al. (2018) use the example of a hospital that is on high alert being close to a severe car accident, where departments equipped with environmental sensors or smart wrists, enable the real-time transmission of patients’ vital signs to appropriate recipients, demonstrating how IoT can facilitate operations and improve efficiency. Senderovich et al. (2016) present an example of a process of treatment in a day hospital for cancer patients through Real-Time Locating System (RTLS) receivers that monitor all corporate entities involved in the process (e.g., patients, doctors, nurses) as well as some of the medical devices that record the data emitted and use it for real-time monitoring of process entities and equipment. Another frequent scenario is elderly care. Integrating the real-time event streams from bed pressure sensors into the BP field can reflect the health status of the elderly in real-time and thus monitor them through immediate warnings (Li et al. 2021).

Industrial sector In the domain of transportation, IoT might be used to realize monitoring of goods, such as dangerous or perishable goods (Domingos et al. 2010, 2014, 2015; Valderas et al. 2022; Suri et al. 2017; Valderas et al. 2023; Gallik et al. 2022; Seiger et al. 2023; Diamantini et al. 2023). These systems react to events that occur during transport using sensors and location technologies to improve the monitoring of transport conditions, such as temperature and pressure (Mass et al. 2016). Sensors and smart devices have great potential to provide further automation in various domains including logistics (Meroni et al. 2018; Gao et al. 2011; Grefen et al. 2019; Zanfack et al. 2015; Wombacher 2011; Schief et al. 2011; Song et al. 2022). Take, for example, the maritime transport scenario of heavy fog where disasters can be prevented in sea-land transport (Wang et al. 2022). Montali and Plebani (2017) use an example of a seafood company that organizes fish delivery and aims to verify if all actors operate correctly because of the complexity of the delivery process (deviations to the plan may occur). Several studies focus on smart factories as representatives of IoT environments (Grambow et al. 2021; Bocciarelli et al. 2017; Malburg et al. 2020; Wieland et al. 2008; Seiger et al. 2020; Kirikkayis et al. 2023c, a, b, 2022a). In the manufacturing industry, BPM and IoT support could improve both management by closely linking digital production and machine data (Schönig et al. 2018, 2020; Shamsuzzoha et al. 2014; Keates 2019) and cybersecurity by integrating security requirements into modeling using a ”security by design” paradigm (Hornsteiner and Schönig 2023).

Public Cheng et al. (2019) develop an actual sensor-aware BP application and validate it by using the designed system to protect a large area of forest in North China. Graja et al. (2019) evaluate their study through an example of disaster recovery systems. Maamar et al. (2020) present a city that runs many systems like transportation for traffic control and environment for air pollution monitoring; Marrella and Mecella (2017) rely on a case study previously conducted by the same authors in (Humayoun et al. 2009b, a; Marrella et al. 2011) to evaluate their study in an emergency management domain, in which teams of first responders act in disaster locations with the primary purpose of assisting potential victims and stabilizing the situation. Chadli et al. (2022) propose a classroom case study involving the deployment of hardware device and management software to measure the environmental parameters and control ventilation, lighting, and air conditioning. Pastor et al. (2022) employ a smart camera with active sensors for vehicle identification in road traffic.

Consumer Services Kahl et al. (2015) test their study for common BPs in the retail domain within the innovative retail lab of a large European supermarket chain; Meyer et al. (2011) evaluate their study using a use case from the domain of retail and show how sensors monitor perishable goods in a store; Meyer et al. (2013) test their study using a dynamic pricing process in the retail domain and show how the IoT device temperature sensor monitors the perishable good orchid in a store; Suri et al. (2018) evaluate their study using a process family from the Retail/Supply Chain Management domain; Ugljanin et al. (2018) test their study using a Smart City Tourism Organization (SCTO) scenario to automate its BPs related to visitor mobility by communicating with them using social networks and collecting their feedback.

Consumer Goods Friedow et al. (2018) use a simple coffee machine billing system to automate the process of counting the coffee amount for each user; Kunz et al. (2011) use the lifecycle of a fish fillet within the different domains of manufacturer, logistics service provider, wholesaler distribution hub, wholesaler store, and the customer; van Eck et al. (2016) use a case study performed at Philips where a smart baby bottle has been developed.

Services Scenarios that use smart device detection and implementation capabilities in smart home environments have been extensively addressed (Janssen et al. 2020; Loke et al. 2007; Wehlitz et al. 2017; Xing et al. 2012; Seiger et al. 2019; Hu et al. 2014; Chiu and Wang 2015; Elali et al. 2022). Examples of instances where service applications play a crucial role include the intruder detection system in a security scenario (Kikuchi et al. 2018); office supply processes (Cherrier and Deshpande 2017), temperature control processes (Seiger et al. 2021), warehouse management system monitoring processes (Hou et al. 2016), automatic irrigation control processes (Martins and Domingos 2017; Martins et al. 2020; Di Martino et al. 2022), processes for adaptive ventilation in a student dormitory (Mottola et al. 2019) or on student thesis defence (Zhu et al. 2014), processes for repair and maintenance of computers and peripherals (Muhsin et al. 2016); while Park et al. (2018) implement smart toilets for companion animals by simply attaching sensors and introducing BPM technology.

C1 – Placing sensors in a process-aware way The research challenge C1 is considered by 25% (i.e., 12) of the studies. The link between the position of the sensors and that of the corresponding actuator is discussed in Mottola et al. (2019). Using a physical simulation model, Malburg et al. (2020) conducted research in BPM and Industry 4.0, aiming to program a smart factory for BPs by selecting hardware components to detect process-relevant events. To place IoT sensors in a process-aware way and link them to the running process, a BPMS must be aware of the current values of IoT objects, and based on an established mapping from IoT variables to process models IoT data is sent to a BPMS (Schönig et al. 2020). Process model discovery from sensors or wearable devices depends on their location and amount (Koschmider et al. 2020; Senderovich et al. 2016; van Eck et al. 2016). Other interesting studies on how to integrate sensors with other services and into existing BPs have been conducted (Ruppen and Meyer 2013; Gallik et al. 2022; Song et al. 2022; Di Martino et al. 2022). Regarding sensors, the presented ontological model covers domain configuration, including IoT resources’ placement and gathered observations over time (Diamantini et al. 2023; Valderas et al. 2023).

C3 – Connection of analytical processes with IoT The research challenge C3 is considered by 8.3% (i.e. 4) of the studies. Elhami et al. (2020) use contextual events in the decision point to control the process execution and incorporate contextual changes with process rules and exaction logic at the runtime. With the proposed extension Kirikkayis et al. (2023b) can specify event-driven behaviour at various points in a BP but also demarcate IoT-based data sources from other process inputs for monitoring and error handling. BP execution necessitates updated information availability. Li et al. (2021) proposed a framework separating event processing and BP execution, reducing BPM engine load.

C4 – Integrating the IoT with process correctness checks The research challenge C4 is considered by 4.2% (i.e. 2) of the studies. By connecting individual tasks and workflow executions to the respective effects in the physical and virtual world with the help of additional sensor data, you can verify the correct execution and behaviour of the IoT entities involved (Seiger et al. 2018; Malburg et al. 2020).

C5 – Dealing with unstructured environments The research challenge C5 is considered by 6.3% (i.e. 3) of the studies. In the IoT world, which is much more ad hoc and situative, Domingos et al. (2010) introduce mechanisms to perform ad-hoc changes in IoT-aware BPs by identifying change primitives that support the change operations needed to modify parts of the BP.

C6 – Managing the link between micro-processes The research challenge C6 is considered by 6.3% (i.e. 3) of the studies. An example is to achieve an efficient and flexible production line by investigating the micro-processes at the individual machines and stations and their interconnections to achieve a more flexible composition of smaller processes (Malburg et al. 2020). By extending BPMN 2.0, Gao et al. (2011) links process models with business functions, connecting them to external open data, including sensor and instance ontology data; another extension suggest using low-level IoT data for decision-making, not including high-level data aggregation (Kirikkayis et al. 2023c).

C7 – Breaking down end-to-end processes The research challenge C7 is considered by 4.2% (i.e. 2) of the studies. Malburg et al. (2020) suggest relaxing and detailing the static and coarse-grained “hardwired” processes to achieve a more flexible composition of smaller processes. Kahl et al. (2015) propose a system that provides an agent-assisted realization and adaptation of BPs with semantic service selection and facilitates an event-driven selection and controlled execution of relevant BPs in intelligent environments.

C9 – Specifying the autonomy level of things The research challenge C9 is considered by 2.1% (i.e. 1) of the studies. Ensuring appropriate autonomy for resource constrained IoT device is investigated by Malburg et al. (2020).

C12 – Dealing with new situations The research challenge C12 is considered by 2.1% (i.e. 1) of the studies. Malburg et al. (2020) use past successful process executions to automatically learn possible adaptations of process instances to deal with new or similar situations.

C13 – Bridging the gap between event-based and process-based systems The research challenge C13 is considered by 25% (i.e. 12) of the studies. The complex IoT system is event-driven due to the large number of sensors and process-based (Malburg et al. 2020). While in the past, process models have been detected by documents or interviews with domain experts, today the challenge of automatically discovering the process model from the network of sensors or wearable devices also depends on their location and amount (Koschmider et al. 2020; Senderovich et al. 2016; van Eck et al. 2016). Therefore, considering the physical context and revealing the correlation between IoT data and process events is crucial (Bertrand et al. 2021).

C14 – Improving online conformance checking The research challenge C14 is considered by 6.3% (i.e. 3) of the studies. Seiger et al. (2020) mainly focus on generating event logs from streams of IoT sensor data with smart factories as an application domain.

C15 – Improving resource utilization optimization The research challenge C15 is considered by 10.4% (i.e. 5) of the studies. Suri et al. (2017) present a framework that formalizes IoT properties and rules to optimize resource management in BPs, defining resource constraints to be mapped to a task during the design phase.