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+file:: [ECMFA_23_paper_8566_1680594411934_0.pdf](../assets/ECMFA_23_paper_8566_1680594411934_0.pdf)
+file-path:: ../assets/ECMFA_23_paper_8566_1680594411934_0.pdf
+
+- design and management 
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+- ollect real-time data and control operation
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+- Additionally, 20 multi-layer IoT systems that leverage edge and fog computing 21 also deploy nodes or compute units (located in the plant) to run 22 lightweight applications and cloud servers for the deployment 23 and execution of the resource-intensive applications
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+- challenges related to the3 design of these block diagrams and the IoT system involved.
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+  hl-page:: 2
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+- unified language for modeling WWTP5 process block diagram
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+- management and adaptation of the system to9 address functional requirements and quality of service (QoS) at10 run-time.
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+- process block diagram of a WWTP 
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+- A code generator and MAPE-K based framework. 
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+- A 81 model-based infrastructure for the specification and runtime 82 execution of self-adaptive IoT architectures.
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+- design 43 time for the specification of the WWTP process block diagram
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+- self-adaptive IoT system, and run-time to support the 45 operation and adaptation of the system.
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+- multi-layer 54 IoT architectures
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+- WWTP processes and the IoT system 59 implied
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+- Both the DSL and the code 64 generator are implemented using MPS 1, a language workbench 65 developed by JetBrains to design DSLs.
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+- Prometheus2—a time-series database
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+- WTP process block diagram — We enable the specifica-26 tion of the main operating units such as filters, grit cham-27 bers, biological reactors, and the flow—either water or28 sludge—between these operating units.
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+- first poin
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+- adap- 37 tation rules
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+- igure 3 defines the concepts for16 the specification of functional and adaptation rules
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+- A Rule is composed of an Expression—condition relation-18 ship—and multiple Actions that are executed on the system if19 the condition is true during a defined perio
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+- Expression concept by adding sensor and QoS25 conditions that can be combined also with all other types of26 expressions—e.g., BinaryOperations, Literals, or BooleanCon-27 stants—in a complex conditional expression.
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+- QoSCondition allows detecting unusual values 40 in QoS and system infrastructure metrics. For example, high 41 RAM and CPU consumption in one specific node (edge, fog, or 42 cloud), or in a group of nodes belonging to a Region.
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+- Redeployment
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+- Offloading
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+- Scaling
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+- OperateActuator
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+- This new feature of the DSL is 60 relevant for the case of wastewater treatment, but could also be 61 useful in other scenarios or application cases.
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+- o define the types of conditions, metrics to monitor, and1 types of actions that cover our language, we relied on our sys-2 tematic literature review (SLR) (Alfonso et al. 2021b), which3 provides a comprehensive and holistic view of the current state4 of the art in IoT adaptation.
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+- The definition12 and implementation of these rules improves the accuracy of the13 DSL and avoids errors that could occur at run-time.
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+- well-formedness rules 
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+- projectional editors
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+  hl-color:: green
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+- The color of the shape represents the main fluid1 treated by the unit operation
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+  hl-page:: 6
+  hl-color:: green
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+- water treatmen
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+  hl-color:: green
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+- sludge treatmen
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+  hl-color:: green
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+- The modeling of rules—functional or adaptive—is per- 37 formed following a textual notation. A condition, a period, and 38 a list of actions must be defined for a rule.
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+- period of the rule 
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+- he actions of a rule are specified in a vertical list that 60 includes the type of action and all the parameters involved in 61 the action. 
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+- Other concepts for modeling the IoT system such as nodes, 77 regions, brokers, applications, and containers can be specified 78 using tabular, textual, and tree-view notations
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+- MAPE-K is a reference model to implement 83 adaptation mechanisms
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+- operate on a knowledge base
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+- Infrastructure and QoS — Infrastructure and QoS metrics9(such as bandwidth, CPU usage, and availability) are col-10 lected using kube-state-metrics4 and node-exporter5 (con-11 tainer cluster monitoring tools)
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+- Sensor data — The data captured by the system’s sensors, such5 as temperature, suspended solids, level, or pressure. These6 metrics are collected using a monitor subscribed to the7 topics where the sensors publish the data.
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+- Prometheus 
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+  hl-color:: green
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+- facilitate the tasks performed in the later stages
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+- analysis 
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+- planning
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+- notification received in the Plan stage, an adapta-32 tion plan is designed with the set of actions—e.g. scaling an33 application—
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+- JSON format via HTTP POST requests to37 the Adaptation Engine.
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+- K3S6, a Kubernetes-based orches-42 trator optimized for IoT and edge environments
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+- ontainer-based IoT applications specified in the input 54 model.
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+- monitoring tools 
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+- Adaptation Engine.
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+  hl-color:: purple
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+- DSL for IoT system modeling of a WWTP 70 extends the aspects designed by the language presented in
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+- process block diagram and conditions and actions of a rule10 that involves groups of sensors and actuators
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