As the vision of the Internet of Things (IoT) becomes a reality, thousands of devices will beconnected to IoT platforms in smart cities and regions. These devices will actively send dataupdates to cloud-based platforms, as part of smart applications in domains like healthcare, trafficand pollution monitoring. Therefore, it is important to study the ability of modern IoT systemsto handle high rates of data updates coming from devices. In this work we evaluated the per-formance of components of the Internet of Things Services Enablement Architecture of theEuropean initiative FIWARE. We developed a testbed that is able to inject data updates usingMQTT and the CoAP-based Lightweight M2M protocols, simulating large scale IoT deploy-ments. Our extensive tests considered the vertical and horizontal scalability of the componentsof the platform. Our results found the limits of the components when handling the load, and thescaling strategies that should be targeted by implementers. We found that vertical scaling is notan effective strategy in comparison to the gains achieved by horizontally scaling the databaselayer. We reflect about the load testing methodology for IoT systems, the scalability needs ofdifferent layers and conclude with future challenges in this topic.

The rise of IoT-based system is making an impact on our daily lives and environment. Fog Computing is a paradigm to utilize IoT data and process them at the first hop of access network instead of distant clouds, and it is going to bring promising applications for us. A mature framework for fog computing still lacks until today. In this study, we propose an approach for monitoring fog nodes in a distributed system using the FogFlow framework. We extend the functionality of FogFlow by adding the monitoring capability of Docker containers using cAdvisor. We use Prometheus for collecting distributed data and aggregate them. The monitoring data of the entire distributed system of fog nodes is accessed via an API from Prometheus. Furthermore, the monitoring data is used to perform the ranking of fog nodes to choose the place to place the serverless functions (Fog Function). The ranking mechanism uses Analytical Hierarchy Processes (AHP) to place the fog function according to resource utilization and saturation of fog nodes’ hardware. Finally, an experiment test-bed is set up with an image-processing application to detect faces. The effect of our ranking approach on the Quality of Service is measured and compared to the current FogFlow.

In the era of Industry 4.0, the Internet-of-Things (IoT) plays the driving role comparable to steam power in the first industrial revolution. IoT provides the potential to combine machine-to-machine (M2M) interaction and real time data collection within the field of manufacturing. Therefore, the adoption of IoT in industry enhances dynamic optimization, control and data-driven decision making. However, the domain suffers due to interoperability issues, with massive numbers of IoT devices connecting to the internet despite the absence of communication standards upon. Heterogeneity is pervasive in IoT ranging from the low levels (device connectivity, network connectivity, communication protocols) to high levels (services, applications, and platforms). The project investigates the current state of industrial IoT (IIoT) ecosystem, to draw a comprehensive understanding on interoperability challenges and current solutions in supporting of IoT-based smart manufacturing. Based upon a literature review, IIoT interoperability issues were classified into four levels: technical, syntactical, semantic, and organizational level interoperability. Regarding each level of interoperability, the current solutions that addressing interoperability were grouped and analyzed. Nine reference architectures were compared in the context of supporting industrial interoperability. Based on the analysis, interoperability research trends and challenges were identified. FIWARE Generic Enablers (FIWARE GEs) were identified as a possible solution in supporting interoperability for manufacturing applications. FIWARE GEs were evaluated with a scenario-based Method for Evaluating Middleware Architectures (MEMS).  Nine key scenarios were identified in order to evaluate the interoperability attribute of FIWARE GEs. A smart manufacturing use case was prototyped and a test bed adopting FIWARE Orion Context Broker as its main component was designed. The evaluation shows that FIWARE GEs meet eight out of nine key scenarios’ requirements. These results show that FIWARE GEs have the ability to enhance industrial IoT interoperability for a smart manufacturing use case. The overall performance of FIWARE GEs was also evaluated from the perspectives of CPU usage, network traffic, and request execution time. Different request loads were simulated and tested in our testbed. The results show an acceptable performance in terms with a maximum CPU usage (on a Macbook Pro (2018) with a 2.3 GHz Intel Core i5 processor) of less than 25% with a load of 1000 devices, and an average execution time of less than 5 seconds for 500 devices to publish their measurements under the prototyped implementation. / I en tid präglad av Industry 4.0, Internet-of-things (IoT) spelar drivande roll jämförbar med ångkraft i den första industriella revolutionen. IoT ger potentialen att kombinera maskin-till-maskin (M2M) -interaktion och realtidsdatainsamling inom tillverkningsområdet. Därför förbättrar antagandet av IoT i branschen dynamisk optimering, kontroll och datadriven beslutsfattande. Domänen lider dock på grund av interoperabilitetsproblem, med enorma antal IoT-enheter som ansluter till internet trots avsaknaden av kommunikationsstandarder på. Heterogenitet är genomgripande i IoT som sträcker sig från de låga nivåerna (enhetskonnektivitet, nätverksanslutning, kommunikationsprotokoll) till höga nivåer (tjänster, applikationer och plattformar). Projektet undersöker det nuvarande tillståndet för det industriella IoT (IIoT) ekosystemet, för att få en omfattande förståelse för interoperabilitetsutmaningar och aktuella lösningar för att stödja IoT-baserad smart tillverkning. Baserat på en litteraturöversikt klassificerades IIoT-interoperabilitetsfrågor i fyra nivåer: teknisk, syntaktisk, semantisk och organisatorisk nivå interoperabilitet. När det gäller varje nivå av driftskompatibilitet grupperades och analyserades de nuvarande lösningarna för adressering av interoperabilitet. Nio referensarkitekturer jämfördes i samband med att stödja industriell driftskompatibilitet. Baserat på analysen identifierades interoperabilitetstrender och utmaningar. FIWARE Generic Enablers (FIWARE GEs) identifierades som en möjlig lösning för att stödja interoperabilitet för tillverkningstillämpningar. FIWARE GEs utvärderades med en scenariebaserad metod för utvärdering av Middleware Architectures (MEMS). Nio nyckelscenarier identifierades för att utvärdera interoperabilitetsattributet för FIWARE GEs. Ett smart tillverkningsfodral tillverkades med prototyper och en testbädd som antog FIWARE Orion Context Broker som huvudkomponent designades. Utvärderingen visar att FIWARE GE uppfyller åtta av nio krav på nyckelscenarier. Dessa resultat visar att FIWARE GE har förmågan att förbättra industriell IoT-interoperabilitet för ett smart tillverkningsfodral. FIWARE GEs totala prestanda utvärderades också utifrån perspektivet för CPU-användning, nätverkstrafik och begär exekveringstid. Olika förfrågningsbelastningar simulerades och testades i vår testbädd. Resultaten visar en acceptabel prestanda i termer av en maximal CPU-användning (på en Macbook Pro (2018) med en 2,3 GHz Intel Core i5-processor) på mindre än 25% med en belastning på 1000 enheter och en genomsnittlig körningstid på mindre än 5 sekunder för 500 enheter att publicera sina mätningar under den prototyperna implementateringen.