![]() ![]() Developing a medical-rehabilitation robot in the CORBYS (Cognitive Control Framework for Robotic Systems ) project and a smart-home gateway in the Arrowhead project () confirmed this textual language’s utility. Three independent compilers targeted microcontrollers, Posix C, and Java, respectively. The second iteration introduced a textual syntax for the state machines together with a first-class action language. The development (with the oil-and-gas industry) of a large sensor network was hindered by the difficulty of using a graphical specification (beyond toy examples), resource-constrained platforms' harsh coding requirements, and the importance of legacy and third-party components. We developed a minimalistic language to describe sensor-based architectures and support integration of theĬode generated from commercial state-machine tools with existing APIs. The first iteration exploited existing UML tools. Over the past six years, three major iterations have shaped the ThingML language and tools. The ThingML (Internet of Things Modeling Language) approach aims to transfer the promises of academic model-based software development to the industry. ![]() ![]() Over the past six years, this approach has been continuously evolved and applied to cases in different domains, including a commercial e-health application. The ThingML (Internet of Things Modeling Language) approach aims to facilitate collaboration between service developers and platform experts so that together they can produce value-added IoTbased services. So, IoT applications are meant to run on shared hardware, and developers should pay special attention to avoiding unwanted interactions between applications.Ĭonsequently, industrializing IoT applications proves challenging at all development phases and requires teams of developers with a broad range of competences from electronics and microcontrollers to cloud and data mining. Rather, they must combine things readily available in the environment: some generic, some application-specific, and some legacy things. Third, in the IoT vision, applications are no longer isolated, proprietary silos of devices and software 4. This way, developers can, for example, take advantage of IoT nodes' deep-sleep modes, which typically differ among platforms, to save power. In IoT applications, part of the heterogeneity isn't accidental and must remain explicit and exploitable by developers. Many tools and techniques hide this heterogeneity to enable developers to write applications that execute the same way on different platforms (OSs, web browsers, mobile devices, and so on) 3. Second, for IoT systems to deliver their full potential, developers must leverage the diverse resources and decentralized computing power that heterogeneous nodes provide. But this isn't the case with IoT software, which must scale in not only the number of nodes but also the number of types of nodes and platforms, from microcontrollers up to the cloud. This homogeneity keeps the software complexity manageable because it allows developing and deploying similar code on large sets of nodes. Most research in distributed systems, telecommunications, or even sensor networks has focused on scaling to very high numbers of nodes but has dealt with only a few types and roles among those nodes (for example, client-server, peer tracker, sensor gateway server, and so on) 2. However, not much research has addressed the combination of the two, which presents three specific challenges.įirst, in large IoT applications, distribution occurs across many heterogeneous nodes, which play different complementary roles to process data close to the sources and respond in a decentralized way. A rich collection of software engineering literature exists for each characteristic. The second is high heterogeneity of the processing nodes and the protocols used between them. The first is distribution over a large range of processing nodes. From a software engineering viewpoint, IoT applications have two main characteristics. The challenge of writing, deploying, and evolving software for the Internet of Things (IoT) is often underestimated 1. Adopt the right emerging trends at QCon San Francisco (Oct 2-6, 2023)! Get actionable advice for your engineering challenges. ![]()
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