Building IoT Systems Using Distributed First-Class Reactive Programming

Troyer, Christophe De; Nicolay, Jens; Meuter, Wolfgang De

doi:10.1109/cloudcom2018.2018.00045

Cited by 10 publications

(10 citation statements)

References 14 publications

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“…There is intense interest in developing tierless, or multitier, language technologies with a number of research languages developed over the last ifteen years, e.g. [15,19,66,79]. These languages demonstrate the advantages of the paradigm, including less development efort, better maintainability, and sound semantics of distributed execution.…”

Section: Tierless Languagesmentioning

confidence: 99%

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Could Tierless Languages Reduce IoT Development Grief?

Lubbers

Koopman

Ramsingh

et al. 2023

ACM Trans. Internet Things

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Internet of Things (IoT) software is notoriously complex, conventionally comprising multiple tiers. Traditionally an IoT developer must use multiple programming languages and ensure that the components interoperate correctly. A novel alternative is to use a single tierless language with a compiler that generates the code for each component and ensures their correct interoperation. We report a systematic comparative evaluation of two tierless language technologies for IoT stacks: one for resource-rich sensor nodes (Clean with iTask), and one for resource-constrained sensor nodes (Clean with iTask and mTask). The evaluation is based on four implementations of a typical smart campus application: two tierless and two Python-based tiered. (1) We show that tierless languages have the potential to significantly reduce the development effort for IoT systems, requiring 70% less code than the tiered implementations. Careful analysis attributes this code reduction to reduced interoperation (e.g. two embedded domain-specific languages (DSLs) and one paradigm versus seven languages and two paradigms), automatically generated distributed communication, and powerful IoT programming abstractions. (2) We show that tierless languages have the potential to significantly improve the reliability of IoT systems, describing how Clean iTask/mTask maintains type safety, provides higher order failure management, and simplifies maintainability. (3) We report the first comparison of a tierless IoT codebase for resource-rich sensor nodes with one for resource-constrained sensor nodes. The comparison shows that they have similar code size (within 7%), and functional structure. (4) We present the first comparison of two tierless IoT languages, one for resource-rich sensor nodes, and the other for resource-constrained sensor nodes.

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Section: Tierless Languagesmentioning

confidence: 99%

“…For example, Links compiles to HTML and JavaScript for the web client and to SQL on the server to interact with the database system. Tierless languages for IoT stacks are more recent and less common, examples include Potato [79] and Clean with iTask/mTask [43].…”

Section: Introductionmentioning

confidence: 99%

Could Tierless Languages Reduce IoT Development Grief?

Lubbers

Koopman

Ramsingh

et al. 2023

ACM Trans. Internet Things

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“…Potato [42] is a stream-based reactive programming library in Elixir for programming Internet of Things devices, which supports intensional acquaintance discovery. The library provides a global stream of acquaintance "join" and "leave" events, which the programmer can use to manually maintain their own collections of acquaintances.…”

Section: Acquaintance Discoverymentioning

confidence: 99%

Topology-Level Reactivity in Distributed Reactive Programs

2022

Self Cite

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Reactive programming is a popular paradigm to program event-driven applications, and it is often proposed as a paradigm to write distributed applications. One such type of application is prosumer applications, which are distributed applications that both produce and consume many events.We analyse the problems that occur when using a reactive programming language or framework to implement prosumer applications. We find that the assumption of an open network, which means prosumers of various types spontaneously join and leave the network, can cause a lot of code complexity or run-time inefficiency. At the basis of these issues lies acquaintance management: the ability to discover prosumers as they join and leave the network, and correctly maintaining this state throughout the reactive program. Most existing reactive programming languages and frameworks have limited support for managing acquaintances, resulting in accidental complexity of the code or inefficient computations.In this paper we present acquaintance management for reactive programs. First, we design an acquaintance discovery mechanism to create a flock that automatically discovers prosumers on the network. An important aspect of flocks is their integration with reactive programs, such that a reactive program can correctly and efficiently maintain its state. To this end we design an acquaintance maintenance mechanism: a new type of operator for functional reactive programming languages that we call "deploy-*". The deploy-* operator enables correct and efficient reactions to time-varying collections of discovered prosumers.The proposed mechanisms are implemented in a reactive programming language called Stella, which serves as a linguistic vehicle to demonstrate the ideas of our approach. Our implementation of acquaintance management results in computationally efficient and idiomatic reactive code.We evaluate our approach quantitatively via benchmarks that show that our implementation is efficient: computations will efficiently update whenever a new prosumer is discovered, or a connected prosumer is dropped. To evaluate the distributed capabilities of our prototype implementation, we implement a use-case that simulates the bike-sharing infrastructure of Brussels, and we run it on a Raspberry Pi cluster computer.We consider our work to be an important step to use functional reactive programming to build distributed systems for open networks, in other words, distributed reactive programs that involve many prosumer devices and sensors that spontaneously join and leave the network.

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“…Furthermore, in [23] the authors present a study for the change propagation cost implementation in DRP, description for DREAM's API and it is consistency implementation on the Java language. Besides, Potato [10] is another example of a distributed reactive programming middleware. Potato is present as an approach to facilitate the deployment and maintenance of IoT systems.…”

Section: Semantic Framework For Reactive Languagesmentioning

confidence: 99%