Middleware for Internet of Things: A quantitative evaluation in small scale

Palade, Andrei; Cabrera, Christian; White, Gary; Razzaque, Mohammad A.; Clarke, Siobhán

doi:10.1109/wowmom.2017.7974340

Cited by 24 publications

(9 citation statements)

References 11 publications

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“…We expect Fluidware be supported by a middleware capable of instantiating local proxies of funnel processes and launch them in execution into the proper location [6], and to relocate them as needed. To promote scalability and flexibility, the platform will support interactions and coordination at three levels (as from Figure 2): direct device-to-device interactions (e.g., for field-based coordination), with funnel processes directly instantiated on sets of devices, and with events flowing, aggregating and re-distributing from device to device; edge level, with funnel processes dynamically allocated on edge computers (i.e., cloudlets or fog computers), to digest streams of events, implement local coordinated services, and possibly to connect multiple edges to realize inter-edge coordination; cloud level, for centralized monitoring, coordination, and storage.…”

Section: B Middlewarementioning

confidence: 99%

“…Concerning middleware, a variety of platforms have been proposed to support the deployment and execution of IoT services and applications (see [6] for a survey) and including solutions to adaptively handle interoperability [16], contextdependency [23], spatial aggregation [24] and adaptivity [2]. Similarly to them, the Fluidware platform will promote interoperability (thanks to device-independence of the funnel process abstraction), adaptivity (thanks to the flexible deployment of funnel processes) and context-dependency (events digested by funnel processes are inherently contextual).…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the specification of funnel processes totally abstracts from the actual number and type of devices to which events belong, enabling to define scale-independent computational activities inherently addressing self-adaptation to contextual conditions, and smoothly fitting various computing/network infrastructures. Indeed, a proper supporting platform (i.e., a middleware [6]) will be put in place to take care of the actual deployment of funnel processes and their transparent allocation and replication.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Adaptive Flow Programming for the IoT: The Fluidware Approach

Zambonelli

Viroli

Fortino

et al. 2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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The objective of this position paper is to present Fluidware, a proposal towards an innovative programming model for the IoT, conceived to ease the development of flexible and robust large-scale IoT services and applications. The key innovative idea of Fluidware is to abstract collectives of devices of the IoT fabric as sources, digesters, and targets of distributed "flows" of contextualized events, carrying information about data produced and actuating commands. Accordingly, programming services and applications implies declaratively specifying "funnel processes" to channel, elaborate, and redirect such flows in a fully-distributed way, as a means to coordinate the activities of devices and realize services and applications. The potential applicability of Fluidware and its expected advantages are exemplified via example in the area of ambient assisted living.

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Section: B Middlewarementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Adaptive Flow Programming for the IoT: The Fluidware Approach

Zambonelli

Viroli

Fortino

et al. 2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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“…This component is designed to reduce the complexity of some of the previously mentioned challenges. An IoT platform can help application development by combining heterogeneous devices and encouraging interoperability within the various applications and services working on these devices [15]. It provides a well-defined Application Programming Interface (API) and supports operations which interact with the resources presented by IoT devices or infrastructures.…”

Section: Introductionmentioning

confidence: 99%

An IoT Platform Based on Microservices and Serverless Paradigms for Smart Farming Purposes

Trilles

González‐Pérez

Huerta

2020

Sensors

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Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform.

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“…Such applications are generally distributed among several devices since a single device is not capable of executing an entire application. For example, a smart route planner for users in a mall can be opportunistically provisioned by exploiting the ad-hoc interactions of a personal-shopper's phone, a nearby car's satellite navigator, and the mall's information kiosk to enable the requested functionality [8,17]. Apart from the functional requirements, this composition process should create solutions with the best possible QoS.…”

Section: Introductionmentioning

confidence: 99%

Stigmergic Service Composition and Adaptation in Mobile Environments

Palade

Cabrera

White

et al. 2018

Service-Oriented Computing

Self Cite

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Users within a limited geographic area can form servicesharing communities using the services deployed on their mobile devices. Creating Quality of Service (QoS) optimal service compositions in such decentralised and dynamic environments is challenging because of the service providers' mobility and the inherent dynamism in the available services. Existing proposals for mobile environments either use templatematching composition or require a-priori knowledge about the QoS objectives' weights, which limits the composition's flexibility in such environments. This paper presents a stigmergic-based approach to model the decentralised, flexible and dynamic service interactions of providers in a mobile environment. A nature-inspired optimisation mechanism is used to approximate the set of QoS optimal compositions that result from these interactions. To facilitate adaptation of the composite during execution, we introduce a procedure that encourages the exploration of service composition configurations that emerge as a result of providers' mobility. We evaluate the performance of the proposed approach with a no-adaptation variant, a Dijkstra-based, a Greedy and a Random approach. The results show that the proposed approach can obtain superior solutions compared with current optimisation methods for flexible service composition in mobile environments at the cost of increased overhead.

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Middleware for Internet of Things: A quantitative evaluation in small scale

Cited by 24 publications

References 11 publications

Towards Adaptive Flow Programming for the IoT: The Fluidware Approach

Towards Adaptive Flow Programming for the IoT: The Fluidware Approach

An IoT Platform Based on Microservices and Serverless Paradigms for Smart Farming Purposes

Stigmergic Service Composition and Adaptation in Mobile Environments

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