Benchmarking IoT middleware platforms

Cardoso, João M. P.; Pereira, Carlos; Aguiar, Ana; Morla, Ricardo

doi:10.1109/wowmom.2017.7974339

Cited by 21 publications

(21 citation statements)

References 5 publications

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“…In particular, FIWARE offers various added-value functions (services) by elaborating a public cloud along with an enriched library of modules, referred as Generic Enablers (GEs), which fulfil capabilities such as cloud hosting, IoT, services support and delivery, developer tools as well as network and devices interface [43,76]. Yet, it has been regarded that issues of interoperability among FIWARE GEs and applications exist while a considerable number of design flaws and severe bugs appear in some of the components as well [77][78][79]. Besides that, FIWARE is a rather large and complex platform involving enormous sets of services and components, which seem to be difficult to comprehend probably due to the lack of documentation updates.…”

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confidence: 99%

“…Besides that, FIWARE is a rather large and complex platform involving enormous sets of services and components, which seem to be difficult to comprehend probably due to the lack of documentation updates. As it can be concluded by the research in the literature [43,[76][77][78][79][80][81][82], the adoption of FIWARE in the development of smart agricultural applications is rather fragmentary and narrowed mostly to European researchers.…”

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confidence: 99%

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A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0

2020

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The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted.

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confidence: 99%

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confidence: 99%

A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0

2020

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“…Among the diversity of IoT communications middleware available, we picked oneM2M to enable our interoperable framework for its suitable performance to support e-health real-time applications [28][29][30]. OneM2M supports two communication models, namely, publish-subscribe and request-response.…”

Section: Onem2m-based E-health Frameworkmentioning

confidence: 99%

“…Few studies have benchmarked the performance of devices in M2M middleware, as performance evaluations of reference middleware implementations and protocols are only now becoming available [28][29][30]34].…”

Section: Performance-latency and Power Requirementsmentioning

confidence: 99%

Open IoT Architecture for Continuous Patient Monitoring in Emergency Wards

et al. 2019

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Due to multiple reasons, emergency wards can become overloaded with patients, some of which can be in critical health conditions. To improve the emergency service and avoid deaths and serious adverse events that could be potentially prevented, it is mandatory to do a continuous monitoring of patients physiological parameters. This is a good fit for Internet of Things (IoT) technology, but the scenario imposes hard constraints on autonomy, connectivity, interoperability, and delay. In this paper, we propose a full Internet-based architecture using open protocols from the wearable sensors up to the monitoring system. Particularly, we use low-cost and low-power WiFi-enabled wearable physiological sensors that connect directly to the Internet infrastructure and run open communication protocols, namely, oneM2M. At the upper end, our architecture relies on openEHR for data semantics, storage, and monitoring. Overall, we show the feasibility of our open IoT architecture exhibiting 20–50 ms end-to-end latency and 30–50 h sensor autonomy at a fraction of the cost of current non-interoperable vertical solutions.

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“…Kruger et al [13] present a micro and macro benchmarking scheme in their paper, however this was done to evaluate the suitability of commercial components such as Raspberry Pi and BeagleBone as IoT gateways. A benchmarking of IoT middlewares were done by Cardoso et al [4] which facilates selection of appropriate IoT middleware that is suitable for a particular deployment. In contrast, we focus on enabling development and network maintenance team with better benchmarking tools.…”

Section: Related Workmentioning

confidence: 99%

Real-time Distributed In-Situ Benchmarking of Energy Harvesting IoT Devices

Thangarajan

Yang

Joosen

et al. 2018

Proceedings of the 5th Workshop on Middleware and Applications for the Internet of Things

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The deployment of Internet of Things (IoT) devices is accelerating across a wide range of applications. The majority of today's IoT devices are powered by batteries that can operate for a maximum of a few years, after which they need to be replaced. This introduces two problems. First, the effort that is required to manually replace batteries cannot economically scale to support the next billion IoT devices. Secondly, treating billions of toxic batteries as disposable is not environmentally friendly. Together, these problems form a critical roadblock in deploying IoT solutions. The biggest problem facing the designers of IoT applications is ensuring that their application software is energy efficient enough to operate within the strict power envelope that is provided by batteries or energy harvesting hardware. In this paper, we tackle this problem through the introduction of a distributed benchmarking middleware that rapidly and accurately quantifies the power consumption of different software configurations. Critically, our middleware operates in real-time across a distributed network of devices, allowing developers to experiment with code changes at runtime. This makes it significantly easier for developers to write applications that operate within the power constraints of batteries or energy harvesting systems. We evaluate our approach on a real world energy harvesting testbed and demonstrate that benchmarking results are accurate, with limited overhead for developers.

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Benchmarking IoT middleware platforms

Cited by 21 publications

References 5 publications

A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0

A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0

Open IoT Architecture for Continuous Patient Monitoring in Emergency Wards

Real-time Distributed In-Situ Benchmarking of Energy Harvesting IoT Devices

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