Benchmarking IoT Context Management Platforms: High-level Queries Matter

Medvedev, Alexey; Hassani, Alireza; Zaslavsky, Arkady; Haghighi, Pari Delir; Ling, Sea; Jayaraman, Prem Prakash

doi:10.1109/giots.2019.8766395

Cited by 8 publications

(30 citation statements)

References 11 publications

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“…IoT has evolved to the phase of an ecosystem, where it behaves as a system of other systems. As part of an ecosystem, smart devices can communicate with each other spontaneously without being locked into the silo of a company producing the device or software [25]. Here, extracting context from IoT data plays an important role as an input to any of the decision-making systems.…”

Section: State Of the Art In Iotmentioning

confidence: 99%

“…The entity is a person, place, or object that is considered relevant to the interaction between a user and an application. A situation can be treated as a context with a higher level that was derived from the raw context by applying specific algorithms [25]. In the domain of digital advertising, which may use an indoor positioning system (IPS), the entity can be the user of an overall system of digital advertising, i.e., the device that the user uses to interact with the system, the products displayed on the shelves, the WiFi sensor, and many others.…”

Section: State Of the Art In Iotmentioning

confidence: 99%

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Towards Marketing 4.0: Vision and Survey on the Role of IoT and Data Science

et al. 2023

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According to KPMG, Internet of Things (IoT) technology was among the top 10 technologies of 2019. It has been growing at a significant pace, influencing and disrupting several application domains. It is expected that by 2025, 75.44 billion devices will be connected to the Internet. These devices generate massive amounts of data which, when harnessed using the power of data science (DS) techniques and approaches such as artificial intelligence (AI) and machine learning (ML), can provide significant benefits to economy, society, and people. Examples of areas that are being disrupted are digital marketing and retail commerce services in smart cities. This paper presents a vision for Marketing 4.0 that is underpinned by disruptive digital technologies such as IoT and DS. We present an analysis of the current state of the art in IoT and DS via the three pillars of marketing: namely, people, products, and places. We propose a blueprint architecture for developing a Marketing 4.0 solution that is underpinned by IoT and DS. We conclude the paper by highlighting the open challenges that need to be addressed in order to realise the Marketing 4.0 blueprint architecture, including supporting the integration of IoT data concerning people, products, and places and using DS to make efficient and effective recommendations.

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Section: State Of the Art In Iotmentioning

confidence: 99%

Section: State Of the Art In Iotmentioning

confidence: 99%

Towards Marketing 4.0: Vision and Survey on the Role of IoT and Data Science

et al. 2023

View full text Add to dashboard Cite

show abstract

“…For example, Medvedev et al [ 21 ], evaluated the performance of OpenIoT platform from data ingestion and data storage perspective. The authors in [ 22 ] use contextual queries for benchmarking IoT context management platforms. In another research by Salhofer et al [ 23 ], the different components (General Enablers) of the FIWARE platform has been thoroughly analyzed and evaluated from an application deployment point of view.…”

Section: Related Workmentioning

confidence: 99%

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms

Mondal

Jayaraman

Haghighi

et al. 2022

Sensors

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With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper.

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“…To measure the performance of the proposed solution, an evaluation framework is required. In this paper, based on the benchmarking approach presented in [13], we considered two main metrics that can affect query execution performance. These metrics are: (i) the number of parallel queries, and (ii) the richness of incoming queries.…”

Section: Cqe Experiments Metricsmentioning

confidence: 99%

“…Offering such functions will reduce the development complexity of context-aware applications. Moreover, supporting complex context queries can lead to higher performance by reducing communication overhead and creating the opportunity for query optimisation [13]. However, in order to support complex queries, it is required to develop a more complicated query execution mechanism for CMP to handle such queries.…”

Section: Introductionmentioning

confidence: 99%

Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications

Hassani

Medvedev

Zaslavsky

et al. 2019

Sensors

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As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.

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Benchmarking IoT Context Management Platforms: High-level Queries Matter

Cited by 8 publications

References 11 publications

Towards Marketing 4.0: Vision and Survey on the Role of IoT and Data Science

Towards Marketing 4.0: Vision and Survey on the Role of IoT and Data Science

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms

Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications

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