Progressive Search Algorithm for Service Discovery in an IoT Ecosystem

Pattar, Santosh; Kulkarni, Dwaraka S; Vala, Darshil; Buyya, Rajkumar; Venugopal, K R; Iyengar, S. S.; Patnaik, L. M.

doi:10.1109/ithings/greencom/cpscom/smartdata.2019.00180

Cited by 12 publications

(11 citation statements)

References 17 publications

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“…Research in home appliance technologies over the past two decades were more focused on developing new electrical gadgets to achieve efficiency through smart use of electricity [18,19]. Studies have reported the technological advancements in the smart home arena coming from three generations based on electromagnetic contacts, temperature sensors, smart power accessories, passive RFID and infrared based sensors.…”

Section: Background and Need For The Studymentioning

confidence: 99%

“…Some of these sensors provide very little data, and further, low-cost devices are prone to data noise and computational limitations. While wearable devices such as personal health tracking gadgets can provide more accurate and rich data, they are intrusive and need to be charged and always operational [19]. With the recent advancement in the IoT landscape, various web-based applications could be used to distribute and process information via the Internet and the wireless home network connected to different home appliances and sensors.…”

Section: Background and Need For The Studymentioning

confidence: 99%

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Big Data and Personalisation for Non-Intrusive Smart Home Automation

Asaithambi

Venkatraman²,

Venkatraman

2021

BDCC

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With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.

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Section: Background and Need For The Studymentioning

confidence: 99%

Section: Background and Need For The Studymentioning

confidence: 99%

Big Data and Personalisation for Non-Intrusive Smart Home Automation

Asaithambi

Venkatraman²,

Venkatraman

2021

BDCC

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“…Such a solution comprises IoT ontologies, functional ontology instance matching, execution guidepost, and IoT devices status monitoring. P. Santosh et al 40 have tackled the problem of service discovery from the user perspective. They have developed a progressive search mechanism that searches and selects the most appropriate IoT services.…”

Section: Related Workmentioning

confidence: 99%

Towards a new semantic middleware for service description, discovery, selection, and composition in the Internet of Things

Berrani

Yachir

Djamaa

et al. 2022

Trans Emerging Tel Tech

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The emergence of the Internet of Things (IoT) allows the integration of everyday devices such as tags, sensors, and actuators into the Internet. IoT also provides handy and automatic techniques to interoperate ad‐hoc, heterogeneous, and mobile networks and simplify data sharing among devices by abstracting their capabilities as services. Nevertheless, the large number and heterogeneity of encompassed networks/devices and the diversity of the offered resources/services make the IoT service description, discovery, selection, and composition a challenging task. To deal with these issues, we introduce, in this article, a semantic middleware for IoT applications based on service composition. Such a solution supports the semantic description of IoT resources, including services and user requests, and provides a modular, end‐to‐end, and loosely coupled request resolution process that comprises a context‐aware service discovery, a semantic service selection, and an automatic lightweight service composition. The proposal is illustrated and extensively evaluated on a restricted smart‐city scenario. Obtained results show that the proposed service discovery, selection, and composition sub‐processes improve the scalability of equivalent state‐of‐the‐art solutions by around 15%$$ \% $$, 20%$$ \% $$, and 40%$$ \% $$, respectively.

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“…Typically, ITSs should be capable of scaling horizontally and handling large amount of traffic coming from several heterogeneous data sources such as IoT devices, smartphones, cameras, machine logs, and social media [38,45]. There are ten non-functional microservice performance requirements in the context of designing contemporary data architectures for intelligent systems: Batch data, stream data, late and out-of-order data, processing guarantees, integration and extensibility, distribution and scalability, cloud support and elasticity, fault-tolerance, flow control, and flexibility and technology agnosticism [38].…”

Section: Non-functional Microservice Performance Requirements Of Itssmentioning

confidence: 99%

MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

Asaithambi

Venkatraman

Venkatraman³

2020

BDCC

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Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.

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Progressive Search Algorithm for Service Discovery in an IoT Ecosystem

Cited by 12 publications

References 17 publications

Big Data and Personalisation for Non-Intrusive Smart Home Automation

Big Data and Personalisation for Non-Intrusive Smart Home Automation

Towards a new semantic middleware for service description, discovery, selection, and composition in the Internet of Things

MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

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