A Bio-Inspired Service Discovery and Selection Approach for IoT Applications

Rapti, Elli; Karageorgos, Anthony; Houstis, Catherine E.; Houstis, Elias N.

doi:10.1109/scc.2016.126

Cited by 15 publications

(16 citation statements)

References 16 publications

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“…Note that these approaches (Wei and Jin, 2012; Zhou and Ma, 2012; Paganelli and Parlanti, 2012; Hussein et al , 2015; Rapti et al , 2016; Forestiero, 2017; Yousefpour et al , 2019) did not mention any service description in their works. However, a recent work (Liu et al , 2018) described IoT services provided by intelligent devices using raw description text and applied a new topic modeling method to extract raw topics from raw description text and then asked users to cluster topic words based on their own cognition.…”

Section: Comparative Analysis Of Service Discovery Approaches In Internet Of Thingsmentioning

confidence: 99%

“…Considerable research has been devoted to the adoption of bio-inspired computing paradigms; using this paradigm is very suitable for pervasive environments such as IoT, taking advantages of their inherent capability such as effective management of resources, self-organization and self-adaptivity. The approach (Rapti et al , 2016) proposed a decentralized service discovery and selection model in the pervasive environment of IoT inspired from response threshold model taking advantage of the inherent capability of social insect colonies to operate without any central control. In this work, the service provision nodes (SPNs) are modeled as agents which act locally based on the bio-inspired response threshold model, and the satisfaction of a user request emerges as the result of individual agent actions.…”

Section: Comparative Analysis Of Service Discovery Approaches In Internet Of Thingsmentioning

confidence: 99%

“…Peer to peer overlays are also applied in some approaches such Li et al (2015), Cirani et al (2014), Paganelli and Parlanti (2012). The authors in Butt et al (2013), Rapti et al (2016), Gochhayat et al (2018), Forestiero (2017) choose to implement their architecture using agents. However some approaches do not perform any architecture such Fredj et al (2014), Klauck and Kirsche (2012), Khanouche et al (2016), Jin et al (2016).…”

Section: Comparative Analysis Of Service Discovery Approaches In Internet Of Thingsmentioning

confidence: 99%

See 2 more Smart Citations

A full comparison study of service discovery approaches for internet of things

Aziez

Benharzallah

Bennoui

2019

IJPCC

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Purpose The purpose of this paper is to address the Internet of Things (IoT) service discovery problem and investigate the existing solutions to tackle this problem in many aspects. Design/methodology/approach This paper presents an overview of IoT services aiming at providing a clear understanding about their features because this term is still ambiguous for the IoT service discovery approaches. Besides, a full comparison study of the most representative service discovery approaches in the literature is presented over four perspectives: the IoT information model, the mechanism of IoT service discovery, the adopted architecture and the context awareness. These perspectives allow classifying, comparing and giving a deeper understanding of the existing IoT service discovery solutions. Findings This paper presents a new definition and a new classification of IoT services and citation of their features comparing with the traditional Web services. This paper discusses the existing solutions, as well as the main challenges, that face the service discovery issue in the IoT domain. Besides, two classifications of the approaches are adopted on the basis of their service description model and their mechanism of discovery, and a set of requirements that need to be considered when defining an IoT service are proposed. Originality/value There are few number works that survey the service discovery approaches in the IoT domain, but none of these surveys discuss the service description models in the IoT or the impact of the context awareness aspect in the service discovery solution. There are also few works that give a comprehensive overview of IoT services to understand their nature to facilitate their description and discovery. This paper fills this gap by performing a full comparison study of multi-category and recent approaches for service discovery in the IoT over many aspects and also by performing a comprehensive study of the IoT service features.

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Section: Comparative Analysis Of Service Discovery Approaches In Internet Of Thingsmentioning

confidence: 99%

Section: Comparative Analysis Of Service Discovery Approaches In Internet Of Thingsmentioning

confidence: 99%

Section: Comparative Analysis Of Service Discovery Approaches In Internet Of Thingsmentioning

confidence: 99%

See 1 more Smart Citation

A full comparison study of service discovery approaches for internet of things

Aziez

Benharzallah

Bennoui

2019

IJPCC

View full text Add to dashboard Cite

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“…It also uses a routing algorithm to determine next node to receive query message based upon their capability to answer queries in past. Li J et al in [17] make use of semantic descriptions and locate services based upon QoS demand of requester. Authors have also devised an efficient trust based propagation mechanism for service discovery.…”

Section: Related Workmentioning

confidence: 99%

Cluster Based Distributed Service Discovery in Internet of Vehicle

Sharma

Chauhan

Chand

2021

JCOMSS

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As automobile sector is witnessing a paradigm shift from Internal Combustion Engine (ICE) based vehicles to smart electric vehicles, a new concept of connected vehicles has emerged that is able to provide a number of services to its users. Internet of Vehicles has now transformed to an indispensable platform of information exchange among vehicles, city infrastructure, drivers and other connected entities. Due to highly dynamic nature of Internet of Vehicles, there is need of a dynamic service discovery infrastructure that can cater to Internet of Vehicle specific challenges. This paper is a step ahead in that direction to provide a distributed service discovery protocol that facilitates service discovery and service selection for Internet of Vehicles environment. We have proposed a clustering based service discovery approach that makes use of neighbor awareness to find neighboring vehicles. Proposed protocol has been validated by running extensive simulations and results show the improvement in terms of query success rate, transmission rate and transmission cost by a considerable margin.

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“…DHT-based approaches have the potential to increase the scalability of service discovery, but they may not provide the availability of all published service information. This is however causing several problems for dynamicity of the overlay network [17,18]. The approaches running on unstructured overlay networks are designed by the themes of constrained-flooding and broadcasting-based search algorithms for the service discovery.…”

Section: Introductionmentioning

confidence: 99%

SARL: A reinforcement learning based QoS-aware IoT service discovery model

Koşunalp

Demir

2020

Journal of Electrical Engineering

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The IoT environment includes the enormous amount of atomic services with dynamic QoS compared with traditional web services. In such an environment, in the service composition process, discovering a requested service meeting the required QoS is a di cult task. In this work, to address this issue, we propose a peer-to-peer-based service discovery model, which looks for the information about services meeting the requested QoS and functionality on an overlay constructed with users of services versus service nodes, with probably constrained resources. However, employing a plain discovery algorithm on the overlay network such as flooding, or k-random walk could cause high message overhead or delay. This necessitates an intelligent and adaptive discovery algorithm, which adapts itself based on users’ previous queries and the results. To fill this gap, the proposed service discovery approach is equipped with a reinforcement learning-based algorithm, named SARL. The reinforcement learning-based algorithm enables SARL to significantly reduce delay and message overhead in the service discovery process by ranking neighboring nodes based on users’ service request preferences and the service query results. The proposed model is implemented on the OMNet simulation platform. The simulation results demonstrate that SARL remarkably outperforms the existing approaches in terms of message overhead, reliability, timeliness, and energy usage efficiency.

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A Bio-Inspired Service Discovery and Selection Approach for IoT Applications

Cited by 15 publications

References 16 publications

A full comparison study of service discovery approaches for internet of things

A full comparison study of service discovery approaches for internet of things

Cluster Based Distributed Service Discovery in Internet of Vehicle

SARL: A reinforcement learning based QoS-aware IoT service discovery model

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