Matching Offerings and Queries on an Internet of Things Marketplace

Charpenay, Victor; Nguyen, Hoan; Ibrahim, Mohamad; Zappa, Achille; Bröring, Arne

doi:10.1007/978-3-319-98192-5_13

Cited by 5 publications

(4 citation statements)

References 5 publications

(4 reference statements)

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“…Different features of the services in the IoT, such as fixed or mobile, scalable or non-scalable, portable or nonportable, have been studied in [30] when making service recommendations. The authors of [31] have studied the matching scheme between registered services and users' requirements in IoT marketplace. A multi-agent recommendation algorithm to solve the object recommendation problem in the IoT environment have been proposed in [32] from a distributed perspective.…”

Section: Related Workmentioning

confidence: 99%

Towards Recommendation in Internet of Things: An Uncertainty Perspective

et al. 2020

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As a bridge between the physical and cyber world, the Internet of Things (IoT) senses and collects a large amount of user data through different types of devices connected to it. As a general information filtering technology, the recommender systems can help to associate information with each other in the IoT and to recommend personalized services for users. However, in practical applications, the collected data is uncertain due to noise, sensor errors, transmission errors, etc., which in turn affects system performance. In order to solve the data uncertainty problem in the IoT-based recommender systems, we propose a new recommender framework with item dithering. In this framework, the list of recommendations generated by the recommender algorithm is stored in a newly opened storage space for the entire session of the interaction between the user and the system. When the user interacts with the system, the list is pushed to the user after being shaken. Based on the proposed framework, we designed IDither, an item-based dithering and recommendation algorithm to shake out irrelevant items through predetermined indicators, thereby retaining the items required by the user and recommending them to the user. Experiment evaluations on real datasets show that IDither is an effective solution for handling uncertainty in the IoT-based recommender systems. We also found that IDither can be viewed as a list updating tool to increase diversity and novelty. INDEX TERMS Recommender systems, Internet of Things, data uncertainty, dithering.

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Section: Related Workmentioning

confidence: 99%

Towards Recommendation in Internet of Things: An Uncertainty Perspective

et al. 2020

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“…A facilitator receives a list of demands from buyers and a set of offerings from sellers. It matches the service offerings and demands based on the criteria specified by both the actors, such as location, data types, budget-pricing using a discovery and selection algorithm proposed in [27]. Next, the facilitator creates a list of potentially matching buyers and sends it to all the identified sellers.…”

Section: B Actors and Their Interactionsmentioning

confidence: 99%

Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading

Gupta

Dedeoglu

Najeebullah

et al. 2020

2020 IEEE International Conference on Smart Computing (SMARTCOMP)

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“…Both algorithms are intended to protect the marketplace from collapse in certain trading scenarios. A semantic matching model for IoT marketplace is presented in [18]. The model facilitates the matching process between providers' offerings and consumers' queries in a marketplace of the BIG IoT project [19].…”

Section: B Iot Trading Mechanismsmentioning

confidence: 99%

AMACoT: A Marketplace Architecture for Trading Cloud of Things Resources

Alrawahi

Lee

Lotfi

2020

IEEE Internet Things J.

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Cloud of Things (CoT) is increasingly viewed as a paradigm that can satisfy the diverse requirements of emerging IoT applications. The potential of CoT is not yet realised due to challenges in sharing and reusing IoT physical resources across multiple applications. Existing approaches provide smallscale and hardware-dependent shared access to IoT resources. This paper considers using market mechanisms to commoditise CoT resources as the approach to enable shared access to CoT resources and to improve their reusability. In order to achieve this, the requirements for trading CoT resources are discussed to conceptualise the proposed approach. A generic description model for CoT resource is introduced to quantify the value of CoT resources. In this paper, a marketplace architecture for trading CoT resources referred to as AMACoT is proposed. By formulating the trading of CoT resources as an optimisation problem, the proposed approach is experimentally validated. The evaluation measures the system performance and verifies the optimisation problem using three evolutionary algorithms. The evaluation of the optimisation algorithms demonstrates the optimality of trading CoT resources solutions in terms of resource cost, resource utilisation, provider lock-in and provider profit.

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Matching Offerings and Queries on an Internet of Things Marketplace

Cited by 5 publications

References 5 publications

Towards Recommendation in Internet of Things: An Uncertainty Perspective

Towards Recommendation in Internet of Things: An Uncertainty Perspective

Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading

AMACoT: A Marketplace Architecture for Trading Cloud of Things Resources

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