Pricing Decision Analysis for Information Services of the Internet of Things Based on Stackelberg Game

Mei, Lijun; Li, Wei; Nie, Kai

doi:10.1007/978-3-642-32054-5_155

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Information can be treated as goods that can be sold and bought in a market. In [11], the authors studied the information service pricing by formulating a hierarchical game, i.e., a Stackelberg game, among information providers, brokers, and customers. The brokers acquire information from the providers, i.e., leaders, and sell the services to the customers, i.e., followers.…”

Section: Pricing Models For Information Marketmentioning

confidence: 99%

Smart data pricing models for the internet of things: a bundling strategy approach

et al. 2016

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Internet of things (IoT) has emerged as a new paradigm for the future Internet. In IoT, enormous devices are connected to the Internet and thereby being a huge data source for numerous applications. In this article, we focus on addressing data management in IoT through using a smart data pricing (SDP) approach. With SDP, data can be managed flexibly and efficiently through intelligent and adaptive incentive mechanisms. Moreover, it is a major source of revenue for providers and partners. We propose a new pricing scheme for IoT service providers to determine the sensing data buying price and IoT service subscription fee offered to sensor owners and service users, respectively. Additionally, we adopt the bundling strategy that allows multiple providers to form a coalition and bid their services as a bundle, attracting more users and achieving higher revenue. Finally, we outline some important open research issues for SDP and IoT.

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Section: Pricing Models For Information Marketmentioning

confidence: 99%

Smart data pricing models for the internet of things: a bundling strategy approach

et al. 2016

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“…Auxiliary users purchase the data with the goal that they can opportunistically access idle licensed spectrum efficiently. Mei et al [24] proposed a pricing model for a single seller and multiple buyers based on auctions. In this model, the buyers submit their bidding prices to the auctioning system and the highest bidder is deemed the winner.…”

Section: Related Workmentioning

confidence: 99%

“…Through their simulation, they investigated the impact of starting an investment, agreement, or term period, the rate of devaluation, the nature of administration, and age of the assets on the asset cost. In recent days, many models for smart data pricing [24,27,28] and techniques have been proposed for Cloud and IoT. In [29], five Sensor Cloud (SC) evaluating Pricing Models (PM) (SCPM1, SCPM2, SCPM3, SCPM4, and SCPM5 ) are proposed.…”

Section: Related Workmentioning

confidence: 99%

A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment

Battula

Garg

Naha

et al. 2019

Sensors

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Fog computing aims to support applications requiring low latency and high scalability by using resources at the edge level. In general, fog computing comprises several autonomous mobile or static devices that share their idle resources to run different services. The providers of these devices also need to be compensated based on their device usage. In any fog-based resource-allocation problem, both cost and performance need to be considered for generating an efficient resource-allocation plan. Estimating the cost of using fog devices prior to the resource allocation helps to minimize the cost and maximize the performance of the system. In the fog computing domain, recent research works have proposed various resource-allocation algorithms without considering the compensation to resource providers and the cost estimation of the fog resources. Moreover, the existing cost models in similar paradigms such as in the cloud are not suitable for fog environments as the scaling of different autonomous resources with heterogeneity and variety of offerings is much more complicated. To fill this gap, this study first proposes a micro-level compensation cost model and then proposes a new resource-allocation method based on the cost model, which benefits both providers and users. Experimental results show that the proposed algorithm ensures better resource-allocation performance and lowers application processing costs when compared to the existing best-fit algorithm.

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“…Consider such a hierarchical information market, the authors in [256] investigated the information service pricing by formulating the Stackelberg game, among service providers, brokers, and customers. The brokers acquire information from the providers, i.e., leaders, and sell the services to the customers, i.e., followers.…”

Section: Pricing Models For the Platform's And Iot Service Providementioning

confidence: 99%

Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

Luong

Hoang

Wang

et al. 2016

IEEE Commun. Surv. Tutorials

295

124

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Abstract-This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the main component of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in Machine-to-Machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.

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Pricing Decision Analysis for Information Services of the Internet of Things Based on Stackelberg Game

Cited by 8 publications

References 4 publications

Smart data pricing models for the internet of things: a bundling strategy approach

Smart data pricing models for the internet of things: a bundling strategy approach

A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment

Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

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