Dynamic Optimal Pricing for Heterogeneous Service-Oriented Architecture of Sensor-Cloud Infrastructure

• Published in: IEEE transactions on services computing Vol. 10; no. 2; pp. 203 - 216
• Main Authors: Chatterjee, Subarna, Ladia, Ranjana, Misra, Sudip
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological system modeling; Cloud computing; cloud pricing; Computational modeling; Energy consumption; Hardware; Homogeneity; Infrastructure; Optimization; Pricing; Provisioning; Residual energy; sensor owners; Sensor-cloud infrastructure; Sensors; Service oriented architecture; Signal strength; User satisfaction; wireless sensor network (WSN); Wireless sensor networks
• DOI: 10.1109/TSC.2015.2453958
• ISSN: 1939-1374

This paper proposes a dynamic and optimal pricing scheme for provisioning Sensors-as-a-Service (Se-aaS) [1] within the sensor-cloud infrastructure. Existing cloud pricing models are limited in terms of the homogeneity in service-types, and hence, are not compliant for the heterogeneous service oriented architecture of Se-aaS. We propose a new pricing model comprising of two components, applicable for Se-aaS architecture: pricing attributed to Hardware (pH) and pricing attributed to Infrastructure (pI). pH addresses the problem of pricing the physical sensor nodes subject to variable demand and utility of the end-users. It maximizes the profit incurred by every sensor owner, while keeping in mind the end-users' utility. pI mainly focuses on the pricing incurred due to the virtualization of resources. It takes into account the cost for the usage of the infrastructural resources, inclusive of the cost for maintaining virtualization within sensor-cloud. pI maximizes the profit of the sensor-cloud service provider (SCSP) by considering the user satisfaction. Simulation results depict improved performance of pH in comparison to the traditional hardware pricing algorithms, viz. PPM and Sprite, in terms of the residual energy, proximity to the base station (BS), received signal strength (RSS), overhead, and cumulative energy consumption. The results also show the tendency of the sensor-owners to converge to the end-user utility, but not exceed it. We also analyze the performance of pI. The results show the optimality in the profit incurred by SCSP and the user satisfaction.