Paper: Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading
Personal data is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. This paper introduces a decentralized marketplace for trading personal sensing data, generated by resource-constrained IoT devices in real-time.
To design such a marketplace, we consider both computing and economic aspects. The computing approach focuses on creating a blockchain-based decentralized marketplace that ensures the integrity of trade agreements. We leverage smart contracts to guarantee that the participants automatically conform to the terms of the agreements without involving a mediator.
The economic approach maximizes the marketplace participant’s utility to improve the sustainability and usability of the marketplace. To this end, we present a blockchain framework underpinned by an Energy-Aware Demand Selection and Allocation (EDSA) mechanism that optimizes the revenue for the seller and fulfills the buyer’s demands.
We use a normalized revenue-based greedy algorithm for solving the EDSA problem. Moreover, we present a proof-of-concept implementation of the blockchain framework in Ethereum.
Through extensive simulations, we separately evaluate the impact of buyer’s demand on the battery drainage of the IoT devices under different scenarios. We observe that increasing battery capacity for IoT devices on the seller end is more strongly related to revenue generation than increasing the number of devices.
Fig. 5. Revenue generation and demand selection with NR-based algorithm.
Pooja Gupta, Volkan Dedeoglu, Kamran Najeebullah, Salil S. Kanhere, Raja Jurdak. Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading. SmartComp 2020 Cryptography and Security (cs.CR); Networking and Internet Architecture (cs.NI).
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