Blockchain Decentralized Reputation, Monetization, and Auctioning in in Fog Computing

Abstract

Public fog nodes can be deployed in public places closer to the edge where many personal and commercial devices (e.g., a sensor, an application, or a device) can connect to. These public fog nodes can provide real-time and localized services, networking, computing, storage, and content delivery services to connected devices. The openness and pervasiveness of public fog nodes leads to the requirement of using trust models to ensure reliability, security, privacy, and meeting the service-level agreements (SLAs). Conventional trust models for public fog nodes are con_gured, deployed, and maintained in a centralized manner. In addition, the monetization and payment of such services are typically manual, centralized, and lacks the necessary trust. The providers of the public fog node services typically o_er _xed pricing models for their services, and the customers manually select and pay for the used services, with little or no transparency. This thesis proposes a decentralized trust model to provide a more trustworthy reputation for publicly available fog nodes. Furthermore, the thesis proposes a blockchain-based decentralized monetization technique for services provided by public fog nodes and considers di_erent SLA requirements for payment. The monetization embeds decentralized reverse auctioning and bidding in which fog service users can request speci_c services, and public fog nodes can make bid o_ers. The proposed solutions are designed using public Ethereum blockchain and smart contract technologies in order to enable decentralized, trustworthy, and automated reputation system with certain guarantees for QoS, customer satisfaction, and dispute resolutions. The proposed blockchain-schemes are tested and evaluated in terms of security, performance, and cost. The results show that using blockchain for decentralized reputation management could become more advantageous when compared to the existing centralized trust models.

Date of Award	Apr 2020
Original language	American English