Mechanism design for internet of things services market - Jiao, Yutao (Nanyang Technological University, 2020)
Over the past decade, the Internet of Things (IoT) adoption and applications have significantly increased. Massive amounts of data are continuously generated and transmitted among connected people and devices over wired and wireless networks. The IoT networks involve different kinds of resources, such as data, communication, and computing, which can become valuable commodities that are exchanged and traded between the service providers and the customers in online marketplaces. For efficient and sustainable resource usage, there is an immediate need for establishing market models for various IoT services and investigating the optimal resource allocation. In this thesis, we focus on designing novel and practical trading mechanisms for the IoT services market, where data, computing, and communication are three main types of resources. Accordingly, we investigate three typical IoT services, including the data analytics services, the cloud/fog computing services for blockchain, and the wireless powered data crowdsourcing services. The thesis presents three major contributions. First, we study the optimal pricing mechanisms and data management for data analytics services and further discuss the perishable services in the time-varying environment. We establish a data market model and define the data utility based on the impact of data size on the performance of data analytics. For perishable services, we study the perishability of data and provide a quality decay function. We apply the Bayesian profit maximization mechanism to selling data analytics services, which is strategyproof and computationally efficient. Our proposed data market model and pricing mechanism can effectively solve the profit maximization problem and provide useful strategies for the data analytics service provider. Second, we discuss the trading between the cloud/fog computing service provider and miners in blockchain networks and propose an auction-based market model for efficient computing resource allocation. We consider the proof-of-work based blockchain that relies on the computing resource. The allocative externalities are particularly addressed due to the competition among miners. We first study the constant-demand scheme where each miner bids for a fixed quantity of resources, and propose an auction mechanism that achieves optimal social welfare. Also, we consider a multi-demand scheme where the miners submit their preferable demands and bids. Since the social welfare maximization problem is NP-hard, we design an approximate algorithm which also guarantees the truthfulness, individual rationality, and computational efficiency. Third, we propose a wireless powered spatial crowdsourcing framework that consists of two mutually dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the spatial crowdsourcing platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, we present three strategyproof deployment mechanisms for the spatial crowdsourcing platform to place a mobile base station. We first apply the classical median mechanism and evaluate its worst-case performance. Given the workers’ geographical distribution, we propose the second strategyproof deployment mechanism to improve the spatial crowdsourcing platform’s expected utility. For a more general case with only the historical location data available, we finally propose a deep learning based strategyproof deployment mechanism to maximize the platform’s utility. The experiments based on synthetic and real-world datasets reveal the effectiveness of the proposed framework in the task and charging power allocation while avoiding the dishonest worker’s manipulation. In summary, this thesis mainly focuses on designing the trading mechanisms for IoT services, which is critical for efficient resource usage and the development of future green IoT ecosystem. To the best of our knowledge, this is the first work that studies the unique characteristics of typical resource types in the IoT system and addresses the corresponding strategyproof mechanism design problems with the rigorous theoretical analysis. Additionally, in this thesis, we not only build novel models but also develop the state-of-the-art deep learning based mechanism to solve the profit/social welfare optimization problem.