Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

With the development of the IoT (Internet of Things), sensors networks can bring a large amount of valuable data. In addition to be utilized in the local IoT applications, the data can also be traded in the connected edge servers. As an efficient resource allocation mechanism, the double auction has been widely used in the stock and futures markets and can be also applied in the data resource allocation in sensor networks. Currently, there usually exist multiple edge servers running double auctions competing with each other to attract data users (buyers) and producers (sellers). Therefore, the double auction market run on each edge server needs efficient mechanism to improve the allocation efficiency. Specifically, the pricing strategy of the double auction plays an important role on affecting traders’ profit, and thus, will affect the traders’ market choices and bidding strategies, which in turn affect the competition result of double auction markets. In addition, the traders’ trading strategies will also affect the market’s pricing strategy. Therefore, we need to analyze the double auction markets’ pricing strategy and traders’ trading strategies. Specifically, we use a deep reinforcement learning algorithm combined with mean field theory to solve this problem with a huge state and action space. For trading strategies, we use the Independent Parametrized Deep Q-Network (I-PDQN) algorithm combined with mean field theory to compute the Nash equilibrium strategies. We then compare it with the fictitious play (FP) algorithm. The experimental results show that the computation speed of I-PDQN algorithm is significantly faster than that of FP algorithm. For pricing strategies, the double auction markets will dynamically adjust the pricing strategy according to traders’ trading strategies. This is a sequential decision-making process involving multiple agents. Therefore, we model it as a Markov game. We adopt Multiagent Deep Deterministic Policy Gradient (MADDPG) algorithm to analyze the Nash equilibrium pricing strategies. The experimental results show that the MADDPG algorithm solves the problem faster than the FP algorithm.

Privacy preserving divisible double auction with a hybridized TEE-blockchain system

Double auction mechanisms have been designed to trade a variety of divisible resources (e.g., electricity, mobile data, and cloud resources) among distributed agents. In such divisible double auction, all the agents (both buyers and sellers) are expected to submit their bid profiles, and dynamically achieve the best responses. In practice, these agents may not trust each other without a market mediator. Fortunately, smart contract is extensively used to ensure digital agreement among mutually distrustful agents. The consensus protocol helps the smart contract execution on the blockchain to ensure strong integrity and availability. However, severe privacy risks would emerge in the divisible double auction since all the agents should disclose their sensitive data such as the bid profiles (i.e., bid amount and prices in different iterations) to other agents for resource allocation and such data are replicated on all the nodes in the network. Furthermore, the consensus requirements will bring a huge burden for the blockchain, which impacts the overall performance. To address these concerns, we propose a hybridized TEE-Blockchain system (system and auction mechanism co-design) to privately execute the divisible double auction. The designed hybridized system ensures privacy, honesty and high efficiency among distributed agents. The bid profiles are sealed for optimally allocating divisible resources while ensuring truthfulness with a Nash Equilibrium. Finally, we conduct experiments and empirical studies to validate the system and auction performance using two real-world applications.

Renewable Energy Certificate Trading via Permissioned Blockchain

With the continuous advancement of the green certificate trading mechanism, information verification needs to span multiple departments, which causes the application process cumbersome and human errors. In order to solve problems of cumbersome issuance process of the renewable energy certificate (REC) and the inflexible pricing mechanism, in this paper, a hybrid REC trading system was proposed based on an permissioned blockchain technology (BT), which combined advantages of the BT and the continuous double auction (CDA). The operation process of the system was introduced in detail, and the view change protocol in the Practical Byzantine Fault Tolerance algorithm was revised according to the characteristics of the system to improve the system stability. The continuous double auction rule was also introduced in the system. And corresponding bidding strategies were designed to maximize the revenue of users (buyer and seller) and transaction probability. The simulation experiment proves that the bidding mechanism can flexibly adjust the REC price according to the supply and demand relationship. At the same time, the effectiveness and feasibility of trading rule and bidding strategy were also verified.

Efficient Allocations in Double Auction Markets

This paper proposes a simple descriptive model of discrete-time double auction markets for divisible assets. As in the classical models of exchange economies, we consider a finite set of agents described by their initial endowments and preferences. Instead of the classical Walrasian-type market models, however, we assume that all trades take place in a centralized double auction where the agents communicate through sealed limit orders for buying and selling. We find that, under nonstrategic bidding, double auction clears with zero trades precisely when the agents’ current holdings are on the Pareto frontier. More interestingly, the double auctions implement Adam Smith’s “invisible hand” in the sense that, when starting from disequilibrium, repeated double auctions lead to a sequence of allocations that converges to individually rational Pareto allocations.