The sheer growth of electricity demand and the rising number of electricity-hungry devices have highlighted and elevated the need of addressing the demand response management problem in residential smart grid systems. In this paper, a novel contract-theoretic demand response management (DRM) framework in residential smart grid systems is introduced based on the principles of labor economics. The residential households produce and consume electricity, acting as dynamic prosumers. Initially, the prosumers' personal electricity generation and consumption characteristics are captured by introducing the concept of prosumers' types. Then, the prosumers' and the electricity market's profit is depicted in representative utility functions. Based on the labor economics principles, Contract Theory is adopted to design the interactions among the electricity market, which offers personalized rewards to the prosumers in order to buy electricity at an announced price, and the prosumers, who offer their "effort" by paying for the purchased electricity. The contract-theoretic DRM problem is formulated as a maximization problem of the electricity market's utility, while jointly guaranteeing the optimal satisfaction of the prosumers, under the scenarios of complete and incomplete information from the electricity market's perspective regarding knowing or not the prosumers' types, respectively. The corresponding optimization problems are solved following a convex optimization approach and the optimal contracts, i.e., rewards and efforts, are determined. Detailed numerical results obtained via modeling and simulation, highlight the key operation features and superiority of the proposed framework.
Summary Blockchain systems rely on oracles to bridge external information to the decentralized applications residing in the systems. Astraea protocols are decentralized oracle designs utilizing majority‐voting mechanism to determine the oracle outcomes and/or rewards to voters. However, the voters are indifferent between voting through a single or multiple identities, as the potential rewards by the decentralized oracles grow linearly with the voters stakes. Additionally, the majority‐voting mechanism may facilitate herd behaviors among the voters, as the voters are rewarded only if they are in agreement with the majority outcomes. In this paper, a novel oracle protocol is introduced by proposing a peer prediction‐based scoring scheme along with non‐linear staking rules, aiming at extracting subjective data truthfully. Specifically, an incentive compatible scoring scheme is designed so that voters uniquely maximize their expected score by honest reporting. The voters are rewarded when their report achieves a relatively high score compared to the rest of the voters, as opposed to the existing schemes, where a reward is only given when they agree to the majority. Furthermore, a non‐linear stake scaling rule is proposed to discourage Sybil attacks. Detailed simulation results are presented to show the operation of the proposed oracle protocol and its improvement compared to indicative mechanisms proposed in the existing literature.
In this article, we address the problem of prolonging the battery life of Internet of Things (IoT) nodes by introducing a smart energy harvesting framework for IoT networks supported by femtocell access points (FAPs) based on the principles of Contract Theory and Reinforcement Learning. Initially, the IoT nodes’ social and physical characteristics are identified and captured through the concept of IoT node types. Then, Contract Theory is adopted to capture the interactions among the FAPs, who provide personalized rewards, i.e., charging power, to the IoT nodes to incentivize them to invest their effort, i.e., transmission power, to report their data to the FAPs. The IoT nodes’ and FAPs’ contract-theoretic utility functions are formulated, following the network economic concept of the involved entities’ personalized profit. A contract-theoretic optimization problem is introduced to determine the optimal personalized contracts among each IoT node connected to a FAP, i.e., a pair of transmission and charging power, aiming to jointly guarantee the optimal satisfaction of all the involved entities in the examined IoT system. An artificial intelligent framework based on reinforcement learning is introduced to support the IoT nodes’ autonomous association to the most beneficial FAP in terms of long-term gained rewards. Finally, a detailed simulation and comparative results are presented to show the pure operation performance of the proposed framework, as well as its drawbacks and benefits, compared to other approaches. Our findings show that the personalized contracts offered to the IoT nodes outperform by a factor of four compared to an agnostic type approach in terms of the achieved IoT system’s social welfare.
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