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Modern smart grid systems exploit a two-way interaction paradigm between the utility and the electricity user and promote the role of prosumer, as a new user type, able to generate and sell energy, or consume energy. Within such a setting, the prosumers and their interactions with the microgrid system become of high significance for its efficient operation. In this article, to model the corresponding interactions, we introduce a labor economics-based framework by exploiting the principles of contract theory, that jointly achieves the satisfaction of the various interacting system entities, i.e., the microgrid operator (MGO) and the prosumers. The MGO offers personalized rewards to the sellers and buyers, to incentivize them to sell and purchase energy, respectively. To provide a stable and efficient operation point, while aiming at jointly satisfying the profit and requirements of the involved competing parties, optimal personalized contracts, i.e., rewards and amount of sold/purchased energy, are determined, by formulating and solving contract-theoretic optimization problems between the MGO and the sellers or buyers. The analysis is provided for both cases of complete and incomplete information availability regarding the prosumers’ types. Detailed numerical results are presented to demonstrate the operation characteristics of the proposed framework under diverse scenarios. 

Recent technological advances in the use of Unmanned Aerial Vehicles (UAVs) and Wireless Powered Communications (WPC) have enabled the energy efficient operation of the Public Safety Networks (PSN) during disaster scenarios. In this paper, an energy efficient information flow and energy harvesting framework capturing users' risk-aware characteristics is introduced based on the principles of Contract Theory. To better support the operational effectiveness of the proposed framework, users are clustered in rescue groups following a socio-physical-aware group formation mechanism, while rescue leaders for each group are selected. A reinforcement learning approach is applied to enable the optimal matching between the UAVs and the rescue leaders in a distributed and efficient manner. The proposed contract-theoretic framework models the UAVs-victims relation based on a labor market setting via offering rewards to the users (incentives) in order to compensate them for their invested labor (reporting information). Detailed numerical results demonstrate the benefits and superiority of the proposed framework under different settings. 

In this paper an Unmanned Aerial Vehicles (UAVs) - enabled dynamic multi-target tracking and data collection framework is presented. Initially, a holistic reputation model is introduced to evaluate the targets' potential in offloading useful data to the UAVs. Based on this model, and taking into account UAVs and targets tracking and sensing characteristics, a dynamic intelligent matching between the UAVs and the targets is performed. In such a setting, the incentivization of the targets to perform the data offloading is based on an effort-based pricing that the UAVs offer to the targets. The emerging optimization problem towards determining each target's optimal amount of offloaded data and the corresponding effort-based price that the UAV offers to the target, is treated as a Stackelberg game between each target and the associated UAV. The properties of existence, uniqueness and convergence to the Stackelberg Equilibrium are proven. Detailed numerical results are presented highlighting the key operational features and the performance benefits of the proposed framework. 

Given the substantial penetration of social networks in citizens' everyday life activities, the success of a public safety system depends on the citizens' incentivization by the Emergency Control Center (ECC), and their effective effort contribution in the overall disaster management operation. In this paper, we introduce a formal method based on the principles of Contract Theory, to identify the optimal rewards to the citizens from the ECC's perspective, and the optimal invested effort from the citizens' side, referred to as contract pairs. The identification of these contract pairs (i.e., rewards and respective efforts) between the ECC and each citizen, depend on each citizen's social and communication characteristics that are used to define their specific type and profile, while they are properly reflected in the corresponding designed utility functions to be optimized. The problem under consideration is treated for both cases of complete (ideal) and incomplete (realistic) information availability, with respect to the level of knowledge of the ECC about the exact type of each citizen. The overall framework was evaluated via modeling and simulation, in terms of its efficiency and effectiveness, by studying multiple operation approaches and scenarios.