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.
more »
« less
Contract-Theoretic Resource Control in Wireless Powered Communication Public Safety Systems
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.
more »
« less
- Award ID(s):
- 1849739
- NSF-PAR ID:
- 10228433
- Date Published:
- Journal Name:
- 2020 IEEE Global Communications Conference
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Modern Public Safety Networks (PSNs) are assisted by Unmanned Aerial Vehicles (UAVs) to provide a resilient communication paradigm during catastrophic events. In this context, we propose a distributed user-centric risk-aware resource management framework in UAV-assisted PSNs supported by both a static UAV and a mobile UAV. The mobile UAV is entitled to a larger portion of the available spectrum due to its capability and flexibility to re-position itself, and therefore establish better communication channel conditions to the users, compared to the static UAV. However, the potential over-exploitation of the mobile UAV-based communication by the users may lead to the mobile UAV’s failure to serve the users due to the increased levels of interference, consequently introducing risk in the user decisions. To capture this uncertainty, we follow the principles of Prospect Theory and design a user’s prospect-theoretic utility function that reflects user’s risk-aware behavior regarding its transmission power investment to the static and/or mobile UAV-based communication option. A non-cooperative game among the users is formulated, where each user determines its power investment strategy to the two available communication choices in order to maximize its expected prospect-theoretic utility. The existence and uniqueness of a Pure Nash Equilibrium (PNE) is proven and the convergence of the users’ strategies to it is shown. An iterative distributed and low-complexity algorithm is introduced to determine the PNE. The performance of the proposed user-centric risk-aware resource management framework in terms of users’ achievable data rate and spectrum utilization, is achieved via modeling and simulation. Furthermore, its superiority and benefits are demonstrated, by comparing its performance against other existing approaches with regards to UAV selection and spectrum utilization.more » « less
-
The importance to our society of emergency network communications cannot be underestimated. The loss of communication and network systems in a state of emergency denies victims of disaster and city emergency response teams critical information about the crisis. It is essential to restore communication systems and service in order to ensure continuous and efficient emergency operations. This paper presents a cloud-based cost-effective emergency network management system to provide dynamic network services. We have developed a network application to enable users to access the Internet during an emergency. The application is ready to immediately restore lost connectivity through economical embedded systems and UAVs. We implement our prototypes based on Resin.io, a framework that utilizes Linux containers on IoT(Internet of Things) devices to deploy applications. We evaluate our systems on Raspberry Pi, a popular embedded system. We demonstrate the feasibility of our proposed system, showing that it is an economical infrastructure that it can successfully be used for an emergency network to replace a demolished network infrastructure.more » « less
-
null (Ed.)Efficient arrangement of UAVs in a swarm formation is essential to the functioning of the swarm as a temporary communication network. Such a network could assist in search and rescue efforts by providing first responders with a means of communication. We propose a user-friendly and effective system for calculating and visualizing an optimal layout of UAVs. An initial calculation to gather parameter information is followed by the proposed algorithm that generates an optimal solution. A visualization is displayed in an easy-to-comprehend manner after the proposed iterative genetic algorithm finds an optimal solution. The proposed system runs iteratively, adding UAV at each intermediate conclusion, until a solution is found. Information is passed between runs of the iterative genetic algorithm to reduce runtime and complexity. The results from testing show that the proposed algorithm yields optimal solutions more frequently than the k-means clustering algorithm. This system finds an optimal solution 80% of the time while k-means clustering is unable to find a solution when presented with a complex problem.more » « less
-
null (Ed.)Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GLOBECOM42002.2020.9348200
