Cooperative Dynamic Game-Based Optimal Power Control in Wireless Sensor Network Powered by RF Energy

Abstract

This paper focuses on optimal power control in wireless sensor networks powered by RF energy, under the simultaneous wireless information and power transfer (SWIFT) protocol, where the information and power can be transmitted at the same time. We aim to maximize the utility for each sensor through the optimal power control, considering the influences of both the SINR and the harvested energy. The utility maximization problem is formulated as a cooperative dynamic game of a given time duration. All the sensors cooperate together to control their transmission power to maximize the utility and agree to act cooperatively so that a team optimum can be achieved. As a result, a feedback Nash equilibrium solution for each sensor is given based on the dynamic programming theory. Simulation results verify the effectiveness of the proposed approach, by comparing the grand coalition solutions with the non-cooperative solutions.

A fast distributed algorithm for coupled utility maximization problem with application for power control in wireless sensor networks

“…where µ = (µ l , l ∈ L) and λ r = (λ rj , j ∈ R) are lagrange multiplier vectors which are respectively associated with the inequality and equality constraints in model (7). From the above expression, the Lagrangian can be separated into many subproblems, where each subproblem uses only local variables, i.e., the rth subproblem uses only variables with the first subscript index r. The dual function is given by…”

Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach

“…Assuming that the energy transfer and information transmission can be done simultaneously. For the sensor nodes, we assume that the wireless energy and information transmission are operated at the same frequency, based on the “harvest-then-transmit” protocol [ 19 , 20 ], as shown in Figure 2 . Based on the system model, we will try to find out the optimized allocated power levels for the resource allocation problems in the propose system.…”

A Hybrid Energy Equating Game for Energy Management in the Internet of Underwater Things

“…Recently, Game Theory (GT) is emerging as an influential tool to solve and investigate the energy management, Cluster Head (CH) selection and routing decisions issues. GT is widely applied in IoUTs algorithms to predict the behavior of FNs and to manage the FNs in USN under critical situations [7,8]. A two-level classical and evolutionary GT is applied to improve the uneven energy consumption for route and CH selection [8].…”

“…GT is widely applied in IoUTs algorithms to predict the behavior of FNs and to manage the FNs in USN under critical situations [7,8]. A two-level classical and evolutionary GT is applied to improve the uneven energy consumption for route and CH selection [8]. These two-levels are wedge-level and node-level, which are used to balance the energy at CH and node-level, respectively.…”