Joint Beacon Power and Beacon Rate Control Based on Game Theoretic Approach in Vehicular Ad Hoc Networks

In vehicular ad hoc networks (VANETs), each vehicle broadcasts its information periodically in its beacons to create awareness for surrounding vehicles aware of their presence. But, the wireless channel is congested by the increase beacons number, packet collision lost a lot of beacons. This paper tackles the problem of joint beaconing power and a beaconing rate in VANETs. A joint utilitybased beacon power and beacon rate game are formulated as a non-cooperative game and a cooperative game. A three distributed and iterative algorithm (Nash Seeking Algorithm, Best Response Algorithm, Cooperative Bargaining Algorithm) for computing the desired equilibrium is introduced, where the optimal values of each vehicle beaconing power and beaconing rate are simultaneously updated at the same step. Extensive simulations show the convergence of a proposed algorithm to the equilibrium and give some insights on how the game parameters may vary the game outcome. It is demonstrated that the Cooperative Bargaining Algorithm is a fast algorithm that converges the equilibrium.

Social-Aware Peer Discovery for Energy Harvesting-Based Device-To-Device Communications

In Device-to-Device (D2D) communications, the first step is to find all of the neighboring peers in the network by performing a peer discovery process. Most previous studies use the social behaviors of the users to adjust the sending rates of the peer discovery messages (i.e., beacons) under the constraint of consumed power for increasing the Peer Discovery Ratio (PDR). However, these studies do not consider the potential for energy harvesting, which allows for the User Equipments (UEs) to procure additional power within charging areas. Accordingly, this paper proposes an Energy-Ratio Rate Decision (ERRD) algorithm that comprises three steps, namely Social Ratio Allocation (SRA), Energy Ratio Allocation (ERA), and Beacon Rate Decision (BRD). The SRA step determines the allocated power quantum for each UE from the total budget power based on the social behavior of the UE. The ERA step then adjusts this allocated power quantum in accordance with the power that is harvested by the UE. Finally, the BRD step computes the beacon rate for the UE based on the adjusted power quantum. The simulation results show that ERRD outperforms the previously-reported Social-Based Grouping (SBG) algorithm by 190% on the PDR for a budget power of one watt and 8% for a budget power of 20 watts.