An integrated game theoretical approach to detect misbehaving nodes in MANETs

Mobile Ad-hoc Networks are dynamic in nature and do not have fixed infrastructure to control nodes in the networks. The challenge lies ahead in coordinating among such dynamically moving nodes. Over a due course, nodes might become selfish and may refrain from packet forwarding because of the heavy dynamism. This selfish behavior might also result as an impact of malicious nodes in the neighborhood. Therefore, trust and reputation based mechanisms are required to keep the mobile neighbors intact. Assigning or altering the trust highly depends on the node behavior. Game theoretical approaches are more suitable in deciding upon the reward mechanisms for which the mobile nodes operate upon. Rewards or penalties have to be decided by ensuring a clean and healthy MANET environment. Not every time the same reward schemes are to be followed since this might result in malicious nodes attacking the rewarding scheme as well. Therefore, a non-routine yet surprise alterations are well required in place in deciding suitable and safe reward strategies. This work focuses on integrating a misbehavior node detection scheme and an incentive based reputation scheme with game theoretical approach called Supervisory Game to analyze the selfish behavior of nodes in the MANETs environment. The proposed work significantly reduces the cost of detecting misbehavior nodes in the network.