A Wireless Sensor Network is a self-configuring set of connections of tiny sensor nodes communicate in the middle of themselves using radio signals, and deployed in measure to sense, observe and identify with the physical world.WSN provide a bridge between the real physical and virtual worlds. Allow the ability to observe the previously unobservable at a fine resolution over large spatiotemporal scales. A join that execute different than typical behavior (drop packets, scare routing system and save their assets by not ahead the other node packets) is identified as selfish node. The multiservice delivery between the source-destination pairs in distributed selfish wireless networks (SeWN), where selfish relay nodes (RN) expose their selfish behaviors. Research focus evaluating the trust of a node group and excluding selfish nodes for improving the network performance. In the network connectivity of selfish wireless networks (SeWNs) constituted by selfish nodes (SeNs). Source transfer the multi-service delivery to destination through Relay Node (RN). At the time of transfer, the selfish relay nodes expose their selfish behavior by doing dropping multiservice. In this environment, the network need to establish the connection between source and destination, for that source need to find the optimal path. Concept of Node selfishness management is constructed to manage the RN’sto manage the RN’s node-selfishness information (NSI). It includes the degree of node-selfishness (DeNS), the degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES). DeNs determines in terms of RN’s historical behaviors, DeIS defines in terms of its available resources and finally DeES described by means of the employed incentive mechanism and the quality-of-service (QoS) requirements. Over the spread node-selfishness administration, a path collection criterion is considered to select the most reliable and through path in terms of RNs’ DeISs precious by their accessible resources, and the optimal incentive are determined by the source to motivate forwarding multiservice of the RNs in the selected path. Simulation results show that this future model effectively manages the RNs’ NSI, and the most select path selection and the optimal incentives are determined
