! "# $ 28 ! " # ! " #$$ % $ " #$$ & To make routing decisions based on more than one check, buffer residency, node energy and hop count and to provide an efficient routing method for wireless mesh networks, a fuzzy based oblivious routing is proposed in this paper. Simulation results in ns2 verify that they perform better than multiple restriction routing. The AP need not be in the reach of all the nodes in the network. Nodes around the AP forward the packets from the neighbor nodes to the AP. If there are an important number of nodes in the network, neighbor nodes can transfer data with the AP in a few hop. In this wireless mesh network to used for oblivious routing fuzzy logic to perform high level data to reach the destination, any traffic occurred in this network it’s to be cleared and data to be send on efficiently on their network Wireless Mesh Network, Fuzzy Logic, AODV, Access Point, Constant Bit Rate, Oblivious Routing, Wireless mesh networks are becoming a promising communication technology for broadband wireless access by providing wireless backbone to mobile devices over multihop wireless communication systems. This emerging technology has a great skill that it can be accessed anywhere and anytime. A number of wireless networking and communication technology is used everywhere, wireless services and applications are widely used on the network [1]. Wireless Mesh Networks (WMNs) are dynamically self organized and selfconfigured, with the nodes in the network automatically establishing an ad hoc network and maintaining the mesh connectivity [2]. The inbuilt characteristics of WMN enable the nodes to find out automatically and route dynamically. The fundamental features of WMN are large in capacity, wide area coverage and high transmission speed. The drawbacks found in WMN include the frequent topological variations, unbalanced link and multiple external interferences on the network. And also it causes packet loss, connectivity problem and slow response of the source to destination on their network [2]. AODV is another routing algorithm used in ad hoc networks, it does not use source routing, but it is ondemand [2]. In AODV, each node maintains a routing table which is used to store destination and next hop IP addresses as well as destination sequence numbers [13]. Each entry in the directionfinding table has a destination talk to, next hop, procedural nodes list, lifetime, and distance to destination. We defined a console as the set of sensors that will be required to route high priority packets from the data sources to the sink. Our solution does not require active queue organization, maintenance of multiple queues or preparation algorithms, or the use of specialized MAC protocols of the network. Our wide simulations show that as compared to AODV, CAR increase the fraction of high priority data delivery, decreases delay and jitter for such delivery while using energy uniformly in the deployment [5]. By discovering the required canzone and using differentiate routing we can free the canzone from most of the low priority traffic traveling through the network. This will help nodes on the canzone to provide better service to high priority data. The concepts of AODV that make it desirable f o r MANETs with limited bandwidth include the following: Minimal space complexity. The algorithm makes sure that the nodes that are not in the active path do not maintain information about this route. After a node receives the RREQ and sets a reverse path in its routing table and propagates the RREQ to its neighbors, if it does not receive any RREP from its neighbors for this request, it deletes the routing info that it has recorded [4]. It is simple with each node behaving as a router, maintaining a simple routing table, and the source node initiating path discovery request, making the network selfstarting. Mos t effective routing info, after working in a n e t w o r k , node should be find and receives the i n t e r n a l with smaller network, it updates its directionfinding info with this superior path solution and propagation o n their wireless mesh network topology. When a node S needs a route to some destination D, it broadcasts a route request message to its neighbors, including the last known sequence number for that destination. The route request is busy in a controlled manner through the network awaiting it reaches a node that has a route to the destination. Each node that forwards the route request creates a reverse route for itself back to node S. When the request route reaches a node with a route to D that node generates a request reply that contains the number of hops necessary to reach D and the sequence number for D most recently seen by the node generating the reply [2]. Each node that participates in forwarding this back toward the originator of the request route (node S) creates a forward route to D. Fuzzy Logic introduced by Zadeh [12] allows a computer to model the same way that people do, not always precise. People think and reason using their terms such as “hot” and “fast”, rather than in exact numerical terms 90 degrees and 200 km/hours respectively. The fuzzy set theory models the understanding of rough and incomplete sensory information as clear by human brain. It’s represents and numerically manipulates such uniform information in a natural way via membership functions and fuzzy rules. Some advantages of fuzzy logic are conceptually easy to understand, flexible, and tolerant of free data. It can model nonlinear functions of difficulty, and also can be built on top of the experience of