Abstract: In networking, routing is very important in day today life. The hybrid wireless network that combine combines a mobile ad-hoc network and an infrastructure network. An efficient and reliable data routing is important for high throughput. Existing routing schemes have the drawback of both infrastructure routing and mobile ad-hoc routing which include high overhead in route discovery and maintenance and the congestion. Although current reputation systems help increase routing reliability, but they are not sufficiently effective and efficient because they rely on local information exchanges between nodes to evaluate node reputation. In this paper co-ordinately develop an efficient routing algorithm and effective approach for reliable routing. To handle this challenge, here presents a peer-to-peer (P2P)-based Market-guided Distributed Routing mechanism (MDR). It takes advantage of wide base stations to analyze efficient data routing, and effective reputation management and trading market management for reliable data routing. The packets from a source node are transmitted to nearby base stations directly or indirectly, and then they are transmitted to the near destination base station then to the destination. The base stations form a P2P structure for the collection of reputation and querying to avoid local information exchanges between the nodes. Also P2P structure used for managing the service transactions between nodes for that it uses the trading market. By using the single-relay transmission feature, base stations can monitor the actual transmitted packets of relay nodes to more accurately and efficiently. Then evaluate their reputations and execute trading market management. Also detect falsely reported reputation information. Then further propose market-based policies to strengthen cooperation incentives for high throughput. Simulation results show that MDR outperforms the traditional hybrid routing schemes and reputation systems in achieving high throughput.
Keywords: Hybrid wireless networks, routing, reputation systems, trading market model.