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IMPLEMENTATION OF TRUST MODELING SCHEME FOR ARTIFICIAL INTELLIGENCE IN COGNITIVE RADIO NETWORKS G.PONMANI[1] S.RAMANI[2] ABSTRACT INTRODUCTION A basic problem facing the future in wireless systems is where to find suitable spectrum bands to fulfill the demand of future services. To overcome this problem and improve the spectrum utilization cognitive radio concept has been evolved. Wireless communication, in which a transmitter and receiver can detect intelligently communication channels that are in use and those which are not in use are known as Cognitive Radio and it can move to unused channels. In this Project using recent advances in uncertain reasoning originated from artificial intelligence community, to propose a unified trust management scheme that enhances the security in Cognitive Radio. The trust model has two components such as Direct and Indirect Observation. Combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in Cognitive radio. Cognitive radio scenario positively supports the effectiveness and performance, which improves throughput and packet delivery ratio considerably, with slightly increased average end-to-end delay and overhead of messages. Based on the proposed scheme, more accurate trust can be obtained by considering different types of packet such as data packets and control packets. G.Ponmani.,M.E(Communication Systems) Varuvan Vadivelan Institute of Technology Email id: [email protected] Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their environment. Much research is currently underway developing various reasoning and learning algorithms that allow cognitive radios to operate optimally in a large variety of different situations. However, as with many new technologies, initial research has not focused on security aspects of cognitive radio. Typically security is always “bolted on” after the fact by adding some sort of link authentication and encryption. This typically works well for data traversing a wireless network, but not necessarily for things fundamental to the operation of the wireless link itself. Since cognitive radios can adapt to their environment and change how they communicate, it’s crucial that they select optimal, secure means of communications. Data integrity and confidentiality can be handled by higher-layer cryptographic security, so here to focus on attacks fundamental to the cognitive radio itself, and independent of its higher-layer communications techniques. By putting artificial intelligence (AI) engines in charge of our wireless devices, need to be aware that these engines can be provided false sensory input by adversaries, and this false input affects its beliefs and behaviour. WIRELESS AD HOC NETWORKS Wireless Ad Hoc Network is a collection of two or more devices or nodes or terminals with wireless Communications and networking capability that communicate with each other without the aid of any centralized administrator also the wireless nodes that can dynamically form a network to exchange information without using any existing fixed network infrastructure. And it’s an autonomous system in which mobile hosts connected by wireless links are free to be dynamically and some time act as routers at the same time. All nodes in a wireless ad hoc network act as a router and host as well as the network topology is in dynamically, because the connectivity between the nodes may vary with time due to some of the node departures and new node arrivals. Nodes mover randomly in different direction and different speeds. In the past few years, the people became realized to use all the technology so widely and the people’s future living environments are emerging, based on information resource provided by the connections of different communication networks for clients also we have seen a rapid expansion in the field of Mobile Computing because the proliferation not expensive, widely available wireless devices . A new small devices such as personal communication like cell phones, laptops, Personal Digital Assistants (PDAs),handhelds, and also there’s a lot of traditional home appliances such as a digital cameras, cooking ovens, washing machines, refrigerators and thermostats, with computing and communicating powers attached. Mobile Ad Hoc Protocols For the Ad Hoc network there are more than 13 kinds of the above routing protocol have been proposed, following the more representative for several separate presentation, and to compare between them, and for more dilates about existing ad hoc network protocols. Destination-Sequence Distance-Vector Routing (DSDV) Destination-Sequenced Distance-Vector Routing is based on traditional Bellman-Ford routing algorithms were developed by the improvement, and a routing table-based protocol. Each node in an operation must be stored a routing table, which records all the possible links with the nodes in the node and the distance like the number of hops, routing table within each record also contains a sequence number, which is used to determine are there any more old path in order to avoid routing table generation. DSDV is basically on the Internet Distance-Vector Routing the same, but more destination sequence number of the record, makes the Distance-Vector Routing more in line with this dynamic network MANET needs, In addition, when network topology changes are less frequent when the routing table does not need to exchange all the information, DSDV, within each node, together with a table, is used to record the routing table changes from the last part of the exchange so far, if you change a lot of the conduct of all the information The exchange, known as the full dump packets, if the change very little, it is only for the part of the exchange, known as the incremental packet. In this protocol, each node in an operation must be stored a routing table, which records all the possible links with the nodes. RELATED WORK EXISTING SYSTEM The design of network protocols for these networks is a complex issue. Regardless of the application, MANETs need efficient distributed algorithms to determine network organization, link scheduling, and routing. However, determining viable routing paths and delivering messages in a decentralized environment where network topology fluctuates is not a well-defined problem. While the shortest path (based on a given cost function) from a source to a destination in a static network is usually the optimal route, this idea is not easily extended to MANETs. Factors such as variable wireless link quality, propagation path loss, fading, multiuser interference, power expended, and topological changes, become relevant issues. The network should be able to adaptively alter the routing paths to alleviate any of these effects. Moreover, in a military environment, preservation of security, latency, reliability, intentional jamming, and recovery from failure are significant concerns. Military networks are designed to maintain a low probability of intercept and/or a low probability of detection. Hence, nodes prefer to radiate as little power as necessary and transmit as infrequently as possible, thus decreasing the probability of detection or interception. A lapse in any of these requirements may degrade the performance and dependability of the network. The results demonstrate that the proposed scheme has a lower routing load because of the higher number of packets received correctly by the destination node. As the number of nodes increases, the routing load of the existing and proposed schemes climb up due to the nature of proactive routing protocol: periodical generation of control messages in every node. PROPOSED SYSTEM A CR "monitors its own performance continuously", in addition to "reading the radio's outputs"; it then uses this information to "determine the RF environment, channel conditions, link performance, etc.", and adjusts the "radio's settings to deliver the required quality of service subject to an appropriate combination of user requirements, operational limitations, and regulatory constraints". Some "smart radio" proposals combine wireless mesh network dynamically changing the path messages take between two given nodes using cooperative diversity; cognitive radio dynamically changing the frequency band used by messages between two consecutive nodes on the path; and softwaredefined radio dynamically changing the protocol used by message between two consecutive nodes. In this proposed system an uncertain reasoning theory from artificial intelligence to evaluate the trust of nodes in cognitive networks. Unified trust management using Bayesian Interference and Dempster-Shafer theory combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in cognitive networks and then evaluate our scheme under the scenario of cognitive networks. Extensive simulation results show the effectiveness of the proposed scheme. These components are similar to those used in direction observation trust, an observer estimates the trust of his one-hop neighbor based on its own opinion. Cognitive Radio (CR), which provides the capability to harness the potential of unused/underutilized spectrum (spectrum holes) in an opportunistic manner, is a key enabling technology for dynamic spectrum access. An illustration of the cognitive radio technology is presented which it is easy to observe that CR can significantly improve the overall spectrum utilization when the CR users are allowed to utilize the spectrum holes. A cognitive radio network typically involves two types of users: primary users (PUs), who are incumbent licensed users of the spectrum, and CR users (also known as secondary users), who try to opportunistically access the unused licensed spectrum as long as the harmful interference to primary users is limited. According to Merriam Webster’s Dictionary, trust is defined as ”assured reliance on the character, ability, strength, or truth of someone or something.” Despite the subjective nature of trust, the concept of trust has been very attractive to network security protocol designers because of its diverse applicability as a decision making mechanism. 5.2.1 Trust in sociology Gambetta describes the nature of trust as subjectivity, an indicator for future actions, and dynamicity based on continuous interactions between two entities. Luhmann also emphasized the importance of trust in society as a mechanism for building cooperation among people to extend human interactions for future collaboration. Cognitive Radio Functions In an introduction of reconfigurable logic and the coining of the term software defined radio (SDR), the dominant implementation architecture used for RF Front-Ends (FEs) was the superheterodyne architecture. The following graphic shows how a cognitive radio Network operates in relation to its environment: TRUST MODELING 5.2.2 Trust in economics In economics, trust is represented as an expectation that applies to situations in which trustors take risky actions under uncertainty or information incompleteness. FRAMEWORK CONCLUSION AND FUTURE WORK This paper uncertain reasoning, Bayesian inference and Dempster-Shafer theory, the trust values of observed nodes in MANETs is derived.. The results of MANET routing scenario which improves throughput and packet delivery ratio considerably, with slightly increased average end-to-end delay and overhead of messages. Although some excellent work has been done on detectionbased approaches based on trust in MANETs, most of existing approaches do not exploit direct and indirect observation at the same time to evaluate the trust of an observed node. Moreover, indirect observation in most approaches is only used to assess the reliability of nodes, which are not in the range of the observer node. Therefore, inaccurate trust values may be derived and also most methods of trust evaluation from direct observation do not differentiate data packets and control packets in MANETs. The cognitive radio networks are developed to solve the current existing problems in the wireless communications. In this also to derive the trust value of nodes by using a unified trust management scheme to improve the security in Cognitive Radio. Using recent advances in uncertain reasoning, Bayesian inference and Dempster-Shafer theory, to evaluate the trust values of observed nodes in Cognitive radio. The results of Cognitive radio positively support the effectiveness and performance of our scheme, which improves throughput and packet delivery ratio. Abundant simulation results have been conducted and validated that the proposed scheme outperforms the existing ones under the impact of different attack patterns and different number of malicious nodes. REFERENCES [1] Akbar I.A and TranteW.H, (2013) “Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case,” in Proceedings of IEEE Southeastcon, 2007, pp. 196–201. [2] Bellman R.E (2011), “Can Artificial Intelligence meet the Cognitive Networking Challenge?” (San Francisco, CA: Boyd & Fraser Publishing Company). [3] Chapin J and Chan V.W(2011), “The next 10 years of DoD wireless networking research,” in Proc. IEEE Milcom’11, (Baltimore, MD, USA). [4] Charles Clancy T, Nathan Goergen (2012), “Security In Cognitive Radio Networks: [5] Threats and Mitigation,” Chen. Z, Guo. N, Z. Hu, and Qiu R (2012), “Channel state prediction in cognitive radio, part ii: Single-user prediction,” in Proceedings of IEEE Southeastcon, pp. 50 –54. [6] Domenico A.D(2010), “A survey on MAC strategies for cognitive radio networks,” IEEE Communication Surveys & Tutorials, June 2010. [7] Guan Q, Yu F.R, Jiang S, and Leung V(2012), “Joint topology control and authentication design in mobile ad hoc networks with cooperative communications,” IEEE Trans. Veh. Tech., vol. 61, pp. 2674 –2685. G.Ponmani .,II year M.E (Communication Systems) Varuvan Vadivelan Institute of Technology Dharmapuri-636703