470 research outputs found

    Observation-based Cooperation Enforcement in Ad Hoc Networks

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    Ad hoc networks rely on the cooperation of the nodes participating in the network to forward packets for each other. A node may decide not to cooperate to save its resources while still using the network to relay its traffic. If too many nodes exhibit this behavior, network performance degrades and cooperating nodes may find themselves unfairly loaded. Most previous efforts to counter this behavior have relied on further cooperation between nodes to exchange reputation information about other nodes. If a node observes another node not participating correctly, it reports this observation to other nodes who then take action to avoid being affected and potentially punish the bad node by refusing to forward its traffic. Unfortunately, such second-hand reputation information is subject to false accusations and requires maintaining trust relationships with other nodes. The objective of OCEAN is to avoid this trust-management machinery and see how far we can get simply by using direct first-hand observations of other nodes' behavior. We find that, in many scenarios, OCEAN can do as well as, or even better than, schemes requiring second-hand reputation exchanges. This encouraging result could possibly help obviate solutions requiring trust-management for some contexts.Comment: 10 pages, 7 figure

    Identification of Malicious Node for Effective Top-k Query Processing in MANETS

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    In Mobile Ad-hoc networks, query processing is optimized using Top-k query processing. The accuracy of the results can be lowered if there exists malicious node. In our proposed system, we assume that malicious node perform Data Replacement Attack, in which the malicious node replaces necessary data sets with the false data sets. In our system malicious node identification method, the query issuing node receives the reply messages from the nodes; if a query-issuing node detects a DRA then it performs subsequent inquiries with the nodes which receive the information from the malicious node. In this way the query issuing node identifies the malicious node, and shares the information with the neighbouring nodes. Then the nodes share the information regarding the malicious node with the other nodes which are far away. Each node tends to identify the malicious node in the network, and then floods the information. Query issuing node performs grouping of the nodes based on the similarity of the information on malicious node detected by the nodes. Identification of malicious node is performed based on the results of malicious node identifications by these groups

    Performance Analysis of the CONFIDANT Protocol (Cooperation Of Nodes - Fairness In Dynamic Ad-hoc NeTworks)

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    Mobile ad-hoc networking works properly only if the par- ticipating nodes cooperate in routing and forwarding. How- ever, it may be advantageous for individual nodes not to cooperate. We propose a protocol, called CONFIDANT, for making misbehavior unattractiv

    Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks

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    A mobile ad hoc network (MANET) is a group of nodes constituting a network of mobile nodes without predefined and pre-established architecture where mobile nodes can communicate without any dedicated access points or base stations. In MANETs, a node may act as a host as well as a router. Nodes in the network can send and receive packets through intermediate nodes. However, the existence of malicious and selfish nodes in MANETs severely degrades network performance. The identification of such nodes in the network and their isolation from the network is a challenging problem. Therefore, in this paper, a simple reputation-based scheme is proposed which uses the consumption and contribution information for selfish node detection and cooperation enforcement. Nodes failing to cooperate are detached from the network to save resources of other nodes with good reputation. The simulation results show that our proposed scheme outperforms the benchmark scheme in terms of NRL (normalized routing load), PDF (packet delivery fraction), and packet drop in the presence of malicious and selfish attacks. Furthermore, our scheme identifies the selfish nodes quickly and accurately as compared to the benchmark scheme
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