Enhancing Node Cooperation in Mobile Wireless Ad Hoc Networks with Selfish Nodes

In Mobile Ad Hoc Networks (MANETs), nodes depend on each other for routing and forwarding packets. However, to save power and other resources, nodes belonging to independent authorities may behave selfishly, and may not be willing to help other nodes. Such selfish behavior poses a real threat to the proper functioning of MANETs. One way to foster node cooperation is to introduce punishment for selfish nodes. Based on neighbor-monitoring techniques, a fully distributed solution to detect, punish, and re-admit selfish nodes, is proposed here. This solution provides nodes the same opportunity to serve/and be served by others. A light-weight solution regarding battery status is also proposed here. This solution requires neighbor monitoring only when necessary, thereby saving nodes battery power. Another effective way to solve the selfish-node problem is to reward nodes for their service according to their cost. To force nodes to show their true cost, truthful protocols are needed. A low overhead truthful routing protocol to find optimal routes is proposed in this thesis. The most prominent feature of this protocol is the reduction of overhead from existing solutions O(n3) to O(n2). A light-weight scalable truthful routing protocol (LSTOP) is further proposed, which finds near-least-cost paths in dense networks. LSTOP reduces overhead to O(n) on average, and O(n2) in worst case scenarios. Multiple path routing protocols are an effective alternative to single path routing protocols. A generic mechanism that can turn any table-driven multipath routing protocol into a truthful one, is outlined here. A truthful multipath routing protocol (TMRP), based on well-known AOMDV protocol, is presented as an example. TMRP incurs an only 2n message overhead for a route discovery, and can also achieve load balancing without compromising truthfulness. To cope with the selfish-node problem in the area of position-based routing, a truthful geographic forwarding (TGF) algorithm is presented. TGF utilizes three auction-based forwarding schemes to stimulate node cooperation. The truthfulness of these schemes is proven, and their performance is evaluated through statistical analysis and simulation studies

Observation-based Cooperation Enforcement in Ad Hoc Networks

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

A NOVEL METHODOLOGY TO OVERCOME ROUTING MISBEHAVIOR IN MANET USING RETALIATION MODEL

ABSTRACT MANET is a cooperative network in which nodes are responsible for forwarding as well as routing. Noncooperation is still a big challenge that certainly degrades the performance and reliability of a MANET. This paper presents a novel methodology to overcome routing misbehavior in MANET using Retaliation Model. In this model node misbehavior is watched and an equivalent misbehavior is given in return. This model employs several parameters such as number of packets forwarded, number of packets received for forwarding, packet forwarding ratio etc. to calculate Grade and Bonus Points. The Grade is used to isolate selfish nodes from the routing paths and the Bonus Points defines the number of packets dropped by an honest node in retaliation over its misconducts. The implementation is done in "GloMoSi

OPTIMUM POWER MANAGEMENT IN MOBILE AD-HOC NETWORKS

Mobile Ad hoc Network is an interconnection of mobile nodes, with no fixed infrastructure. Optimum management of power is very important in MANET as all its nodes are battery operated. If a node fails to forward the data packets from other nodes and just utilize the network to send its own data, the network will definitely face connectivity issues. The nodes which behave in such a selfish manner are termed as selfish nodes. Many research works have found ways for eliminating the selfish nodes. But elimination of nodes will reduce the connectivity and lifetime of the network. In this paper instead of eliminating, the selfish nodes we have tried to eliminate the selfish behaviour by maintaining the sanctity of every node involved in the MANET formation. This will help in improving the connectivity and lifetime by reducing selfish behaviour in individual nodes

Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks

This book presents collective works published in the recent Special Issue (SI) entitled "Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks”. These works expose the readership to the latest solutions and techniques for MANETs and VANETs. They cover interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The book demonstrates pioneering work in these fields, investigates novel solutions and methods, and discusses future trends in these field