Image scoring in ad-hoc networks : an investigation on realistic settings

Encouraging cooperation in distributed Multi-Agent Systems (MAS) remains an open problem. Emergent application domains such as Mobile Ad-hoc Networks (MANETs) are characterised by constraints including sparse connectivity and a lack of direct interaction history. Image scoring, a simple model of reputation proposed by Nowak and Sigmund, exhibits low space and time complexity and promotes cooperation through indirect reciprocity, in which an agent can expect cooperation in the future without repeat interactions with the same partners. The low overheads of image scoring make it a promising technique for ad-hoc networking domains. However, the original investigation of Nowak and Sigmund is limited in that it (i) used a simple idealised setting, (ii) did not consider the effects of incomplete information on the mechanism’s efficacy, and (iii) did not consider the impact of the network topology connecting agents. We address these limitations by investigating more realistic values for the number of interactions agents engage in, and show that incomplete information can cause significant errors in decision making. As the proportion of incorrect decisions rises, the efficacy of image scoring falls and selfishness becomes more dominant. We evaluate image scoring on three different connection topologies: (i) completely connected, which closely approximates Nowak and Sigmund’s original setup, (ii) random, with each pair of nodes connected with a constant probability, and (iii) scale-free, which is known to model a number of real world environments including MANETs

Investigating Open Issues in Swarm Intelligence for Mitigating Security Threats in MANET

The area of Mobile Adhoc Network (MANET) has being a demanded topic of research for more than a decade because of its attractive communication features associated with various issues. This paper primarily discusses on the security issues, which has been still unsolved after abundant research work. The paper basically stresses on the potential features of Swarm Intelligence (SI) and its associated techniques to mitigate the security issues. Majority of the previous researches based on SI has used Ant Colony Optimization (ACO) or Particle Swarm Optimization (PSO) extensively. Elaborated discussion on SI with respect to trust management, authentication, and attack models are made with support of some of the recent studies done in same area. The paper finally concludes by discussing the open issues and problem identification of the review

A power efficient method against misbehaving node in reputation system to prevent sybil attack in mobile ad-hoc network

Mobile ad-hoc network has become a very important field of study for students and researchers owing to its wide application. In mobile ad-hoc network all nodes are responsible for routing and forwarding of packets, hence all nodes are required to act selflessly for proper functioning of mobile ad-hoc network. The presence of selfish behavior in a node can degrade the performance of the mobile ad hoc network to a large extent. Several works have been done for identification and punishment of the misbehaving nodes in mobile ad hoc network. We propose here a method where some selected neighbors are participated in detecting misbehaving nodes in power effective manners. These neighbors participating in selfish node detection are chosen randomly. It also alerts all other nodes about the misbehaving links in the network. The simulation studies show that this does the job efficiently with less power consumption in the network. The power effectiveness of the algorithm also reduces the number of misbehaving nodes because many nodes show misbehavior to save their power