A Mobile Adhoc Network (Manet) is a highly challenging environment due to its dynamic topology, limited processing capability, limited storage, band width constraints, high bit error rate and lack of central control. In this dynamic network, each node is considered as a mobile router. A malicious node can easily disrupt the proper functioning of the routing by refusing to forward routing message (misbehavior node), inject the wrong routing packets, modifying routing information, etc. Hence the design of secured routing algorithm is a major issue in Manets. Mobile Agent based algorithms also called Ant Routing algorithms are a class of swarm intelligence and try to map the solution capability of ant colonies for routing in Manets. In this paper we discuss prominent attacks and propose counter-measures in three Network Protocols and Algorithms ISSN 1943-3581 2011 www.macrothink.org/npa 109 Ant based routing protocols that are proactive, reactive and hybrid routing.

I. INTRODUCTIONIn 21st Century, Wireless Sensor Networks [1] has gained huge amount of attention of researchers across the world with regard to tons of theoretical, practical and implementation challenges. The research progress in Wireless Sensor Networks has explored diverse novel applications enabled by large-scale networks comprising of thousands of sensor nodes capable of sensing environmental information, processing it in efficient manner and transmitting the information back to base locations for further analysis. Wireless Sensor Networks comprise of large number of low-cost, low power and multi-functional capable wireless sensor nodes [2,3] for sensing different information's, equipped with wireless communication transmission mediums and computational capabilities. In sensor nodes, different types of sensors in form of mechanical, biological, chemical, optical can be attached to acquire real-time data from environment and transmit it back after processing. The sensor nodes forming Wireless Sensor Networks [4,5] have limitation in terms of low memory, less processing speeds. A radio is integrated on nodes to transmit the information back to sink node. The nodes are powered using battery to act as main power source. As the batteries are the only and primary power source, it becomes difficult to replace and charge the batteries once nodes are deployed which remains the foremost challenge in front of researchers to enhance the Energy Efficiency of sensor nodes. The characteristics of Wireless Sensor Network [1,6,3] are Deployment of sensor nodes; Less power; less computation capability; Limited memory; Unreliability. Therefore, the characteristics open wide range of challenges for the development and real-time implementations of wireless sensor network in the world. Sensor nodes once deployed in the real world are grouped into clusters, and each cluster has a node named as -Cluster Head‖. All the nodes in the cluster forward their sensed data to the cluster head and it is the duty of Cluster Head to route the information back to Sink Node (termed as -Base Station‖) via multi-hop wireless communication as shown in Fig. 1. With regard to various limitations cum shortcomings, Wireless Sensor Networks requires a collection of network protocols for implementing network control and other managerial functions in terms of synchronization, localization, security, routing, clustering etc. Various routing protocols proposed till date for routing the packets among nodes suffer from energy-constraints. Researchers [2,5] have combined various allied field like Neuro-computing, evolutionary computing, fuzzy logic computing, reinforcement learning and swarm intelligence based techniques in WSN. The ultimate goal is to improvise the Routing Efficiency in Wireless Sensor Networks. Researchers have successfully deployed Swarm Intelligence based techniques to address varied challenges existing in area of Wireless Sensor Networks and able to successfully deploy WSN in varied applications. The aim of this research paper is to expl...

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