B. Nancharaiah

Bio: B. Nancharaiah is an academic researcher. The author has contributed to research in topics: Destination-Sequenced Distance Vector routing & Wireless Routing Protocol. The author has an hindex of 5, co-authored 5 publications receiving 73 citations.

Hybrid optimization using Ant Colony Optimization and Cuckoo Search in MANET routing

Abstract: Ad hoc networks are a group of mobile nodes with wireless communication adapters which dynamically forms a temporary network. These networks do not require any existing infrastructure or network support. The lack of a central infrastructure imposes challenges, as services ensured by central infrastructure must be ensured by mobile nodes in the new environment. The network operation is decentralized as the network's organization/message delivery is effected by nodes. A Mobile Ad hoc Network (MANET) is functional in either a stand-alone manner or connected to large networks. In this work, a hybrid optimization technique using Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for the optimization of MANET routing. Ad hoc On-demand Distance Vector Routing (AODV) protocol is enhanced using the proposed optimization algorithm. The proposed hybrid algorithm achieves improved performance in terms of average end-to-end delay.

MANET link performance using Ant Colony Optimization and Particle Swarm Optimization algorithms

Abstract: End-to-end delay and Communication cost are the most important metrics in MANET (Mobile Adhoc Network) routing from source to destination. Recent approaches in Swarm intelligence (SI) technique, a local interaction of many simple agents to meet a global goal, prove that it has more impact on routing in MANETs. Ant Colony Optimization (ACO) algorithm uses mobile agents as ants to discover feasible and best path in a network. ACO helps in finding the paths between two nodes in a network and acts as an input to the Particle Swarm Optimization (PSO) technique, a metaheuristic approach in SI. PSO finds the best solution over the particle's position and velocity with the objective of cost and minimum End-to-end delay. This hybrid algorithm exhibits better performances when compared to ACO approach.

Modified Ant Colony Optimization to Enhance MANET Routing in Adhoc on Demand Distance Vector

Abstract: an adhoc network without any centralized structures. Networking infrastructure such as the base stations is unavailable for MANET. The interconnected collection of wireless nodes depends on its position and, its transmitting and receiving radio capacity to create a wireless connectivity and form a multi-hop graph or an "adhoc" network. Routing in MANET is used for finding the multi-hop paths to transmit the data flow through the network while optimizing one or more performance measures. In this paper, modified Ant Colony Optimization (ACO) has been proposed for the optimization for MANET routing protocol. The modifications have been incorporated in Adhoc On-demand Distance Vector (AODy) routing. The proposed modified Ant Colony Optimization algorithm performs better compared to existing algorithms(AODV and Cooperative Opportunistic Routing in Mobile Adhoc Networks i.e. CORMAN) in terms of end-to-end delay, route acquisition time, throughput, total cache replies and packet delivery ratio.

Routing in mobile adhoc networks (manets) using fuzzy aided ant colony optimization (aco) technique

Abstract: In this work, a routing algorithm suitable for Mobile Adhoc Networks is proposed. MANETS are unstable nature when network mobility increases. Path selection process is a critical task in routing algorithms. The proposed work addresses this problem by employing Ant Colony Optimization and Fuzzy logic technques while developing the routing algorithm. The path information by ants will be given to FIS(Fuzzy Interference system ) in order to compute the available path’s score values, based on this score value from the FIS system the optimal paths will be selected. Hence, the routing problem can be solved more effectively by achieving high successful path delivery rate rather than the conventional routing algorithms. This technique is implemented and the results are compared to the existing algorithms. The performance of the proposed algorithm is assessed using distance, power consumption etc..,

A Survey of Shortest-Path Algorithms.

Abstract: A shortest-path algorithm finds a path containing the minimal cost between two vertices in a graph. A plethora of shortest-path algorithms is studied in the literature that span across multiple disciplines. This paper presents a survey of shortest-path algorithms based on a taxonomy that is introduced in the paper. One dimension of this taxonomy is the various flavors of the shortest-path problem. There is no one general algorithm that is capable of solving all variants of the shortest-path problem due to the space and time complexities associated with each algorithm. Other important dimensions of the taxonomy include whether the shortest-path algorithm operates over a static or a dynamic graph, whether the shortest-path algorithm produces exact or approximate answers, and whether the objective of the shortest-path algorithm is to achieve time-dependence or is to only be goal directed. This survey studies and classifies shortest-path algorithms according to the proposed taxonomy. The survey also presents the challenges and proposed solutions associated with each category in the taxonomy.

CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems

Abstract: Combinatorial optimization problems are typically NP-hard, due to their intrinsic complexity. In this paper, we propose a novel chaotic particle swarm optimization algorithm (CS-PSO), which combines the chaos search method with the particle swarm optimization algorithm (PSO) for solving combinatorial optimization problems. In particular, in the initialization phase, the priori knowledge of the combination optimization problem is used to optimize the initial particles. According to the properties of the combination optimization problem, suitable classification algorithms are implemented to group similar items into categories, thus reducing the number of combinations. This enables a more efficient enumeration of all combination schemes and optimize the overall approach. On the other hand, in the chaos perturbing phase, a brand-new set of rules is presented to perturb the velocities and positions of particles to satisfy the ideal global search capability and adaptability, effectively avoiding the premature convergence problem found frequently in traditional PSO algorithm. In the above two stages, we control the number of selected items in each category to ensure the diversity of the final combination scheme. The fitness function of CS-PSO introduces the concept of the personalized constraints and general constrains to get a personalized interface, which is used to solve a personalized combination optimization problem. As part of our evaluation, we define a personalized dietary recommendation system, called Friend, where CS-PSO is applied to address a healthy diet combination optimization problem. Based on Friend, we implemented a series of experiments to test the performance of CS-PSO. The experimental results show that, compared with the typical HLR-PSO, CS-PSO can recommend dietary schemes more efficiently, while obtaining the global optimum with fewer iterations, and have the better global ergodicity.

Fuzzy based energy efficient multicast routing for ad-hoc network

Abstract: Ad-hoc network is an infrastructure less wireless network. Since ad-hoc networks are self-organizing, rapidly deployable wireless networks, they are highly suitable for various applications. Every nodes of ad-hoc network are connected dynamically in an arbitrary manner. There is no default router available in this network because all nodes of this network behave as routers and take part in discovery and maintenance of routes to other nodes. Ad-hoc nodes are powered by batteries with limited capacity due to its distributed nature. Therefore energy consumption occurs due to sending a packet, receiving a packet and to select next hop node. Hence, the present paper proposes a routing protocol, named Fuzzy Based Energy Efficient Multicast Routing (FBEEMR) for ad-hoc network. The basic idea of FBEEMR is to select the best path which reduces energy consumption of ad-hoc nodes based on fuzzy logic. This protocol is mainly used to extend the lifetime of ad-hoc network with respect to energy efficient multicast routing by calculating route lifetime values for each route. Based on the comprehensive simulation of FBEEMR using MATLAB and NS2 and comparative study of same with other existing protocols, it is observed that proposed routing protocol contributes to the performance improvements in terms of energy efficiency.