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Sivajothi, M.
- Multipath Ant Routing Algorithm in Mobile Ad Hoc Networks
Authors
1 Department of Computer Science, Sri Parasakthi College for Women, Courtallam, IN
Source
Wireless Communication, Vol 6, No 1 (2014), Pagination: 31-36Abstract
Mobile Ad hoc Networks (MANETs) are networks that consist entirely of mobile nodes, placed together in an ad hoc manner. All nodes have routing capabilities, and forward data packets for other nodes. Nodes can enter or leave the network at any time, and may be mobile, so that the network topology changes frequently. There are lots of challenges in these networks which makes routing task hard. These challenges arise from the dynamic and unplanned nature of these networks, from the unreliability of wireless communication, from the limited resources available in terms of bandwidth, processing capacity, etc., and from the possibly large scale of these networks. Due to these different challenges, new algorithms are needed at all layers of the network protocol stack.
We try to cover these challenges by using ideas from artificial intelligence namely Ant Colony Optimization (ACO) and Multipath Routing. ACO algorithms tend to provide properties such as adaptivity and robustness, which are needed to deal with the challenges present in MANETs. Multipath routing provides multiple stable paths for routing data sets, which bring efficiency for MANETs.
So we present Multipath Ant Routing (MAR) algorithm which combines ideas from ACO routing and Multipath routing. The algorithm has hybrid architecture, combining both reactive and proactive mechanisms. In simulation results, we show that for a wide range of different environments and performance metrics, MAR can outperform.
Keywords
Ad Hoc Networks, Ant Colony Optimization, MANETs, Routing Protocol.
- An Efficient Multiple Ant Colony Based Routing Protocol to Support Multimedia Communication in Ad Hoc Wireless Network
Authors
1 Departmaent of Computer Science in Sri Parasakthi College for Women, IN
2 School of Computer Science and Head of the Department of Computer Science and Engineering, Alagappa University, IN
Source
Wireless Communication, Vol 2, No 11 (2010), Pagination: 474-477Abstract
The major problem in Ad hoc Networks is to find a route between the communication end points. The topology of the network changes constantly due to the mobility of the nodes and paths which were initially competent and can quickly become incompetent or even infeasible. Ant Colony Optimization (ACO) is a biological inspiration simulating the ability of real ant colony of finding the shortest path between the nest and food source. Ant colony algorithms are motivated by the observation of real ant colonies. ACO is one of the successful applications of Swarm Intelligence (SI) which is the field of Artificial Intelligence (AI) that studies the intelligent behavior of ants. In this paper, Multiple Ant Colony Optimization based routing protocol is used to support multimedia communication in Ad Hoc Network. This approach avoids the stagnation problem on ants. This approach increases effectiveness and adaptiveness. Moreover, tabu search is used in this approach which avoids the blind alley problem of ants.
Keywords
Swarm Intelligence (SI), Ant Colony Optimization (ACO), Stagnation, Tabu Search.- Color Image Segmentation Using Discrete Wavelet Transform and Improved Saliency Map
Authors
1 Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Science, Sri Parasakthi College for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 4 (2019), Pagination: 2020-2024Abstract
The main objective of this paper is to apply Self-Organizing Map (SOM) based Enhanced Fuzzy C-Means (EFCM) algorithm to color image segmentation which is a crucial problem in computer vision and pattern recognition. Discrete Wavelet Transform (DWT) is applied on the color image. The decomposition level is set to 3. Approximation coefficients are extracted from the decomposed image. Improved Saliency Map (ISM) is computed. Self-Organizing Map (SOM) is trained with approximation coefficients along with ISM values. The resultant image is clustered with EFCM algorithm. Proposed method is validated on Berkeley segmentation dataset and other natural color images. Performance of the method is evaluated by using accuracy, precision, recall, entropy and time. Simulation results showed that the proposed method can achieve good segmentation results with low computational complexity than other methods considered for comparison from the literature.Keywords
Color Image Segmentation, Discrete Wavelet Transform, Improved Saliency Map and SOM Enhance Fuzzy C-means Clustering.References
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