An Intelligent Hybrid Protocol for Effective Load Balancing and Energy Efficient Routing for MANETs

MANET (Mobile ad hoc network) is an autonomous decentralised network. And it is a collection of wireless mobile nodes that dynamically form a temporary network without the reliance of any infrastructure or central administration. Routing is a challenging task in manet. When the size and complexity increases the important challenge in manet is to avoid congestion with effective load balancing and improve energy, QoS parameters inside the network. In this work we propose a new hybrid protocol by combining ACO and Predator prey (LV) model which known as ACRRCC (Ant colony based rate regulating congestion control) method, which works efficiently in two phases. The efficient and optimal routing strategy is done by phase I using ant colony optimization. In phase II the congestion is majorly controlled by employing a mathematical model named predator-prey model which regulates the rate of the traffic flow in the network path. Performance of our proposed hybrid model ACRRCC yields good results under simulation study when compared with simple ACO

Analysis Performance and Fairness (using Jain’s Index) of AODV and DSDV based on Ant Colony Optimization (ACO) in MANETs

Mobile Ad Hoc Networks (MANETs) are the collection of mobile nodes which can form randomly and dynamically without need preexisting network infrastructure that nodes can be arbitraly located and can move freely. The challenges research in MANETs routing is topology changes continuously. Its caused paths which initially efficient can quickly become inefficient or even infeasible. AODV and DSDV routing protocols have weaknesses for mobility network, which is often happens to drop a link. It caused source node should build routing again from scratch. So, for conditions simultant delivery of data will caused decreased dropped packets values, and resulting throughput values down. Some weakness in AODV and DSDV routing protocol, can be assisted to take advantage of the characteristics of the ant collony optimization. In this thesis, make a comparing AODV and DSDV fairness and performance using Ant Colony Optimazation in MANETs base on prevoius research. In our simulation result shown modification conventional routing protocols AODV and DSDV with the added Antnet Algorithm can affected to better performance. The throughput values increased about 6,367% - 13,02% for AODV and about 0,68% - 5,47% for DSDV. But mke a change delay time worst about 5.23% - 6,02% for AODV and 60.88% - 65.82% for DSDV. AODV routing protocols performance is still better than DSDV routing protocols even added Antnet Algorithm to them. Index Fairness AODV more fair then DSDV even added Antnet Algorithm. It shown at Index Fairness graphic, distributed Index Fairness about 1.00 for AODV which added Antnet Algorithms. Keyword : mobile ad-hoc networks (MANETs); throughput; delay time; routing overhead; Index Fairnes

An ACO Algorithm for Effective Cluster Head Selection

This paper presents an effective algorithm for selecting cluster heads in mobile ad hoc networks using ant colony optimization. A cluster in an ad hoc network consists of a cluster head and cluster members which are at one hop away from the cluster head. The cluster head allocates the resources to its cluster members. Clustering in MANET is done to reduce the communication overhead and thereby increase the network performance. A MANET can have many clusters in it. This paper presents an algorithm which is a combination of the four main clustering schemes- the ID based clustering, connectivity based, probability based and the weighted approach. An Ant colony optimization based approach is used to minimize the number of clusters in MANET. This can also be considered as a minimum dominating set problem in graph theory. The algorithm considers various parameters like the number of nodes, the transmission range etc. Experimental results show that the proposed algorithm is an effective methodology for finding out the minimum number of cluster heads.Comment: 7 pages, 5 figures, International Journal of Advances in Information Technology (JAIT); ISSN: 1798-2340; Academy Publishers, Finlan

Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.Comment: To appear in Journal of Computer and System Science

An Ant Colony Optimization based Routing Techniques for VANET

With number of moving vehicles, vehicular Ad Hoc Network (VANET) is formed. These are provided with the wireless connections. Among various challenges in the VANET such as security and privacy of the messages, data forwarding is also considered as a major challenge. The effective communication is mainly depends on the how safely and fast the data is being forwarded among the vehicles. Data forwarding using Greedy mechanism suitable for routing in the VANETs, it depends only on the position of nodes and also data forwarding is done with minimum number of hops. In this paper, Position based GPCR and topology based DYMO routing protocol are adapted to make the use of Ant Colony Optimization (ACO) procedures. The resulting bio-inspired protocols, ACO_GPCR and ACO_DYMO had its performance evaluated and compared against existing GPCR and DYMO routing protocols. The obtained results suggest that making the use of ACO algorithm make these protocols more efficient in terms of Delay, Jitter, Packet Delivery Ratio and energy consumption

An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs