Impact of Number of Artificial Ants in ACO on Network Convergence Time: A Survey

Due to the dynamic nature of computer networks today, there is need to make the networks self-organized. Selforganization can be achieved by applying intelligent systems in the networks to improve convergence time. Bio-inspired algorithms that imitate real ant foraging behaviour of natural ants have been seen to be more successful when applied to computer networks to make the networks self-organized. In this paper, we studied how Ant Colony Optimization (ACO) has been applied in the networks as a bio-inspired algorithm and its challenges. We identified the number of ants as a drawback to guide this research. We retrieved a number of studies carried out on the influence of ant density on optimum deviation, number of iterations and optimization time. We found that even though some researches pointed out that the numbers of ants had no effect on algorithm performance, many others showed that indeed the number of ants which is a parameter to be set on the algorithm significantly affect its performance. To help bridge the gap on whether or not the number of ants were significant, we gave our recommendations based on the results from various studies in the conclusion section of this pape

Geographic GReedy routing with ACO recovery strategy GRACO

International audienceGeographic routing is an attractive routing strategy in wireless sensor networks. It works well in dense networks, but it may suffer from the void problem. For this purpose, a recovery step is required to guarantee packet delivery. Face routing has widely been used as a recovery strategy since proved to guarantee delivery. However, it relies on a planar graph not always achievable in realistic wireless networks and may generate long paths. In this paper, we propose GRACO, a new geographic routing algorithm that combines a greedy forwarding and a recovery strategy based on swarm intelligence. During recovery, ant packets search for alternative paths and drop pheromone trails to guide next packets within the network. GRACO avoids holes and produces near optimal paths. Simulation results demonstrate that GRACO leads to a significant improvement of routing performance and scalability when compared to the literature algorithms

Performance Analysis of AntNet-LA Protocol for Ad-hoc Networks based on Disaster Area Mobility Model

Availability of cheap positioning instruments like GPS receivers makes it possible for routing algorithms to use the position of nodes in an ad hoc mobile network. Regular position based routing algorithms fail to find a route from a source to a destination in some cases when the network contains nodes with irregular transmission ranges or they find a route that is much longer than the shortest path. On the other hand routing algorithms based on Ant Colony Optimization (ACO) find routing paths that are close to the shortest paths even if the nodes in the network have different transmission ranges. The drawback of these algorithms is the large number of messages that needs to be sent or the long delay before the routes are established. In this paper, we propose a novel protocol AntNet-LA which combines the idea of ACO with information about position of all nodes. In this technique the distance between the nodes is considered to transmit the packets, hence overcomes the drawbacks of AntNet algorithm which considers only cumulative probability for packet transmission. We compare performance of AntNet-LA with AntNet, Ad-hoc On Demand Distance Vector (AODV), Ad-hoc On Demand Multipath Distance Vector (AOMDV), Dynamic Source Routing (DSR) and Destination-Sequenced Distance-Vector Routing (DSDV) protocols. We also compare performance of AntNet-LA with distance-aware protocols such as Location Aided Routing (LAR), Geographical AODV GeoAODV and Position Based ANT colony optimization (PBANT)

GRACO: a geographic GReedy routing with an ACO-based void handling technique

Geographic routing has gained much attention as a basic routing primitive inwireless sensor networks due to its memory-less, scalability, efficiency and low overheadfeatures. Greedy forwarding is the simplest geographic routing scheme, it uses the distanceas a forwarding criterion. Nevertheless, it may suffer from communication holes, whereno next hop candidate is closer to the destination than the node currently holding thepacket. For this purpose, a void handling technique is needed to recover from the voidproblem and successfully deliver data packets if a path does exist between source anddestination nodes. Many approaches have been reported to solve this issue at the expenseof extra processing and or overhead. This paper proposes GRACO, an efficient geographicrouting protocol with a novel void recovery strategy based on ant colony optimization(ACO). GRACO is able to adaptively adjust the forwarding mechanism to avoid theblocking situation and effectively deliver data packets. Compared to GFG, one of the bestperforming geographic routing protocols, simulation results demonstrate that GRACOcan successfully find shorter routing paths with higher delivery rate, less control packetoverhead and shorter end-to-end delay

ANTMANET: a novel routing protocol for mobile ad-hoc networks based on ant colony optimisation

The core aim of this research is to present “ANTMANET” a novel routing protocol for Mobile Ad-Hoc networks. The proposed protocol aims to reduce the network overhead and delay introduced by node mobility in MANETs. There are two techniques embedded in this protocol, the “Local Zone” technique and the “North Neighbour” Table. They take an advantage of the fact that the nodes can obtain their location information by any means to reduce the network overhead during the route discovery phase and reduced the size of the routing table to guarantee faster convergence. ANTMANET is a hybrid Ant Colony Optimisation-based (ACO) routing protocol. ACO is a Swarm Intelligence (SI) routing algorithm that is well known for its high-quality performance compared to other distributed routing algorithms such as Link State and Distance Vector. ANTMANET has been benchmarked in various scenarios against the ACO routing protocol ANTHOCNET and several standard routing protocols including the Ad-Hoc On-Demand Distance Vector (AODV), Landmark Ad-Hoc Routing (LANMAR), and Dynamic MANET on Demand (DYMO). Performance metrics such as overhead, end-to-end delay, throughputs and jitter were used to evaluate ANTMANET performance. Experiments were performed using the QualNet simulator. A benchmark test was conducted to evaluate the performance of an ANTMANET network against an ANTHOCNET network, with both protocols benchmarked against AODV as an established MANET protocol. ANTMANET has demonstrated a notable performance edge when the core algorithm has been optimised using the novel adaptation method that is proposed in this thesis. Based on the simulation results, the proposed protocol has shown 5% less End-to-End delay than ANTHOCNET. In regard to network overhead, the proposed protocol has shown 20% less overhead than ANTHOCNET. In terms of comparative throughputs ANTMANET in its finest performance has delivered 25% more packets than ANTHOCNET. The overall validation results indicate that the proposed protocol was successful in reducing the network overhead and delay in high and low mobility speeds when compared with the AODV, DMO and LANMAR protocols. ANTMANET achieved at least a 45% less delay than AODV, 60% less delay than DYMO and 55% less delay than LANMAR. In terms of throughputs; ANTMANET in its best performance has delivered 35% more packets than AODV, 40% more than DYMO and 45% more than LANMAR. With respect to the network overhead results, ANTMANET has illustrated 65% less overhead than AODV, 70% less than DYMO and 60 % less than LANMAR. Regarding the Jitter, ANTMANET at its best has shown 60% less jitter than AODV, 55% jitter less than DYMO and 50% less jitter than LANMAR