AntMeshNet: Difference between revisions
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== Introduction == | == Introduction == | ||
Wireless Mesh Networks (WMNs) are emerging as evolutionary self organizing networks to provide connectivity to end users. Efficient Routing in WMNs is a highly challenging problem due to existence of stochastically changing network environments. Routing strategies must be dynamically adaptive and evolve in a decentralized, self organizing and fault tolerant way to meet the needs of this changing environment inherent in WMNs. Conventional routing paradigms establishing exact shortest | Wireless Mesh Networks (WMNs) are emerging as evolutionary self-organizing networks to provide connectivity to end users. Efficient Routing in WMNs is a highly challenging problem due to the existence of stochastically changing network environments. Routing strategies must be dynamically adaptive and evolve in a decentralized, self-organizing, and fault-tolerant way to meet the needs of this changing environment inherent in WMNs. Conventional routing paradigms establishing exact shortest paths between a source-terminal node pair perform poorly under the constraints imposed by dynamic network conditions. In this paper, the authors propose an optimal routing approach inspired by the foraging behavior of ants to maximize the network performance while optimizing the network resource utilization. The proposed AntMeshNet algorithm is based upon the Ant Colony Optimization (ACO) algorithm, exploiting the foraging behavior of simple biological ants. The work shows an Integrated Link Cost (ILC) measure used as link distance between two adjacent nodes. ILC takes into account throughput, delay, jitter of the link, and residual energy of the node. Since the relationship between input and output parameters is highly non-linear, fuzzy logic was used to evaluate ILC based upon four inputs. This fuzzy system consists of various rules. Routing tables are continuously updated after a predefined interval or after a change in network architecture is detected. This takes care of the dynamic environment of WMNs. A large number of trials were conducted for each model. The results have been compared with the Adhoc On-demand Distance Vector (AODV) algorithm. The results are found to be far superior to those obtained by the AODV algorithm for the same WMN. | ||
poorly under the constraints imposed by dynamic network conditions. In this paper, the authors propose an optimal routing approach inspired by the foraging behavior of ants to maximize the network performance while optimizing the network resource utilization. The proposed AntMeshNet algorithm is based upon Ant Colony Optimization (ACO) algorithm | |||
takes into account throughput, delay, jitter of the link and residual energy of the node. Since the relationship between input and output parameters is highly non-linear, fuzzy logic was used to evaluate ILC based upon four inputs. This fuzzy system consists of various rules. Routing tables are continuously updated after a predefined interval or after a change in network architecture is detected. This takes care of dynamic environment of WMNs. A large number of trials were conducted for each model. The results have been compared with Adhoc On-demand Distance Vector (AODV) algorithm. The results are found to be far superior to those obtained | |||
by AODV algorithm for the same WMN. | |||
== Integrated Link Cost Evaluation == | == Integrated Link Cost Evaluation == | ||
The Integrated Link Cost (ILC) measure in this work consists of four vital parameters of the network and nodes: throughput, end-to-end | The Integrated Link Cost (ILC) measure in this work consists of four vital parameters of the network and nodes: throughput, end-to-end delay, jitter of the link, and the residual energy of the node. For a link between adjacent nodes, high throughput, low end-to-end delay, and low jitter are the required conditions. A variety of applications in ‘always on’ dynamic multi-hop WMNs require optimal use of node energy. As Mesh Routers (MRs) deal with heavier traffic load, optimization of energy at MRs is very significant. Considering the significance of residual energy of nodes, we have included this parameter to optimize the performance of WMNs. The node having less energy must be used accordingly for hopping or other routing purposes. Based upon these four parameters, the fuzzy logic evaluated integrated link cost measure. This integrated link cost is used as the distance between the two particular adjacent nodes. | ||
delay, jitter of the link and the residual energy of the node. For a link between adjacent nodes high throughput, low end-to-end delay and low jitter are the required conditions. A variety of applications in ‘always on’ dynamic multi-hop WMNs require optimal use of node energy. As Mesh Routers (MRs) deal with heavier traffic load, optimization of energy at MRs is very significant. Considering the significance of residual energy of nodes we have included this parameter to optimize the performance of WMNs. The node having less energy must be used accordingly for | |||
nodes. | |||
Integrated Link Cost (ILC) = f (Throughput, Delay, Jitter, residual Energy)(1) | Integrated Link Cost (ILC) = f (Throughput, Delay, Jitter, residual Energy)(1) | ||
Integrated Link Cost is a function of throughput, delay, jitter of the link and the residual energy of the node. | Integrated Link Cost is a function of throughput, delay, jitter of the link, and the residual energy of the node. | ||
== References == | == References == | ||
Latest revision as of 22:03, 15 January 2026
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Introduction
Wireless Mesh Networks (WMNs) are emerging as evolutionary self-organizing networks to provide connectivity to end users. Efficient Routing in WMNs is a highly challenging problem due to the existence of stochastically changing network environments. Routing strategies must be dynamically adaptive and evolve in a decentralized, self-organizing, and fault-tolerant way to meet the needs of this changing environment inherent in WMNs. Conventional routing paradigms establishing exact shortest paths between a source-terminal node pair perform poorly under the constraints imposed by dynamic network conditions. In this paper, the authors propose an optimal routing approach inspired by the foraging behavior of ants to maximize the network performance while optimizing the network resource utilization. The proposed AntMeshNet algorithm is based upon the Ant Colony Optimization (ACO) algorithm, exploiting the foraging behavior of simple biological ants. The work shows an Integrated Link Cost (ILC) measure used as link distance between two adjacent nodes. ILC takes into account throughput, delay, jitter of the link, and residual energy of the node. Since the relationship between input and output parameters is highly non-linear, fuzzy logic was used to evaluate ILC based upon four inputs. This fuzzy system consists of various rules. Routing tables are continuously updated after a predefined interval or after a change in network architecture is detected. This takes care of the dynamic environment of WMNs. A large number of trials were conducted for each model. The results have been compared with the Adhoc On-demand Distance Vector (AODV) algorithm. The results are found to be far superior to those obtained by the AODV algorithm for the same WMN.
Integrated Link Cost Evaluation
The Integrated Link Cost (ILC) measure in this work consists of four vital parameters of the network and nodes: throughput, end-to-end delay, jitter of the link, and the residual energy of the node. For a link between adjacent nodes, high throughput, low end-to-end delay, and low jitter are the required conditions. A variety of applications in ‘always on’ dynamic multi-hop WMNs require optimal use of node energy. As Mesh Routers (MRs) deal with heavier traffic load, optimization of energy at MRs is very significant. Considering the significance of residual energy of nodes, we have included this parameter to optimize the performance of WMNs. The node having less energy must be used accordingly for hopping or other routing purposes. Based upon these four parameters, the fuzzy logic evaluated integrated link cost measure. This integrated link cost is used as the distance between the two particular adjacent nodes. Integrated Link Cost (ILC) = f (Throughput, Delay, Jitter, residual Energy)(1) Integrated Link Cost is a function of throughput, delay, jitter of the link, and the residual energy of the node.
References
1. Sharma, Sharad, Shakti Kumar and Brahmjit Singh. "AntMeshNet: An Ant Colony Optimization Based Routing Approach to Wireless Mesh Networks." IJAMC 5.1 (2014): 20-45. Web. 8 Feb. 2016. doi:10.4018/ijamc.2014010102
2. Di Caro, G., Ducatelle, F., & Gambardella, L. M.(2005). AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications, 16(5), 443–455. doi:10.1002/ett.1062
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