Surviving node-node failures within wireless networks for a near optimal ant colony system message re-routing

Owoade, Ayoade Akeem; Osunmakinde, Isaac Olusegun

doi:10.1504/ijmndi.2019.107701

Cited by 2 publications

(5 citation statements)

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“…where the number of nodes in the network and the length of the route found by a nearest neighbor heuristic are given as n and L nn , respectively. 22 • Local pheromone update:…”

Section: Mathematical Analysis Of Acs Algorithmmentioning

confidence: 99%

“…where α and β are the two parameters that decide the relative influence of the pheromone trail and τ i,j is the amount of pheromone on the link between nodes i and j. 22 Table 2 summarizes the ACS model parameters.…”

Section: Mathematical Analysis Of Acs Algorithmmentioning

confidence: 99%

“… Initial pheromone concentration:

τ_{n} = \frac{1}{n L_{n n}},

where the number of nodes in the network and the length of the route found by a nearest neighbor heuristic are given as

n

and

L_{n n}

, respectively 22 Local pheromone update:

τ_{i, j} false(t + 1 false) = false(1 - ρ false) τ_{i, j} false(t false) + ρ τ_{0},

where the parameter governing pheromone decay is given as

ρ, ρ = 0.1

was adopted from literature such that

0 < ρ < 1

and

τ_{0}

, the amount of pheromone on the edge

false(i, j false)

at time

t

is given as

τ_{i, j} false(t false)

, and the initial value of pheromone on all edges is given as

τ_{0}

22 Edge attractiveness:

η_{i, j} = \frac{1}{d_{i, j}},

where the distance between

i

and

j

is given

d

22 …”

Section: Egaacs Model Developmentmentioning

confidence: 99%

“…Local pheromone update:

τ_{i, j} false(t + 1 false) = false(1 - ρ false) τ_{i, j} false(t false) + ρ τ_{0},

where the parameter governing pheromone decay is given as

ρ, ρ = 0.1

was adopted from literature such that

0 < ρ < 1

and

τ_{0}

, the amount of pheromone on the edge

false(i, j false)

at time

t

is given as

τ_{i, j} false(t false)

, and the initial value of pheromone on all edges is given as

τ_{0}

22 Edge attractiveness:

η_{i, j} = \frac{1}{d_{i, j}},

where the distance between

i

and

j

is given

d

22 Computation of edge probability:

P_{i, j}^{k} false(t false) = \frac{{false[τ_{i, j} false(t false) false]}^{α} {false[η_{i, j} false]}^{β}}{\sum_{i \in J_{i}^{k}} {false[τ_{i, j} false(t false) false]}^{α} * {false[η_{i, j} false]}^{β}},

where

…”

Section: Egaacs Model Developmentmentioning

confidence: 99%

“…Edge attractiveness:

η_{i, j} = \frac{1}{d_{i, j}},

where the distance between

i

and

j

is given

d

22 Computation of edge probability:

P_{i, j}^{k} false(t false) = \frac{{false[τ_{i, j} false(t false) false]}^{α} {false[η_{i, j} false]}^{β}}{\sum_{i \in J_{i}^{k}} {false[τ_{i, j} false(t false) false]}^{α} * {false[η_{i, j} false]}^{β}},

where

α

and

β

are the two parameters that decide the relative influence of the pheromone trail and

τ_{i, j}

is the amount of pheromone on the link between nodes

i

and

j

22 . Table 2 summarizes the ACS model parameters. …”

Section: Egaacs Model Developmentmentioning

confidence: 99%

See 4 more Smart Citations

Efficient hybrid enhanced genetic algorithm and ant colony system model for rerouting multimedia message in multiple node–link failures within wireless network

Owoade

Ogundile

Babalola

et al. 2023

Int J Communication

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Summary Failure‐resilient wireless networks have attracted the interest of the research community and have been an open area of concern in the studies of wireless network in recent years. Accordingly, different research on restoration techniques have been carried out to proffer solution to the network component failures. In the same vein, this article proposes a hybridized enhanced genetic algorithm and ant colony system (EGAACS) survivability model, which can instantly resolve node–link failure problems, thus improving the quality of service of the wireless network. The EGAACS is a hybrid model that combines the principles of the enhanced genetic algorithm (EGA) and ant colony system (ACS) models to form a capacity efficiency solution that outperforms the ACS and EGA models in terms of path cost and transmission delay. The resilience of this proposed EGAACS model is verified for different wireless network sizes (20, 30, 40, and 50 node networks). Simulation results show that the proposed EGAACS model generates the best close to optimal paths in terms of the path cost and transmission delay in comparison to the EGA and ACS models. In fact, the performance of the proposed EGAACS model is more conspicuous as the size of the network increases. More importantly, the proposed EGAACS model is suitable for real‐time wireless network as it exhibits moderate computational time complexity.

show abstract

“…where the number of nodes in the network and the length of the route found by a nearest neighbor heuristic are given as n and L nn , respectively. 22 • Local pheromone update:…”

Section: Mathematical Analysis Of Acs Algorithmmentioning

confidence: 99%

Section: Mathematical Analysis Of Acs Algorithmmentioning

confidence: 99%

“… Initial pheromone concentration:

τ_{n} = \frac{1}{n L_{n n}},

where the number of nodes in the network and the length of the route found by a nearest neighbor heuristic are given as

n

and

L_{n n}

, respectively 22 Local pheromone update:

τ_{i, j} false(t + 1 false) = false(1 - ρ false) τ_{i, j} false(t false) + ρ τ_{0},

where the parameter governing pheromone decay is given as

ρ, ρ = 0.1

was adopted from literature such that

0 < ρ < 1

and

τ_{0}

, the amount of pheromone on the edge

false(i, j false)

at time

t

is given as

τ_{i, j} false(t false)

, and the initial value of pheromone on all edges is given as

τ_{0}

22 Edge attractiveness:

η_{i, j} = \frac{1}{d_{i, j}},

where the distance between

i

and

j

is given

d

22 …”

Section: Egaacs Model Developmentmentioning

confidence: 99%

“…Local pheromone update:

τ_{i, j} false(t + 1 false) = false(1 - ρ false) τ_{i, j} false(t false) + ρ τ_{0},

where the parameter governing pheromone decay is given as

ρ, ρ = 0.1

was adopted from literature such that

0 < ρ < 1

and

τ_{0}

, the amount of pheromone on the edge

false(i, j false)

at time

t

is given as

τ_{i, j} false(t false)

, and the initial value of pheromone on all edges is given as

τ_{0}

22 Edge attractiveness:

η_{i, j} = \frac{1}{d_{i, j}},

where the distance between

i

and

j

is given

d

22 Computation of edge probability:

P_{i, j}^{k} false(t false) = \frac{{false[τ_{i, j} false(t false) false]}^{α} {false[η_{i, j} false]}^{β}}{\sum_{i \in J_{i}^{k}} {false[τ_{i, j} false(t false) false]}^{α} * {false[η_{i, j} false]}^{β}},

where

…”

Section: Egaacs Model Developmentmentioning

confidence: 99%

“…Edge attractiveness:

η_{i, j} = \frac{1}{d_{i, j}},

where the distance between

i

and

j

is given

d

22 Computation of edge probability:

P_{i, j}^{k} false(t false) = \frac{{false[τ_{i, j} false(t false) false]}^{α} {false[η_{i, j} false]}^{β}}{\sum_{i \in J_{i}^{k}} {false[τ_{i, j} false(t false) false]}^{α} * {false[η_{i, j} false]}^{β}},

where

α

and

β

are the two parameters that decide the relative influence of the pheromone trail and

τ_{i, j}

is the amount of pheromone on the link between nodes

i

and

j

22 . Table 2 summarizes the ACS model parameters. …”

Section: Egaacs Model Developmentmentioning

confidence: 99%

See 3 more Smart Citations

Efficient hybrid enhanced genetic algorithm and ant colony system model for rerouting multimedia message in multiple node–link failures within wireless network

Owoade

Ogundile

Babalola

et al. 2023

Int J Communication

View full text Add to dashboard Cite

show abstract

Resilient Rerouting in IoT Systems with Evolutionary Computing

Owoade

Osunmakinde

2021

Artificial Intelligence in Intelligent Systems

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Surviving node-node failures within wireless networks for a near optimal ant colony system message re-routing

Cited by 2 publications

References 0 publications

Efficient hybrid enhanced genetic algorithm and ant colony system model for rerouting multimedia message in multiple node–link failures within wireless network

Efficient hybrid enhanced genetic algorithm and ant colony system model for rerouting multimedia message in multiple node–link failures within wireless network

Resilient Rerouting in IoT Systems with Evolutionary Computing

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