FAMACROW: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks

Gajjar, Sachin; Sarkar, Mitali; Dasgupta, K. S.

doi:10.1016/j.asoc.2016.02.019

Cited by 99 publications

(51 citation statements)

References 34 publications

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“…However, the main drawback of this approach was that the high volume of control message exchange between selected CHs and sensor nodes to from clusters within the network. Gajjar et al proposed an unequal clustering algorithm named fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross‐layer protocol for WSN (FAMACROW). In this technique, fuzzy logic was used to select CHs within the network.…”

Section: Related Workmentioning

confidence: 99%

“…However, rotation of CHs only balances the energy consumption of the non‐CH nodes in an intra‐cluster environment, it does not balance the energy consumption among CHs in the inter‐cluster multi‐hop routing environment. Furthermore, most studies on load balanced clustering propose static solutions, that is, they do not adapt the topology to the changing network conditions . These conditions are often not favorable for the elected CHs, regarding energy consumption.…”

Section: Introductionmentioning

confidence: 99%

“…However, these approaches do not balance CH load in the inter‐cluster environment. To address the balance of energy consumption problem in an inter‐cluster environment, recent studies on balance energy consumption in WSNs have proposed unequal clustering mechanisms whereby the entire network was divided into several clusters of unequal size . The main concept behind these unequal clustering mechanisms was to adjust the cluster size with respect to the distance between the CHs and the BS to reduce the energy consumption of the CHs that are close to the BS.…”

Section: Introductionmentioning

confidence: 99%

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A green cluster‐based routing scheme for large‐scale wireless sensor networks

Chanak

Banerjee

Sherratt

2020

Int J Communication

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In wireless sensor networks (WSNs), clustering has been shown to be an efficient technique to improve scalability and network lifetime. In clustered networks, clustering creates unequal load distribution among cluster heads (CHs) and cluster member (CM) nodes. As a result, the entire network is subject to premature death because of the deficient active nodes within the network. In this paper, we present clustering-based routing algorithms that can balance out the trade-off between load distribution and network lifetime "green cluster-based routing scheme." This paper proposes a new energy-aware green cluster-based routing algorithm to preventing premature death of large-scale dense WSNs. To deal with the uncertainty present in network information, a fuzzy rule-based node classification model is proposed for clustering. Its primary benefits are flexibility in selecting effective CHs, reliability in distributing CHs overload among the other nodes, and reducing communication overhead and cluster formation time in highly dense areas. In addition, we propose a routing scheme that balances the load among sensors. The proposed scheme is evaluated through simulations to compare our scheme with the existing algorithms available in the literature. The numerical results show the relevance and improved efficiency of our scheme. K E Y W O R D Sdense wireless sensor networks, energy consumption, fuzzy logic, green clustering, network separation

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A green cluster‐based routing scheme for large‐scale wireless sensor networks

Chanak

Banerjee

Sherratt

2020

Int J Communication

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“…Gajjat et al, proposed FAMACROW[14], a fuzzy based algorithm. But when compared with other fuzzy based clustering algorithm, FAMACROW uses Link Quality Indicator (LQI) of the transmission channel of a node as an important factor for CH election.…”

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confidence: 99%

DHCFL: Dual Head Clustering Based On Fuzzy Logic for Wireless Sensor Networks

2020

IJRTE

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Wireless Sensor Networks (WSN) are constructed by interconnecting miniature sensor nodes for monitoring the environment uninterrupted. These miniature nodes are having the sensing, processing and communication capability in a smaller scale powered by a battery unit. Proper energy conservation is required for the entire system. Clustering mechanism in WSN advances the lifetime and stability in the network. It achieves data aggregation and reduces the number of data transmission to the Base station (BS). But the Cluster Head (CH) nodes are affected by rapid energy depletion problem due to overload. A CH node spends its energy for receiving data from its member nodes, aggregation and transmission to the BS. In CH election, multiple overlapping factors makes it difficult and inefficient which costs the lifetime of the network. In recent years, Fuzzy Logic is widely used for CH election mechanism for WSN. But the underlying problem of the CHs node continues. In this research work, a new clustering algorithm DHCFL is proposed which elects two CHs for a cluster which shares the load of a conventional CH node. Data reception and aggregation will be done by CH aggregator (CH-A) node and data transmission to the BS will be carried over by CH relay (CH-R) node. Both CH-A and CH-R nodes are elected through fuzzy logic which addresses the uncertainty in the network too. The proposed algorithm DHCFL is compared and tested in different network scenarios with existing clustering algorithms and it is observed that DHCFL outperforms other algorithms in all the network scenarios.

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“…Through the wireless sensor network, humans can directly perceive the outside world. In 2003, Business Week evaluates that the wireless sensor network is one of the four major high-tech in the century [3]. In addition, it points out that wireless sensor network is one of the most influential technologies in the world.…”

Section: Introductionmentioning

confidence: 99%

An Improved Ant Colony Algorithm in Wireless Sensor Network Routing

Lv¹

2017

Int. J. Onl. Eng.

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Abstract-In order to make the energy consumption of network nodes relatively balanced, we apply ant colony optimization algorithm to wireless sensor network routing and improve it. In this paper, we propose a multi-path wireless sensor network routing algorithm based on energy equalization. The algorithm uses forward ants to find the path from the source node to the destination node, and uses backward ants to update the pheromone on the path. In the route selection, we use the energy of the neighboring nodes as the parameter of the heuristic function. At the same time, we construct the fitness function, and take the path length and the node residual energy as its parameters. The simulation results show that the algorithm can not only avoid the problem of local optimal solution, but also prolong the life cycle of the network effectively.

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FAMACROW: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks

Cited by 99 publications

References 34 publications

A green cluster‐based routing scheme for large‐scale wireless sensor networks

A green cluster‐based routing scheme for large‐scale wireless sensor networks

DHCFL: Dual Head Clustering Based On Fuzzy Logic for Wireless Sensor Networks

An Improved Ant Colony Algorithm in Wireless Sensor Network Routing

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