An Unequal Multi-hop Balanced Immune Clustering protocol for wireless sensor networks

Sabor, Nabil; Abo‐Zahhad, Mohammed; Sasaki, Shigenobu; Ahmed, Sabah M.

doi:10.1016/j.asoc.2016.02.016

Cited by 72 publications

(33 citation statements)

References 13 publications

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“…CH senses data, CH receives data packets, CH aggregate data packets, CH receives and transmits data packets from the other CH, and CH transmits the aggregated packets and the received packets of other CH to the BS [24]. Thus, the consumed energy by a CH can be obtained using the following equation [24].…”

Section: Energy Consumption Modelmentioning

confidence: 99%

“…On the contrary, the path loss is assigned p = 4 and the multi-path fading model is employed by setting E amp = ε mp if distance d more than the distance threshold d 0 . Besides, the sensing data by sensor also consumes amount of energy significantly as follows [24].…”

Section: Energy Consumption Modelmentioning

confidence: 99%

“…In each round, a node senses l-bit packet data and transmits it to the corresponding cluster head once a frame. Thus, the consumed energy by a regular node in the intra-cluster communication between the regular node i and its cluster head j is calculated by [24]:…”

Section: Energy Consumption Modelmentioning

confidence: 99%

“…Therefore, CH can use data aggregation ways to bundle all highly related data into a single length-fixed packet. The consumed energy to aggregate n packet of l-bits by the cluster head is calculated by [24]:…”

Section: Energy Consumption Modelmentioning

confidence: 99%

“…However, these approaches are a waste of energy and the transmission coverage are limited [18]; [4]; [7]. Therefore, several multi-hop routing protocols using unequal sized clustering technique [2]; [21]; [24] have been proposed to overcome the hot spot problem, improve the network scalability and optimize the energy-saving in order to prolong the network lifetime.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

Misbahuddin

Ratna

Sari

2018

INT J COMPUT COMMUN

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In multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. The cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. This paper proposes a dynamic multihop routing algorithm named Data Similarity Aware for Dynamic Multi-hop Routing Protocol (DSA-DMRP) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. The DSA-DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. In this mechanism, each node can recognize and note its similar neighbor nodes. Next, K-hop Clustering Algorithm (KHOPCA) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. The DSA-DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA and can satisfy the requirement of the dynamic multi-hop routing protocol.

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Section: Energy Consumption Modelmentioning

confidence: 99%

Section: Energy Consumption Modelmentioning

confidence: 99%

Section: Energy Consumption Modelmentioning

confidence: 99%

Section: Energy Consumption Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

Misbahuddin

Ratna

Sari

2018

INT J COMPUT COMMUN

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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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I‐FBECS: Improved fuzzy based energy efficient clustering using biogeography based optimization in wireless sensor network

Dwivedi

Sharma

2020

Trans Emerging Tel Tech

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Cluster formation and optimal cluster head selection are two optimization problems in wireless sensor network (WSN) for prolonged lifetime. To address the above mentioned issue, biogeography based selection of cluster heads along with efficient cluster formation is propound in this article which improves the performance of fuzzy based enhanced clustering scheme (FBECS). A novel fitness function is formulated by making use of remnant energy, distance to base station (BS), and density around node for optimal cluster head selection using biogeography based optimization (BBO). A rank based mechanism is developed for non‐cluster head nodes for selecting their cluster head. Routing technique for region based forwarding is designed to improve the lifetime expectancy. The proposed I‐FBECS is compared with two state of the art protocols namely LEACH and FBECS. Simulation results reveal an improved stability period by 33.8% and 38.9% over FBECS and LEACH, respectively. The proposed I‐FBECS exhibits better load balancing and improvement in terms of more alive nodes per round, more packets forwarded to the BS, improved average remnant energy, and extended lifetime. The proposed I‐FBECS is suitable for applications where the BS is located at a distant place.

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An Unequal Multi-hop Balanced Immune Clustering protocol for wireless sensor networks

Cited by 72 publications

References 13 publications

Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

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

I‐FBECS: Improved fuzzy based energy efficient clustering using biogeography based optimization in wireless sensor network

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