A Survey on Centralised and Distributed Clustering Routing Algorithms for WSNs

Zanjireh, Morteza Mohammadi; Larijani, Hadi

doi:10.1109/vtcspring.2015.7145650

Cited by 85 publications

(38 citation statements)

References 26 publications

(29 reference statements)

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“…Thus, the value of Expression (5) would be A = ( 9 n 1) ( 9 n 2) ( 9 n � ) 10 + + 10 + + ... + 10 + 10 As we have TI; terms in the (6), it can be simplified to (6) n 9 n n A=( 10 )X( 1O )+(1+2+ .. ·+ 1O )·…”

Section: Analytical Modelling Of A-anchmentioning

confidence: 99%

“…They can gather and deliver their sensed events to a Base Station (BS) in order to monitor a far-away environment. Due to their flexibility and low-cost nature, they can be used in various applications such as emergency response, commercial, surveillance, volcanic earthquakes, health-care, smart traffics, and weather forecast ing [1]- [6]. WSNs can be employed in an ad-hoc manner and in rough areas in which a human presence is impossible [7].…”

Section: Introductionmentioning

confidence: 99%

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Analytical modelling of the A-ANCH clustering algorithm for WSNs

Zanjireh

Larijani

2015

2015 Seventh International Conference on Ubiquitous and Future Networks

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In a Wireless Sensor Network (WSN), the im portance of individual sensors might not be equal. That is, some nodes might be more important than others due to their proximity to critical regions. A-ANCH is one of the new clustering algorithms for these scenarios which extends the lifetimes of active sensors by sacrificing in-active ones. In this paper, an analytical model for the A-ANCH algorithm is presented in order to predict the number of sensed events (packets) in the network. Our extensive experiments illustrated on average 91.08% accuracy for the proposed mathematical model compared with the results of a simulation study. The analytical model presented here has also revealed that the number of sensed events in the A-ANCH algorithm is sensitive to the number of sensors and the network dimensions. On the other hand, it is almost insensitive to the number of clusters.

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Section: Analytical Modelling Of A-anchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analytical modelling of the A-ANCH clustering algorithm for WSNs

Zanjireh

Larijani

2015

2015 Seventh International Conference on Ubiquitous and Future Networks

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“…Virtual concentric circle band-based clustering (VCCBC) [12] and an energy-efficient protocol with static clustering (EEPSC) [13] are two of the examples of the static clustering methods in WSNs; although, in terms purely of static protocol performance, network overhead is reduced but shows instability for a long period of time. The main disadvantage of static methods is that it results in energy depletion in several nodes [14,15]. In dynamic protocols, clustering is performed in each round, and new clusters form during each round.…”

Section: Introductionmentioning

confidence: 99%

“…Low-energy adaptive clustering hierarchy (LEACH) [3] is one of the examples of the dynamic clustering methods. Dynamic performance can improve network lifetime, but usually, it has a high overhead [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes

et al. 2019

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The Internet of things (IoT) provides the possibility of communication between smart devices and any object at any time. In this context, wireless nodes play an important role in reducing costs and simple use. Since these nodes are often used in less accessible locations, recharging their battery is hardly feasible and in some cases is practically impossible. Hence, energy conservation within each node is a challenging discussion. Clustering is an efficient solution to increase the lifetime of the network and reduce the energy consumption of the nodes. In this paper, a novel hybrid unequal multi-hop clustering based on density (HCD) is proposed to increase the network lifetime. In the proposed protocol, the cluster head (CH) selection is performed only by comparing the status of each node to its neighboring nodes. In this new technique, the parameters involving energy of nodes, the number of neighboring nodes, the distance to the base station (BS), and the layer where the node is placed in are considered in CH selection. So, in this new and simple technique considers energy consumption of the network and load balancing. Clustering is performed unequally so that cluster heads (CHs) close to BS have more energy for data relay. Also, a hybrid dynamic–static clustering was performed to decrease overhead. In the current protocol, a distributed clustering and multi-hop routing approach was applied between cluster members (CMs), to CHs, and CHs to BS. HCD is applied as a novel assistance to cluster heads (ACHs) mechanism, in a way that a CH accepts to use member nodes with suitable state to share traffic load. Furthermore, we performed simulation for two different scenarios. Simulation results showed the reliability of the proposed method as it was resulted in a significant increase in network stability and energy balance as well as network lifetime and efficiency.

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“…However, this is not true and there is various research works where researchers have chosen non-energy parameters too in the selection process. At present, there are various studies being carried out towards enhancing clustering process [5], routing process [6], load balancing process [7], and traffic management process. Unfortunately, very less number of works has been found to be attained a height of standards.…”

Section: Introductionmentioning

confidence: 99%

MOMEE: Manifold Optimized Modeling of Energy Efficiency in Wireless Sensor Network

Rajalakshmi¹,

Prakash²

2017

ijacsa

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Abstract-Although adoption pace of wireless sensor network has increased in recent times in many advance technologies of ubiquitous-ness, but still there are various open-end challenges associated with energy efficiencies among the sensor nodes till now. We reviewed the existing research approaches towards energy optimization techniques to explore significant problems. This paper introduces MOMEE i.e. Manifold Optimized Modeling of Energy Efficiency that offers novel clustering as well as novel energy optimized routing strategy. The proposed system uses analytical modeling methodology and is found to offer better resiliency against traffic bottleneck condition. The study outcome of MOMEE exhibits higher number of alive nodes, lower number of dead nodes, good residual energy, and better throughput as compared to existing energy efficient routing approaches in wireless sensor network.

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A Survey on Centralised and Distributed Clustering Routing Algorithms for WSNs

Cited by 85 publications

References 26 publications

Analytical modelling of the A-ANCH clustering algorithm for WSNs

Analytical modelling of the A-ANCH clustering algorithm for WSNs

A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes

MOMEE: Manifold Optimized Modeling of Energy Efficiency in Wireless Sensor Network

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