Solving the load balanced clustering and routing problems in WSNs with an fpt-approximation algorithm and a grid structure

Yarinezhad, Ramin; Hashemi, Seyed Naser

doi:10.1016/j.pmcj.2019.101033

Cited by 48 publications

(29 citation statements)

References 28 publications

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“…However, due to some limitations of the particle swarm optimization algorithm, there is no guarantee that the selected clustering and the selected relays are the best possible solution. A novel cluster-based routing protocol proposed in [21] uses a Fixed-Parameter Tractable (f pt) approximation algorithm with an approximation ratio of 1.1 to solve the load balancing problem. In addition, a virtual grid infrastructure containing multiple equal-sized cells is introduced, and the algorithm runs independently for each cell to make the f ptapproximation algorithm suitable for large-scale WSNs.…”

Section: Related Workmentioning

confidence: 99%

“…So formula (20) is proposed to evaluate the energy proportion of the cluster heads. By the way of weighted sum, the two optimization goals of the energy balance degree of the cluster head and the energy ratio of the cluster head are converted into a single objective optimization, as shown in equation (21), where ω 1 and ω 2 are weighting factors, and…”

Section: Proposed Fitness Functionmentioning

confidence: 99%

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An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

et al. 2020

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Clustering sensor nodes is an effective method in designing routing algorithms for Wireless Sensor Networks (WSNs), which improves network lifetime and energy efficiency. In clustered WSNs, cluster heads are the key nodes, they need to perform more tasks, so they consume more energy. Therefore, it is an important problem to select the optimal cluster heads. In this paper, we propose a clustering algorithm that selects cluster heads using an improved artificial bee colony (ABC) algorithm. Based on the standard ABC algorithm, an efficient improved ABC algorithm is proposed, and then the network cluster head energy, cluster head density, cluster head location and other similar factors are introduced into the improved ABC algorithm theory to solve the clustering problem in WSNs. In the network initialization period, all nodes have the same energy level, the improved ABC algorithm is used to optimize fuzzy C-means clustering to find the optimal clustering method. We also propose an energy-efficient routing algorithm based on an improved ant colony optimization for routing between the cluster heads and the base station. In order to improve energy efficiency and further improve network throughput, in the stable transmission phase, we introduce a polling control mechanism based on busy/idle nodes into intra-cluster communication. The performance of the proposed protocol is evaluated in several different scenarios. The simulation results show that the proposed protocol has a better performance compared to a number of recent similar protocols. INDEX TERMS WSN, clustering, energy efficiency, network lifetime, high throughput, polling, routing algorithm, artificial bee colony

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Fitness Functionmentioning

confidence: 99%

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

et al. 2020

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“…Recently, in [20, 21], two fpt‐approximation algorithms for solving the LBCP problem are presented. These two algorithms are designed based on grouping techniques.…”

Section: Previous Work On Lbcpmentioning

confidence: 99%

“…The fpt‐approximation algorithm presented in [20] has an approximation factor of 1.2 and is practical for WSNs with less than 800 sensor nodes and 40 gateways. The second fpt‐approximation algorithm presented in [21] has an approximation factor of 1.1 and is practical for WSNs with a maximum of 100 gateways.…”

Section: Previous Work On Lbcpmentioning

confidence: 99%

“…The design part of the proposed fpt‐approximation algorithm in this paper is somewhat similar to those in [20, 21] because it uses the same grouping techniques. However, since the proposed fpt‐approximation algorithm is k ‐additive, as opposed to the algorithms presented in [20, 21], and different parameters are used, the calculations involved in the design of this algorithm and the proofs of the theorems and lemmas are different from [20, 21] and thus they are given completely in this paper.…”

Section: Previous Work On Lbcpmentioning

confidence: 99%

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Exact and approximate algorithms for clustering problem in wireless sensor networks

Yarinezhad

Hashemi

2020

IET Communications

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A mobile sink path planning for wireless sensor networks based on priority‐ordered dependent nonparametric trees

Yalçın

Erdem

2020

Int J Communication

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SummaryThe professional design of the routing protocols with mobile sink(s) in wireless sensor networks (WSNs) is important for many purposes such as maximizing energy efficiency, increasing network life, and evenly distributing load balance across the network. Moreover, mobile sinks ought to first collect data from nodes which have very important and dense data so that packet collision and loss can be prevented at an advanced level. For these purposes, the present paper proposes a new mobile path planning protocol by introducing priority‐ordered dependent nonparametric trees (PoDNTs) for WSNs. Unlike traditional clustered or swarm intelligence topology‐based routing methods, a topology which has hierarchical and dependent infinite tree structure provides a robust link connection between nodes, making it easier to reselect ancestor nodes (ANs). The proposed priority‐ordered infinite trees are sampled in the specific time frames by introducing new equations and hierarchically associated with their child nodes starting from the root node. Hence, the nodes with the highest priority and energy that belong to the constructed tree family are selected as ANs with an opportunistic approach. A mobile sink simply visits these ANs to acquire data from all nodes in the network and return to where it started. As a result, the route traveled is assigned as the mobile path for the current round. We have performed comprehensive performance analysis to illustrate the effectiveness of the present study using NS‐2 simulation environment. The present routing protocol has achieved better results than the other algorithms over various performance metrics.

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Solving the load balanced clustering and routing problems in WSNs with an fpt-approximation algorithm and a grid structure

Cited by 48 publications

References 28 publications

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

Exact and approximate algorithms for clustering problem in wireless sensor networks

A mobile sink path planning for wireless sensor networks based on priority‐ordered dependent nonparametric trees

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