Balancing Energy Consumption in Clustered Wireless Sensor Networks

Ducrocq, Thierry; Hauspie, Michaël; Mitton, Nathalie

doi:10.1155/2013/314732

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…Extending the life of a wireless sensor network is critical to important applications such as battlefield surveillance where the nodes of the network must continue to be monitored and reported for a maximum period rather than getting exhausted in a less span of time, leading to interruptions in the network, division due to depletion of energy, etc. Due to the constraints exhibited by nodes of the WSN, it is necessary to utilize energy in an efficient way by introducing novel techniques and approaches to extend the lifetime of the WSN [25,26]. In this section, the significance of the conservation of energy and its balancing using threshold energy concept (TECB) among nodes [27,28] in SIBER-XLP is presented.…”

Section: Energy Conservation and Balancing In Wsnmentioning

confidence: 99%

Swarm Intelligence-Based Bio-Inspired Framework for Wireless Sensor Networks

Naseer¹,

Neelima²,

Narsimha³

2021

Wireless Sensor Networks - Design, Deployment and Applications

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Wireless Sensor Networks (WSNs) are gaining immense popularity as a result of their wide potential applications in industry, military, and academia such as military surveillance, agricultural monitoring, industrial automation, and smart homes. Currently, WSN has garnered tremendous significance as it is has become the core component of the Internet of Things (IOT) area. Modern-day applications need a high level of security and quick response mechanism to deal with the emerging data trends where the response is measured in terms of latency, throughput, and scalability. Further, critical security issues need to be considered due to various types of threats and attacks WSNs are exposed to as they are deployed in harsh and hostile environments unattended in most of the mission critical applications. The fact that a complex sensor network consisting of simple computing units has similarities with specific animal communities, whose members are often very simple but produce together more sophisticated and capable entities. Thus, from an algorithmic viewpoint, bio-inspired framework such as swarm intelligence technology may provide valuable alternative to solve the large scale optimization problems that occur in wireless sensor networks. Self-organization, on the other hand, can be useful for distributed control and management tasks. In this chapter, swarm intelligence and social insects-based approaches developed to deal with a bio-inspired networking framework are presented. The proposed approaches are designed to tackle the challenges and issues in the WSN field such as large scale networking, dynamic nature, resource constraints, and the need for infrastructure-less and autonomous operation having the capabilities of self-organization and survivability. This chapter covers three phases of the research work carried out toward building a framework. First phase involves development of SIBER-XLP model, Swarm Intelligence Based Efficient Routing protocol for WSN with Improved Pheromone Update Model, and Optimal Forwarder Selection Function which chooses an optimal path from source to the sink to forward the packets with the sole objective to improve the network lifetime by balancing the energy among the nodes in the network and at the same time selecting good quality links along the path to guarantee that node energy is not wasted due to frequent retransmissions. The second phase of the work develops a SIBER-DELTA model, which represents Swarm Intelligence Based Efficient Routing protocol for WSN taking into account Distance, Energy, Link Quality, and Trust Awareness. WSNs are prone to behavior related attacks due to the misbehavior of nodes in forwarding the packets. Hence, trust aware routing is important not only to protect the information but also to protect network performance from degradation and protect network resources from undue consumption. Finally, third phase of the work involves the development of SIBER-DELTAKE hybrid model, an improved ACO-KM-ECC trust aware routing protocol based on ant colony optimization...

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Section: Energy Conservation and Balancing In Wsnmentioning

confidence: 99%

Swarm Intelligence-Based Bio-Inspired Framework for Wireless Sensor Networks

Naseer¹,

Neelima²,

Narsimha³

2021

Wireless Sensor Networks - Design, Deployment and Applications

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“…Through simulation results, the authors show that their proposed scheme improves network lifetime in comparison to traditional clustering schemes. They have extended their work in [9], where detailed analysis of their scheme is done with the help of other parameter values, such as the number of clusters, network stability, and average eccentricity. Most recently, Li et al [10] investigated the problem of constructing optimal clustering architecture in homogeneous WSNs for lifetime maximization.…”

Section: Literature Reviewmentioning

confidence: 99%

Lifetime Optimizing Clustering Structure Using Archimedes’ Spiral-Based Deployment in WSNs

Ghosal¹,

Halder²

2017

IEEE Systems Journal

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In wireless sensor networks (WSNs), preserving energy requires utmost attention, as they are highly resource constrained. Clustering is commonly considered as one of the efficient energy conservation techniques. In a clustered WSN, due to the nature of operation, cluster heads (CHs) near the sink bear the major share of data forwarding compared with the CHs far away from the sink, resulting in an energy hole problem. First, we have analyzed the optimization of network lifetime by balancing the energy consumption among different CHs. To meet the requirement of optimization of network lifetime, we have devised a routing-aware optimal clustering strategy. Further, keeping the network model in mind, we identify Archimedes' spiral, based on which a deployment function is proposed for distributing a member node and a CH. The performance of the optimal clustering strategy is evaluated in terms of energy balance and network lifetime. Simulation results show that our scheme provides satisfactory network performance in terms of end-to-end delay and throughput. Finally, all the results are compared with two competing schemes that confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics.Index Terms-Clustering, energy balance, network lifetime, node deployment, wireless sensor network.

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“…Since then, network formation algorithms based on clustering techniques are developed intensively. Nguyen [10], the very first distributed clustering algorithm providing non-overlapping multi-hop clusters with energy concerns. In [3], Cheng and Ganganath are the first who attempted to exploit the geographical locations of sensor nodes in order to facilitate the formation of DADCNS.…”

Section: Related Workmentioning

confidence: 99%

A k-Means-Based Formation Algorithm for the Delay-Aware Data Collection Network Structure

Tsoi

Cheng

Ganganath

2014

2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

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A wireless sensor network (WSN) consists of a large number of wireless sensor nodes that collect information from their sensing terrain. Wireless sensor nodes are, in general, battery-powered devices with limited processing and transmission power. Therefore, the lifetime of WSNs heavily depends on their energy efficiency. Multiple-cluster 2-hop (MC2H) network structure is commonly used in WSNs to reduce energy consumption due to long-range communications. However, networks with the MC2H network structure are commonly associated with long data collection processes. The delay-aware data collection network structure (DADCNS) is proposed to shorten the duration of data collection processes without sacrificing network lifetime. In this paper, a k-means-based formation algorithm for the DAD-CNS, namely DADCNS-RK, is proposed. The proposed algorithm can organize a network into the DADCNS, while minimizing the total communication distance among connected sensor nodes by performing k-means clustering recursively. Simulation results show that, when comparing with other DADCNSs formed by different algorithms, the proposed algorithm can reduce the total communication distances of networks significantly.

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Balancing Energy Consumption in Clustered Wireless Sensor Networks

Cited by 11 publications

References 18 publications

Swarm Intelligence-Based Bio-Inspired Framework for Wireless Sensor Networks

Swarm Intelligence-Based Bio-Inspired Framework for Wireless Sensor Networks

Lifetime Optimizing Clustering Structure Using Archimedes’ Spiral-Based Deployment in WSNs

A k-Means-Based Formation Algorithm for the Delay-Aware Data Collection Network Structure

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