A novel energy-aware bio-inspired clustering scheme for IoT communication

Zhang, Yefei; Wang, Yichuan

doi:10.1007/s12652-020-01704-w

Cited by 27 publications

(16 citation statements)

References 31 publications

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“…The efficiency of the T2FL-PSO is compared with those of the PSO-C and PSO-WZ algorithms using the same parameters [34]. The simulation is carried out in MATLAB 2019a [56][57][58][59][60][61][62] taking 100 random nodes in the network.…”

Section: Resultsmentioning

confidence: 99%

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T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Balasubramaniyam³

et al. 2021

IEEE Access

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In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%-15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.

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Section: Resultsmentioning

confidence: 99%

“…Zhang and Wang [34] proposed a bio-inspired CH selection in IoT. Sensor nodes are located, and the number of CH nodes is selected at random regions of the network.…”

Section: Related Workmentioning

confidence: 99%

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Balasubramaniyam³

et al. 2021

IEEE Access

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“…Proposal for a novel energy-aware bio-inspired clustering scheme (PSO-WZ) was given by Zhang & Wang [10]. At the start of this work, there is random initialization of the CHs combination and allocation of non-CHs depending on the division rules.…”

Section: Related Workmentioning

confidence: 99%

“…Update for this projectile will be by Eq. (10) as below: (10) In this equation, the updated lead projectile is denoted as , the old lead projectile is denoted as p L , and a function which will generate numbers randomly from the normal distribution with a mean μ and a standard deviation parameter σ is denoted as normran (μ, σ). Eq.…”

Section: Lightning Search Algorithm (Lsa)mentioning

confidence: 99%

Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network

Senthil

Raaza

Kumar

2021

Wireless Pers Commun

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Specialized transducers in Wireless Sensor Networks (WSNs) that offer sensing services to the Internet of Things (IoT) devices have limited storage and energy resources. One of the most vital issues in WSN design is power usage, as it is nearly impossible to recharge or replace sensor nodes' batteries. A prominent role in conserving power for energyconstrained networks is served by the clustering algorithm. It is possible to reduce network energy usage and network lifespan prolongation by proper balancing of the network load with Cluster Head (CH) election. The single-hop inter-cluster routing technique, in which there is a direct transfer from CHs to the Base Station (BS), is done by the Low Energy Adaptive Clustering Hierarchy (LEACH). However, for networks with large-regions, this technique is not viable. An optimized Orphan-LEACH (O-LEACH) has been proposed in this work to facilitate the formation of a novel process of clustering, which can result in minimized usage of energy as well as enhanced network longevity. Sufficient energy is possessed by the orphan node, which will attempt to be cover the network. The proposed work's primary novel contribution is the O-LEACH protocol that supplies the entire network's coverage with the least number of orphaned nodes and has extremely high connectivity rates. A hybrid optimization utilizing Simulated Annealing (SA) with Lightning Search Algorithm (LSA) (SA-LSA), and Particle Swarm Optimization (PSO) with LSA (PSO-LSA) Algorithm is proposed. These proposed techniques effectivelymanage the CH election achieving optimal path routing and minimization in energy usage, resulting in the enhanced lifespan of the WSN. The proposed technique's superior performance, when compared with other techniques, is confirmed from the outcomes of the experimentations.

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“…The IoT supported WSNs consist of sensing, data collection, accumulation, processing, and a communication unit. Data collection and accumulation of information in IoT devices should consume minimum energy [3].…”

Section: Introductionmentioning

confidence: 99%

MBSR: MIMO Based Sink Relocation for Path Selection in IoT Based WSN

Manasa¹,

Shaila²,

Venugopal

2020

SSRN Journal

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Wireless Sensor Networks have extended its functionalities by integrating with the Internet of Things and are highly proficient when it is incorporated with other technologies. This demands communication with lesser energy consumption and one such idea of low energy consumption is low energy path selection by a mobile sink node. Path selection requires a good Signal Interference Noise Ratio (SINR) which is calculated for the positioned mobile sink. Thus, a high-quality transmission path is selected by choosing a path to obtain better SINR. MBSR algorithm considers hierarchical clustering schemes and heads are elected to monitor the network. Heads of the clusters communicate to IoT applications using Multiple Input and Multiple Output Dual Antennas. The proposed MBSR scheme utilizes a path optimization technique and finds a suitable path to establish links to end user and realtime environments. The proposed MBSR mechanism employs three phases of execution. Phase 1: Cluster Creation, Phase 2: SINR Analysis for Path Selection. Phase 3: Data transmission to MIMO Devices and in turn forwards to static sinks that connect to end-user using the internet. MBSR improves transmission quality and the proposed scheme is tested for various network parameters to check transmission quality and is compared with existing methodologies.

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A novel energy-aware bio-inspired clustering scheme for IoT communication

Cited by 27 publications

References 31 publications

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network

MBSR: MIMO Based Sink Relocation for Path Selection in IoT Based WSN

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