Low Energy-Efficient Clustering and Routing Based on Genetic Algorithm in WSNs

Hamidouche, Ranida; Aliouat, Zibouda; Guéroui, Abdelhak Mourad

doi:10.1007/978-3-030-03101-5_14

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…After the clustering phase, the LER-GA 35,36 protocol is used to transfer the node's sensed data to its affected chef. The cluster is partitioned into zones and regions.…”

Section: Transmissions Intra-clustersmentioning

confidence: 99%

Optimized data gathering in a heterogeneous Internet of Things network

Hamidouche

Aliouat

Ari

et al. 2021

Int J Communication

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Recent research introduces data gathering using mobile data collectors to conserve energy and elevate the hotspot problem. However, many of them suffer from buffer overflow due to the limited memory capacity of objects, even more, when dealing with heterogeneous Internet of Things (IoT) devices. Emerging bio-inspiration technologies provide a novel direction for data collection and make it more intelligent and available. In this paper, we present an intelligent data gathering schema by taking into consideration buffer overflow called Center Gravity Mobile data collector based on Salp Swarm Algorithm (CGMSSA).To conduct data collection to improve the network performance, we adopt a hybridized distributed bio-inspired technique to elect chefs and salp swarm intelligence to control data collector movement. First, we select chefs using grey wolf optimization. Then, several groups are formed. Then, a mobile data collector is adopted to access chefs, following the buffer overflow value and gather information. Considerable experiments are conducted to demonstrate that our solution can efficiently enhance the lifetime of the network and increase data throughput.

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“…After the clustering phase, the LER-GA 35,36 protocol is used to transfer the node's sensed data to its affected chef. The cluster is partitioned into zones and regions.…”

Section: Transmissions Intra-clustersmentioning

confidence: 99%

Optimized data gathering in a heterogeneous Internet of Things network

Hamidouche

Aliouat

Ari

et al. 2021

Int J Communication

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“…-Compute the total cost of all empires -Pick the weakest colony from the weakest empire and give it to the empire that is more likely to possess it (Imperialistic competition) if There is an empire with no colonies then if This country has much wealth then -Elect this country as Dependent else -The country requests help from the closest colony. end if end if -iteration = iteration + 1 end while return Colonies for each imperialist G. DATA TRANSMISSION After the clustering phase, the nodes send their data to the concerned CH as in the LER-GA [6], [7] protocol. The cluster, in this case, is subdivided into regions and zones.…”

Section: ) Generating Initial Empiresmentioning

confidence: 99%

An Efficient Clustering Strategy Avoiding Buffer Overflow in IoT Sensors: A Bio-Inspired Based Approach

et al. 2019

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The Internet of Things (IoT) invention has taken the growth of sensors technology to a completely high step. New challenges in terms of data delivery have emerged due to strict QoS conditions. Among the solutions proposed in the literature is the subdivision of the large-scale network into several clusters. Except that most of these solutions are conventional. However, prior research generally confirms that bio-inspired paradigms are more flexible and effective compared to traditional methods. When it comes to a heterogeneous network, additional constraints appear. Nodes have different buffer sizes. Then, data captured must be sent before their buffers are full, otherwise, some data will be lost. This is not suitable for a real-time application where time and information are crucial elements. In this study, a comprehensive overview of the use of sensors in IoT contexts is performed. Two algorithms as Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA), combined with the Imperialist Competitive Algorithm (ICA) based Cluster Head (CH) selection with a novel approach for heterogeneous networks are proposed. These algorithms can support data exchange over a heterogeneous Wireless Sensor Network (WSN) infrastructure with taking into consideration the buffer overflow problem. Simulation results are presented and discussed in different network designs. The research demonstrated that knowing well how to manage buffers using bio-inspired techniques, leads to a significant reduction in data loss.INDEX TERMS Bio-inspired optimization, heterogeneous WSN, CH selection, IoT, model checking.

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“…WSN technology, when compared to existing environmental monitoring techniques, is a green energy-based technology that is more favorable for future applications in effectively detecting environmental fluctuation [ 7 ]. WSNs used for environmental monitoring are made up of excess battery-operated nodes that are closely distributed in inaccessible or remote locations [ 8 ]. The fundamental problem in WSNs, however, is the sensor nodes’ limited power resources.…”

Section: Introductionmentioning

confidence: 99%

Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network

Natarajan¹,

Megharaj

Marchewka

et al. 2022

Sensors

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In modern trends, wireless sensor networks (WSNs) are interesting, and distributed in the environment to evaluate received data. The sensor nodes have a higher capacity to sense and transmit the information. A WSN contains low-cost, low-power, multi-function sensor nodes, with limited computational capabilities, used for observing environmental constraints. In previous research, many energy-efficient routing methods were suggested to improve the time of the network by minimizing energy consumption; sometimes, the sensor nodes run out of power quickly. The majority of recent articles present various methods aimed at reducing energy usage in sensor networks. In this paper, an energy-efficient clustering/routing technique, called the energy and distance based multi-objective red fox optimization algorithm (ED-MORFO), was proposed to reduce energy consumption. In each communication round of transmission, this technique selects the cluster head (CH) with the most residual energy, and finds the optimal routing to the base station. The simulation clearly shows that the proposed ED-MORFO achieves better performance in terms of energy consumption (0.46 J), packet delivery ratio (99.4%), packet loss rate (0.6%), end-to-end delay (11 s), routing overhead (0.11), throughput (0.99 Mbps), and network lifetime (3719 s), when compared with existing MCH-EOR and RDSAOA-EECP methods.

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Low Energy-Efficient Clustering and Routing Based on Genetic Algorithm in WSNs

Cited by 8 publications

References 8 publications

Optimized data gathering in a heterogeneous Internet of Things network

Optimized data gathering in a heterogeneous Internet of Things network

An Efficient Clustering Strategy Avoiding Buffer Overflow in IoT Sensors: A Bio-Inspired Based Approach

Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network

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