Sink Mobility Based on Bacterial Foraging Optimization Algorithm

Hamidouche, Ranida; Khentout, Manel; Aliouat, Zibouda; Guéroui, Abdelhak Mourad; Ari, Ado Adamou Abba

doi:10.1007/978-3-319-89743-1_31

Cited by 5 publications

(5 citation statements)

References 12 publications

(16 reference statements)

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“…Then, it is compared with the original SSA [6] to study the effects and the advantage that the α parameter adds to the algorithm. Finally, it is compared to the newest swarm algorithm named SMBFOA [9].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Their objective function was to minimize the length L(T) of a traveling-path constituted by several visited points T. The mobile sink visits each visiting point only once in each collection phase. Hamidouche et al [9] presented a new pattern of Sink Mobility based on the Bacterial Foraging Optimization Algorithm (SMBFOA). The proposed protocol takes into account new characteristics inspired by the autonomous and intelligent movement of the Escherichia coli bacterium.…”

Section: Related Workmentioning

confidence: 99%

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Mobile Sink Path Planning in Heterogeneous IoT Sensors: a Salp Swarm Algorithm Scheme

2021

KSII TIIS

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To assist in data collection, the use of a mobile sink has been widely suggested in the literature. Due to the limited sensor node's storage capacity, this manner to collect data induces huge latencies and drop packets. Their buffers will be overloaded and lead to network congestion. Recently, a new bio-inspired optimization algorithm appeared. Researchers were inspired by the swarming mechanism of salps and thus creating what is called the Salp Swarm Algorithm (SSA). This paper improves the sink mobility to enhance energy dissipation, throughput, and convergence speed by imitating the salp's movement. The new approach, named the Mobile Sink based on Modified Salp Swarm Algorithm (MSSA), is approved in a heterogeneous Wireless Sensor Network (WSN) data collection. The performance of the MSSA protocol is assessed using several iterations. Results demonstrate that our proposal surpass other literature algorithms in terms of lifespan and throughput.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Mobile Sink Path Planning in Heterogeneous IoT Sensors: a Salp Swarm Algorithm Scheme

2021

KSII TIIS

View full text Add to dashboard Cite

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“…To design such a topology, several solutions have been proposed in the literature such as clustering and backbone [28]. Several techniques have been proposed to significantly increase the lifetime of cluster-based networks by partitioning the network into groups such that the intergroup distance is less than the extra-group distance [4,29]. Each network group is managed by a Cluster Head (CH).…”

Section: Big Data Dimensions and Analysis Tools Related To Wsnsmentioning

confidence: 99%

A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection

et al. 2020

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Large Scale Wireless Sensor Networks (LS-WSNs) are Wireless Sensor Networks (WSNs) composed of an impressive number of sensors, with inherent detection and processing capabilities, to be deployed over large areas of interest. The deployment of a very large number of diverse or similar sensors is certainly a common practice that aims to overcome frequent sensor failures and avoid any human intervention to replace them or recharge their batteries, to ensure the reliability of the network. However, in practice, the complexity of LS-WSNs pose significant challenges to ensuring quality communications in terms of symmetry of radio links and maximizing network life. In recent years, most of the proposed LS-WSN deployment techniques aim either to maximize network connectivity, increase coverage of the area of interest or, of course, extend network life. Few studies have considered the choice of a good LS-WSN deployment strategy as a solution for both connectivity and energy consumption efficiency. In this paper, we designed a LS-WSN as a tool for collecting big data generated by smart cities. The intrinsic characteristics of big data require the use of heterogeneous sensors. Furthermore, in order to build a heterogeneous LS-WSN, our scientific contributions include a model of quantifying the kinds of sensors in the network and the multi-level architecture for LS-WSN deployment, which relies on clustering for the big data collection. The results simulations show that our proposed LS-WSN architecture is better than some well known WSN protocols in the literature including Low Energy Adaptive Clustering Hierarchy (LEACH), E-LEACH, SEP, DEEC, EECDA, DSCHE and BEENISH.

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“…The evolution of microelectronics in the last decades allowed the development of low cost and low power sensors, which can be deployed on a large-scale environment to collectively gather information from the deployed field and wirelessly deliver the gathered data to a remote computer for computing and storage purposes [5], [8]- [10]. This cycle is summarized in figure 1.…”

Section: Introductionmentioning

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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Sink Mobility Based on Bacterial Foraging Optimization Algorithm

Cited by 5 publications

References 12 publications

Mobile Sink Path Planning in Heterogeneous IoT Sensors: a Salp Swarm Algorithm Scheme

Mobile Sink Path Planning in Heterogeneous IoT Sensors: a Salp Swarm Algorithm Scheme

A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection

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

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