Energy Efficient Cluster based Routing Protocol with Secure IDS for IoT Assisted Heterogeneous WSN

Alkhliwi, Sultan

doi:10.14569/ijacsa.2020.0111162

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Integrating such techniques will preserve stability of the network and efficiently enhances the routing performance along with the reward. Alkhliwi [20] presented an energy-efficient cluster-related routing protocol with secure IDS (EECRP-SID) for WSN. This aforementioned approach includes three main stages; they are intrusion detection, cluster construction, and optimal path selection.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Muddy Soil Fish Optimization-Based Energy Aware Routing in IoT-Assisted Wireless Sensor Networks

et al. 2023

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The seamless operation of interconnected smart devices in wireless sensor networks (WSN) and the Internet of Things (IoT) needs continuously accessible end-to-end routes. However, the sensor node (SN) relies on a limited power source and tends to cause disconnection in multi-hop routes because of a power shortage in the WSN, eventually leading to the inefficiency of the total IoT network. Furthermore, the density of available SNs affects the existence of feasible routes and the level of path multiplicity in the WSN. Thus, an effective routing model is predictable to extend the lifetime of WSN by adaptively choosing the better route for the data transfers between interconnected IoT devices. This study develops a Hybrid Muddy Soil Fish Optimization-based Energy Aware Routing Scheme (HMSFO-EARS) for IoT-assisted WSN. The presented HMSFO-EARS technique majorly focuses on the identification of optimal routes for data transmission in the IoT-assisted WSN. To accomplish this, the presented HMSFO-EARS technique involves the integration of the MSFO algorithm with the Adaptive β-Hill Climbing (ABHC) concept. Moreover, the presented HMSFO-EARS technique derives a fitness function for maximizing the lifespan and minimizing energy consumption. To demonstrate the enhanced performance of the HMSFO-EARS technique, a series of experiments was performed. The simulation results indicate the better performance of the HMSFO-EARS algorithm over other recent approaches with reduced energy consumption, less delay, high throughput, and extended network lifetime.

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Section: Related Workmentioning

confidence: 99%

Hybrid Muddy Soil Fish Optimization-Based Energy Aware Routing in IoT-Assisted Wireless Sensor Networks

et al. 2023

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“…Alkhliwi et al 98 proposed an energy‐efficient secure hierarchical routing protocol for IoT‐assisted heterogeneous IoS. The proposed system has three key phases, including cluster formation, optimal path selection, and intrusion detection.…”

Section: Solutionsmentioning

confidence: 99%

“…-All of the fuzzy logic type II-based solutions consider energy efficiency. [98][99][100] Meanwhile, the work proposed in Adnan et al 99 handles load-balancing as well as unequal clustering, which results in a better performance in terms of energy consumption and network durability.…”

Section: Node Clusteringmentioning

confidence: 99%

Machine learning‐based clustering protocols for Internet of Things networks: An overview

Merah

Aliouat

Harbi

et al. 2023

Int J Communication

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Summary The Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML‐based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology‐centered ML‐based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML‐based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues.

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“…Alkhliwi [14] presents an energy-effective cluster-based routing mechanism with secured IDS in HWSN named EECRP-SID. In the initial stage, the T2FC protocol with 2 input parameters can be employed for the CHS model.…”

Section: Related Workmentioning

confidence: 99%

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

Alruwaili,

Asiri,

Alrayes

et al. 2023

IEEE Access

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The Internet of Things (IoT) based Wireless Sensor Networks (WSNs) contain interconnected autonomous sensor nodes (SN), which wirelessly communicate with each other and the wider internet structure. Intrusion detection to secure IoT-based WSNs is critical for identifying and responding to great security attacks and threats that can cooperate with the integrity, availability, and privacy of the network and its data. Machine learning (ML) algorithms are deployed for detecting difficult patterns and subtle anomalies in IoT data. Artificial intelligence (AI) driven methods are learned and adapted from novel data for improving detection accuracy over time. In this article, we introduce a Red Kite Optimization Algorithm with an Average Ensemble Model for Intrusion Detection (RKOA-AEID) technique for Secure IoT-based WSN. The purpose of the RKOA-AEID methodology is to accomplish security solutions for IoT-assisted WSNs. To accomplish this, the RKOA-AEID technique performs pre-processing to scale the input data using min-max normalization. In addition, the RKOA-AEID technique performs an RKOA-based feature selection approach to elect an optimum set of features. For intrusion detection, an average ensemble learning model is used. Finally, the Lévy-fight chaotic whale optimization Algorithm (LCWOA) can be executed for the optimum hyperparameter chosen for the ensemble models. The performance evaluation of the RKOA-AEID algorithm can be tested on the benchmark WSN-DS dataset. The extensive experimental outcomes stated the higher outcome of the RKOA-AEID algorithm with other approaches with an improved accuracy of 98.94%.

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Energy Efficient Cluster based Routing Protocol with Secure IDS for IoT Assisted Heterogeneous WSN

Cited by 4 publications

References 23 publications

Hybrid Muddy Soil Fish Optimization-Based Energy Aware Routing in IoT-Assisted Wireless Sensor Networks

Hybrid Muddy Soil Fish Optimization-Based Energy Aware Routing in IoT-Assisted Wireless Sensor Networks

Machine learning‐based clustering protocols for Internet of Things networks: An overview

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

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