An Enhanced Heterogeneous Gateway-Based Energy-Aware Multi-Hop Routing Protocol for Wireless Sensor Networks

Jibreel, Fuseini; Tuyishimire, Emmanuel; Daabo, Mohammed Ibrahim

doi:10.3390/info13040166

Cited by 17 publications

(15 citation statements)

References 28 publications

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“…(4) An Enhanced Heterogeneous Gateway-Based Energy-Aware Multihop Routing Protocol for Wireless Sensor Networks [50]. Key points: this work proposed an energyefficient routing protocol for the WSN environment.…”

Section: Deterministic Approachmentioning

confidence: 99%

Energy Optimization-Based Clustering Protocols in Wireless Sensor Networks and Internet of Things-Survey

Prasad

Sasikumar

2023

International Journal of Distributed Sensor Networks

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Research on popular themes today is mainly concentrated on cutting-edge home applications made up of Internet of Things gadgets. As its principal means of sensing, wireless sensor networks are a component of the Internet of Things. Tracking and monitoring applications benefit from the use of sensor nodes. Every step in the data collection, processing, and transmission processes carried out by wireless sensor nodes takes energy. Small capacity batteries on the sensor nodes in the networks make charging them frequently impractical. Energy optimization is required for sensor nodes since there is no other option but to replace the nodes. Clustering is a well-known and effective solution to increase the energy efficiency of the sensor nodes among the various routing techniques. The closest route between the cluster head node and the base station is thus determined using routing techniques in order to manage energy.

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Section: Deterministic Approachmentioning

confidence: 99%

Energy Optimization-Based Clustering Protocols in Wireless Sensor Networks and Internet of Things-Survey

Prasad

Sasikumar

2023

International Journal of Distributed Sensor Networks

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“…Therefore, the MNs are detected using RMCV and DL models. Furthermore, the LNs are registered in the blockchain and routing is performed again for the LNs using LEACH and HMGEAR protocols [39], [40]. The following steps present the detailed working of the proposed solution, also shown in Fig.…”

Section: Routing In the Wsnmentioning

confidence: 99%

A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

Khan

Amjad

Ahmed³

et al. 2023

IEEE Access

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Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-ofwork in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.

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“…In [24], the authors propose an enhanced energyefficient clustering mechanism that depends on the electrostatic discharge approach. This enables the implementation of a fully connected network with the shortest path between the nodes and the cluster head (CH).…”

Section: Related Work and Motivationmentioning

confidence: 99%

Efficient Energy Mechanism in Heterogeneous WSNs for Underground Mining Monitoring Applications

et al. 2022

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Wireless Sensor Networks (WSNs) play an important role in underground mining applications. In particular, they help to collect information using sensors and provide monitoring of complex mine environments to avoid potential risks and manage operations. Despite the importance of WSNs, they face the problem of energy consumption and the difficulty of replacing the batteries of the sensor nodes. The distributed energy efficient aggregation protocol (DEECP) helps to reduce the power consumption of the WSN. This protocol enables an increase in the lifetime of a WSN. The DEECP algorithm uses the clustering concept and selects cluster heads (CHs) according to the election probability based on the ratio between the residual energy and network average energy of each node. However, this method does not provide an optimum solution because it does not take into account the different sensor energy levels. In addition, the algorithm does not consider the effect of the distance between the base station and sensor node likely be chosen to become a CH. This can significantly affect the performance of the WSN. This paper proposes an optimization threshold for CH selection based on three energy levels of a sensor, namely, low, high, and super as well as the measurement of the distances between base stations and possible nodes be selected as CHs to optimize the CH selection method. The proposed approach is evaluated and compared with DEECP in terms of dead nodes, alive nodes, and network throughput. The results show that the proposed approach outperforms DEECPs in terms of network stability and lifetime.

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An Enhanced Heterogeneous Gateway-Based Energy-Aware Multi-Hop Routing Protocol for Wireless Sensor Networks

Cited by 17 publications

References 28 publications

Energy Optimization-Based Clustering Protocols in Wireless Sensor Networks and Internet of Things-Survey

Energy Optimization-Based Clustering Protocols in Wireless Sensor Networks and Internet of Things-Survey

A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

Efficient Energy Mechanism in Heterogeneous WSNs for Underground Mining Monitoring Applications

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