A Distributed Multi-Hop Intra-Clustering Approach Based on Neighbors Two-Hop Connectivity for IoT Networks

Batta, Mohamed Sofiane; Mabed, Hakim; Aliouat, Zibouda; Harous, Saad

doi:10.3390/s21030873

Cited by 17 publications

(8 citation statements)

References 59 publications

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“…To extend the local view of the network we utilized TCR [43], which presents the connectivity ratio of a node with its neighborhood. To obtain the TCR, a node accesses its twohop neighbor node information.…”

Section: B Tcrmentioning

confidence: 99%

Distributed Energy-Efficient Clustering and Routing for Wearable IoT Enabled Wireless Body Area Networks

2023

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Recently, the wearable internet of things (WIoT) has brought a new dimension of connectivity to wireless body area networks (WBANs). For instance, the WIoT provides valuable functions, such as collecting, analyzing, and transmitting data for real-time health monitoring of medical services. However, designing a clustering and routing protocol is challenging because WIoT attributes are subject to high interference in dense deployment, social mobility, and limited energy. In a WIoT-enabled WBAN, cooperative control is the fundamental function of clustering and routing. Cluster-based routing protocols are preferred for WBAN because of their scalability. However, one-hop neighbor-based clustering approaches do not ensure connectivity and reachability owing to node mobility. The existing two-hop-based clustering protocols are mostly centralized-based approaches, which are unsuitable for dynamic WBANs. In this paper, we propose a distributed energy-efficient two-hop-based clustering and routing protocol (DECR) targeting WIoT-enabled WBAN. In DECR, in the cluster formation phase, each node obtains the information of its neighbor nodes within the two-hop range. We utilize the modified grey-wolf optimization algorithm for the cluster head (CH) selection and routing optimization. Node connectivity and residual energy were jointly considered when determining the CH in each cluster. We also developed an analytical model to determine the optimal number of clusters by considering intra-and inter-cluster transmission distances to reduce the overall transmission distance and number of transmissions. Finally, we proposed a routing algorithm to ensure energy-efficient packet delivery from CH to sink. Our simulation outcomes revealed that the proposed DECR significantly outperforms the existing clustering and routing protocols in various performance metrics.INDEX TERMS Wearable internet of things, clustering, two-hop range, distributed, cluster head, energyefficiency, grey wolf optimization, routing, wireless body area network (WBAN).

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Section: B Tcrmentioning

confidence: 99%

Distributed Energy-Efficient Clustering and Routing for Wearable IoT Enabled Wireless Body Area Networks

2023

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“…The lifespan of sensor nodes is restricted by the lifetime of their batteries. To preserve the energy and increase the lifespan of the wireless devices, many clustering algorithms have been proposed [2,7,8,21]. In this section, some of the relevant works in this field are reviewed.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the residual energy RE i of each node i is considered during the CHs election. To estimate the battery charge diminution, we used the energy consumption model proposed in [7].…”

Section: Remaining Energy (Re I )mentioning

confidence: 99%

A Distributed Energy-Efficient Unequal Clustering based Kruskal Heuristic for IoT Networks

Batta¹,

Aliouat²,

Mabed³

et al. 2022

Machine Learning &Amp; Applications

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Energy efficiency is a major concern and a critical issue for energy constrained wireless networks. In this context, clustering is commonly used for topology management and maximizing the network lifetime. Clustering approaches typically use a multi-hopping mechanism where Cluster Heads (CHs) near the Base Station (BS) consume higher energy since they relay data of farther CHs. Therefore, nodes close to the BS are strangled with an overloaded routing task and tend to die earlier than their intended lifetime, which affects the network performance. This situation is known as the hot spot problem that induces unbalanced energy consumption among CHs. The concern in this work is to address the intra-clustering structure in large scale environments to tolerate the network scaling and reasonably balance the energy consumption among CHs. In this regard, we propose a new Unequal Clustering algorithm based on Kruskal heuristic (UCKA) to optimize the network lifetime. UCKA applies the Kruskal heuristic in a distributed fashion to perform a minimum spanning tree within large cluster which strengthen the intracluster routing structure and reduce the energy devoted to wireless communications. To the best of our knowledge, this is the first solution that combines the Kruskal heuristic and the unequal clustering to extend the devices durability and alleviate the hot spot problem. Simulation results indicate that UCKA can effectively reduce the energy consumption and lengthen the network lifetime.

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“…Several studies on the efficient use of network energy have been proposed in the field of wireless networks [4], [8], [9]. Research community developed several topologies for routing protocols to address the energy consumption problem [4].…”

Section: Related Workmentioning

confidence: 99%

State of Health Optimization Based Unequal Clustering in IoT Networks

Batta

Mabed

Aliouat

2022

2022 International Wireless Communications and Mobile Computing (IWCMC)

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Energy optimization is an imminent worldwide issues for green computing, it constitutes a major concern and a critical aspect especially for energy constrained wireless networks. To overcome this issue, clustering techniques were introduced as a prominent method that arranges the system operation in correlated manner to attend the energy preservation and prolong the network lifespan. However, existing clustering works only focus on preserving the battery charge to operate until it drains out. This approach is most appropriate for nonrechargeable batteries. However, rechargeable batteries become commonly used and need to be considered. The full discharge of rechargeable battery does not mean the device obsolescence. Therefore, the system lifetime optimization should take into consideration the degradation of the rechargeable batteries performances. In this context, we proposed an improved long-term energy efficient unequal clustering approach based on the battery state of health for IoT networks (ILEC SOH). This work represents an initial step in the integration of the battery health degradation into the unequal network clustering. The obtained results show that the consideration of battery state of health (SOH) significantly improve the network lifespan in the long term compared to the conventional energy efficient approaches.

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A Distributed Multi-Hop Intra-Clustering Approach Based on Neighbors Two-Hop Connectivity for IoT Networks

Cited by 17 publications

References 59 publications

Distributed Energy-Efficient Clustering and Routing for Wearable IoT Enabled Wireless Body Area Networks

Distributed Energy-Efficient Clustering and Routing for Wearable IoT Enabled Wireless Body Area Networks

A Distributed Energy-Efficient Unequal Clustering based Kruskal Heuristic for IoT Networks

State of Health Optimization Based Unequal Clustering in IoT Networks

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