Towards Energy Efficient Clustering in Wireless Sensor Networks: A Comprehensive Review

Merabtine, Nassima; Djenouri, Djamel; Zegour, Djamel-Eddine

doi:10.1109/access.2021.3092509

Cited by 51 publications

(40 citation statements)

References 61 publications

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“…Clustering is one of the main methods used to divide a network into a number of groups (clusters), with one node designated as the cluster head (CH) for each cluster [ 46 ]. Intuitively, a cluster in the SFPCR scheme is precisely defined as a set of density-connected LoRa devices in a particular region.…”

Section: Sf-partition-based Clustering and Relaying (Sfpcr) Schemementioning

confidence: 99%

SF-Partition-Based Clustering and Relaying Scheme for Resolving Near–Far Unfairness in IoT Multihop LoRa Networks

Mugerwa

Nam

Choi

et al. 2022

Sensors

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Long range (LoRa) is one of the most successful low-power wide-area networking technologies because it is ideally suited for long-distance, low-bit rate, and low-power communications in the unlicensed sub-GHz spectrum utilized for Internet of things (IoT) networks. The effectiveness of LoRa depends on the link budget (i.e., spreading factor (SF), bandwidth (BW), and transmission power (TX)). Due to the near–far effect, the allocation of a link budget to LoRa devices (LDs) in large coverage regions is unfair between them depending on their distance to the GW. Thus, more transmission opportunities are given to some LDs to the detriment of other LD’s opportunities. Numerous studies have been conducted to address the prevalent near–far fairness problem. Due to the absence of a tractable analytical model for fairness in the LoRa network, however, it is still difficult to solve this problem completely. Thus, we propose an SF-partition-based clustering and relaying (SFPCR) scheme to achieve enormous LD connectivity with fairness in IoT multihop LoRa networks. For the SF partition, the SFPCR scheme determines the suitable partitioning threshold point for bridging packet delivery success probability gaps between SF regions, namely, the lower SF zone (LSFZ) and the higher SF zone (HSFZ). To avoid long-distance transmissions to the GW, the HSFZ constructs a density-based subspace clustering that generates clusters of arbitrary shape for adjacent LDs and selects cluster headers by using a binary score representation. To support reliable data transmissions to the GW by multihop communications, the LSFZ offers a relay LD selection that ideally chooses the best relay LD to extend uplink transmissions from LDs in the HSFZ. Through simulations, we show that the proposed SFPCR scheme exhibits the highest success probability of 65.7%, followed by the FSRC scheme at 44.6%, the mesh scheme at 34.2%, and lastly the cluster-based scheme at 29.4%, and it conserves the energy of LDs compared with the existing schemes.

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Section: Sf-partition-based Clustering and Relaying (Sfpcr) Schemementioning

confidence: 99%

SF-Partition-Based Clustering and Relaying Scheme for Resolving Near–Far Unfairness in IoT Multihop LoRa Networks

Mugerwa

Nam

Choi

et al. 2022

Sensors

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“…Users clustering has also been investigated to improve spectral and energy network efficiency [51]- [53]. In [51], a two-step clustering scheme first divides small cells into disjoint cell clusters according to the neighboring relationship and then the UEs in each cell cluster are further grouped into UE groups with the target of minimizing intra-cluster interference.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…A statistical framework has been proposed in [52] to improve both spectral and energy efficiency, being the cluster size sensitive to changes in user and BS densities. In [53], the authors propose users clustering to enhance energy efficiency by lowering signaling overhead. In this work, the user with the best channel quality will communicate with the cellular BS on behalf of the whole cluster to reduce overhead and minimize energy consumption.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Unsupervised Clustering for 5G Network Planning Assisted by Real Data

et al. 2022

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The fifth-generation (5G) of networks is being deployed to provide a wide range of new services and to manage the accelerated traffic load of the existing networks. In the present-day networks, data has become more noteworthy than ever to infer about the traffic load and existing network infrastructure to minimize the cost of new 5G deployments. Identifying the region of highest traffic density in megabyte (MB) per km 2 has an important implication in minimizing the cost per bit for the mobile network operators (MNOs). In this study, we propose a base station (BS) clustering framework based on unsupervised learning to identify the target area known as the highest traffic cluster (HTC) for 5G deployments. We propose a novel approach assisted by real data to determine the appropriate number of clusters k and to identify the HTC. The algorithm, named as NetClustering, determines the HTC and appropriate value of k by fulfilling MNO's requirements on the highest traffic density MB/km 2 and the target deployment area in km 2 . To compare the appropriate value of k and other performance parameters, we use the Elbow heuristic as a benchmark. The simulation results show that the proposed algorithm fulfills the MNO's requirements on the target deployment area in km 2 and highest traffic density MB/km 2 with significant cost savings and achieves higher network utilization compared to the Elbow heuristic. In brief, the proposed algorithm provides a more meaningful interpretation of the underlying data in the context of clustering performed for network planning.

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“…The various features of a WSN attract a large number of customers daily; however, the increasing volume results in multiple challenges in sending and receiving packets. 5 In these types of networks, routing is highly intricate, and various ad hoc routing protocols have evolved to manage these complexities. The dynamic change in the topology and unsupervised behavior makes designing of efficient routing protocols complex.…”

Section: Introductionmentioning

confidence: 99%

“…WSNs have had a massive impact in the global era, resulting in the rapid growth of customers globally. The various features of a WSN attract a large number of customers daily; however, the increasing volume results in multiple challenges in sending and receiving packets 5 . In these types of networks, routing is highly intricate, and various ad hoc routing protocols have evolved to manage these complexities.…”

Section: Introductionmentioning

confidence: 99%

An optimized ticket manager based energy‐aware multipath routing protocol design for IoT based wireless sensor networks

Roberts¹,

Thangavel²

2022

Concurrency and Computation

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Recently, wireless sensor networks (WSNs) and Internet of Things (IoTs) have emanated as an indispensable assets that play a critical role in revolutionizing the field of data communication. Owing to the evolution of communication standards, research trends in IoT based wireless sensor networks have been rapidly progressing toward achieving effective data routing with a prolonged network lifetime and minimized energy consumption. In this article, an optimized ticket manager based energy-aware multipath routing protocol (TMERP) is proposed. The proposed protocol design comprises three important functional entities: ticket manager (TM), routing planner (RP), and backup node (BN). The TM is responsible for controlling and monitoring all the constraints related to networking. Then, the RP minimizes the overall complexity of the optimal resource allocation by avoiding an end-to-end delay. Finally, the BN facilitates efficient data routing through the optimal selection of routing paths using the node trust evaluation and backup process to minimize data loss. Hence, the proposed multipath routing system has a distinct advantage in enhancing the network lifetime constraint with minimal energy consumption owing to the collective performance of its functional entities. The simulation results of the experimental studies show that the proposed protocol design achieved an improved performance in terms of network energy, throughput, and network operational lifetime by 39.3%, 47.9%, and 10.5%, respectively when compared with similar existing protocols.

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Towards Energy Efficient Clustering in Wireless Sensor Networks: A Comprehensive Review

Cited by 51 publications

References 61 publications

SF-Partition-Based Clustering and Relaying Scheme for Resolving Near–Far Unfairness in IoT Multihop LoRa Networks

SF-Partition-Based Clustering and Relaying Scheme for Resolving Near–Far Unfairness in IoT Multihop LoRa Networks

Unsupervised Clustering for 5G Network Planning Assisted by Real Data

An optimized ticket manager based energy‐aware multipath routing protocol design for IoT based wireless sensor networks

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