Energy efficiency cooperative scheme for cluster-based capillary networks in Internet of Things systems

Song, Liumeng; Chai, Kok Keong; Chen, Yue; Schormans, J.A.

doi:10.1109/pimrc.2016.7794904

Cited by 1 publication

(1 citation statement)

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“…They use a scheduling model that can react to different data flow changes in routers (CHs) by re-ordering active periods of clusters and optimizing the time-slot to access shared channels by each cluster. In [271], the authors introduce a multiple-input-single-output (CMISO) method to prolong the lifetime of CHs. In the paper, they formulate the cooperative coalition selection for CMISO and use a quantum-inspired particle swarm optimization technique to select the optimum cooperative coalition.…”

Section: Physical Layer Supportmentioning

confidence: 99%

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Shahraki

Taherkordi

Haugen

et al. 2021

IEEE Trans. Netw. Serv. Manage.

120

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Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this paper, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.

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Section: Physical Layer Supportmentioning

confidence: 99%