Relocation routing for energy balancing in mobile sensor networks

Deng, Yiping; Chen, Lin; Wu, Dapeng; Ren, Fengyuan

doi:10.1002/wcm.2410

Cited by 2 publications

(3 citation statements)

References 35 publications

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“…One group is based on static node scheduling [55,56], and the schemes in this group mostly adopt sleep scheduling to reduce the energy consumption of WSNs. The other group is based on mobile node scheduling [57,58], which optimizes the network lifetime by moving nodes on purpose.…”

Section: Energy-efficient Scheduling-based Routing Algorithmsmentioning

confidence: 99%

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An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks

2021

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Machine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (WSNs) has attracted much attention. As resources are limited in WSNs, identifying how to improve resource utilization and achieve power-efficient load balancing is becoming a critical issue in WSNs. Traditional green routing algorithms aim to achieve this by reducing energy consumption and prolonging network lifetime through optimized routing schemes in WSNs. However, there are usually problems such as poor flexibility, a single consideration factor, and a reliance on accurate mathematical models. ML techniques can quickly adapt to environmental changes and integrate multiple factors for routing decisions, which provides new ideas for intelligent energy-efficient routing algorithms in WSNs. In this paper, we survey and propose a theoretical hypothetic model formulation of ML as an effective method for creating a power-efficient green routing model that can overcome the limitations of traditional green routing methods. In addition, the study also provides an overview of past, present, and future progress in green routing schemes in WSNs. The contents of this paper will appeal to a wide range of audiences interested in ML-based WSNs.

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Section: Energy-efficient Scheduling-based Routing Algorithmsmentioning

confidence: 99%

“…In order to save power efficiently, a global energy balancing routing protocol (GEBRP) was proposed [57], which adopts the virtual grid-based network model. In GEBRP, node mobility scheduling includes two phases: the diffusion phase and supplementary phase.…”

Section: Mobile Node Scheduling-based Schemesmentioning

confidence: 99%

An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks

2021

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“…Energy‐use efficiency is always a primary issue in WSNs because most sensor nodes are typically battery‐powered and difficult or impossible to replenish. Many research work has been carried out to pursue high energy‐use efficiency . Duty‐cycled sleep scheduling is an effective and widely used strategy for energy saving and consequently network lifetime prolongation.…”

Section: Introductionmentioning

confidence: 99%

A gradient‐based multiple‐path routing protocol for low duty‐cycled wireless sensor networks

Hao

Zheng

Huang

et al. 2014

Wireless Communications

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Routing in a low duty-cycled wireless sensor network (WSN) has attracted much attention recently because of the challenge that low duty-cycled sleep scheduling brings to the design of efficient distributed routing protocols for such networks. In a low duty-cycled WSN, a big problem is how to design an efficient distributed routing protocol, which uses only local network state information while achieving low end-to-end (E2E) packet delivery delay and also high packet delivery efficiency. In this paper, we study low duty-cycled WSNs wherein sensor nodes adopt pseudorandom sleep scheduling for energy saving. The objective of this paper is to design an efficient distributed routing protocol with low overhead. For this purpose, we design a simple but efficient hop-by-hop routing protocol, which integrates the ideas of multipath routing and gradient-based routing for improved routing performance. We conduct extensive simulations, and the results demonstrate the high performance of the proposed protocol in terms of E2E packet delivery latency and packet delivery efficiency as compared with existing protocols.

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Relocation routing for energy balancing in mobile sensor networks

Cited by 2 publications

References 35 publications

An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks

An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks

A gradient‐based multiple‐path routing protocol for low duty‐cycled wireless sensor networks

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