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

Hao, Jie; Zheng, Yao; Huang, Kui; Zhang, Baoxian; Li, Cheng

doi:10.1002/wcm.2552

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…To meet the different requirements of applications, optimizing data flow-based routing algorithms provides efficient solutions, which are classified into two categories based on the number of paths from the source node to the destination node. One is single path-based routing algorithms [59,60], and the other is multi-path-based routing algorithms [61,62].…”

Section: Optimizing Data Flow-based Routing Algorithmsmentioning

confidence: 99%

“…To construct multiple path segments according to the gradient information of nodes, the gradient-based multi-path routing protocol (GMRP) is proposed [61]. The node chooses the path segment that can forward packets to the sink fastest as its next path segment.…”

Section: Multi-path-based Schemesmentioning

confidence: 99%

“…EBRP [59] Delay, residual energy, energy density EESPEED [60] Delay, residual energy, forwarding rate GMRP [61] Delay RSML [62] Routing quality, supporting time…”

Section: Scheme Energy Consumptionmentioning

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: Optimizing Data Flow-based Routing Algorithmsmentioning

confidence: 99%

Section: Multi-path-based Schemesmentioning

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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“…FSG establishes safe routes for data transmission by aiding the BS to translate the complete hop information into gradient data. Migabo et al in [41] [42] suggested the GMPR (Gradient-based multipath routing protocol), which is targeted at reinforcing decreased node failures and improved transmission performances through duty-cycling. It applies the sensory reports to gauge the distances of neighboring nodes.…”

Section: Query-driven Routing Solutionsmentioning

confidence: 99%

Lifetime-enhancement Schemes for Power-constrained Routing in Wireless Sensor Networks: A Survey

2021

IJETER

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Notwithstanding the broad applicability of wireless sensor network (WSN), the successful development and deployment in various real-world applications is currently challenging. The node-devices are powered by constrained-batteries that are typically impossible to substitute after deployments. Therefore, WSNs are energy-constrained and liable to early failure while expected to run for long-terms without battery replenishments.Diverse researchers have recently presented specific lifetime enhancement routing solutions based on data aggregation technology targeted at reducing energy-consumption,and improving the communication reliability of various WSNs.This paper presents a brief survey of the research field of data aggregation-ware routingin energy-constrained WSN. The paper overviews and classifies various existing routing solutions based on their delivery models and also identifies someof their majordrawbacks.

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“…In addition, since some application of EH-WSNs depending on a synchronized notion of time, as a traditional problem of WSNs and EH-WSNs, time synchronization is researched widely for achieving global or local time synchronized; e.g., RBS [34], TPSN [35], and SATS [20]. Other examples can be found in [36], [37], and references therein. Even though numbers approaches have been proposed for EH-WSNs, a single communication technology is utilized in these works for solving the problems of routing protocol and time synchronization.…”

Section: Related Workmentioning

confidence: 99%

RowBee: A Routing Protocol Based on Cross-Technology Communication for Energy-Harvesting Wireless Sensor Networks

Gao

Zhang

et al. 2019

IEEE Access

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Low and dynamic duty cycles cause that the E2E delay for packet delivery is more critical in energy-harvesting wireless sensor networks (EH-WSNs). The traditional routing protocols are constrained by the in-technology communication paradigm, where Wi-Fi devices can talk to the Wi-Fi devices only, and so on for ZigBee or wireless technology. This is, however, not necessary by recent advances in cross-technology communication (CTC). The CTC enables ZigBee nodes to be coordinated by a Wi-Fi node without any hardware changes or gateway equipment, which sheds the light on more efficient routing protocols design. In this paper, we introduce a new routing protocol based on a CTC technique called RowBee. RowBee takes the advantages of coordination from the Wi-Fi node to assist the ZigBee nodes for establishing routing paths and allows nodes to choose their duty cycles freely with finer duty-cycle granularity. A simple yet effective method is employed so that the ZigBee nodes are coordinately waked up simultaneously according to the beacons broadcasted by the Wi-Fi nodes. We implement RowBee based on a USRP-N210 and MICAz hybrid platform, and the experimental results show that RowBee can reduce the E2E delay greatly. INDEX TERMS Wireless sensor networks, routing protocol, cross-technology communication, energy-harvesting wireless sensor networks. I. INTRODUCTION As an interesting strategy to extend the network lifetime of Wireless Sensor Networks (WSNs), Energy-Harvesting Wireless Sensor Networks (EH-WSNs) are more economical and useful in the long-term as they can operate for very long periods of time (perhaps more than ten years until hardware failure) relying on rechargeable technologies [1], which convert sources such as foot strike [2], body heat [3], finger strokes [4] and solar [5] into electricity. Assuming energy neutral operation [6], a sensor node 1 can operate perpetually The associate editor coordinating the review of this manuscript and approving it for publication was Zhenyu Xiao.

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

Cited by 6 publications

References 27 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

Lifetime-enhancement Schemes for Power-constrained Routing in Wireless Sensor Networks: A Survey

RowBee: A Routing Protocol Based on Cross-Technology Communication for Energy-Harvesting Wireless Sensor Networks

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