A Cooperative Clustering Protocol With Duty Cycling for Energy Harvesting Enabled Wireless Sensor Networks

Bahbahani, Mohammed S.; Alsusa, Emad

doi:10.1109/twc.2017.2762674

Cited by 85 publications

(42 citation statements)

References 33 publications

(42 reference statements)

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“…Mohamed et al [35] have proposed a cooperative scheduling protocol for sensor networks. The cooperative Time Division Multiple Access(cTDMA) scheduling was used to schedule the sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Mohamed et al [35] cTDMA It allocates slots to transmit data packets based on the cluster head occurrence count.…”

Section: Jaeyoung Et Al [30] E-leachmentioning

confidence: 99%

“…In this section, we compare our proposed work's performance with the existing methods LDC [43], iABC [27], cTDMA [35] and DRA [42] methods. The contributions of these methods are similar to the contribution of the E 2 S-DRL work in WSN.…”

Section: Comparative Analysismentioning

confidence: 99%

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Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling

Sinde

Begum

Njau³

et al. 2020

Sensors

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Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, we propose an Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (E2S-DRL) algorithm in WSN. E2S-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: the clustering phase, duty-cycling phase and routing phase. E2S-DRL starts with the clustering phase where we reduce the energy consumption incurred during data aggregation. It is achieved through the Zone-based Clustering (ZbC) scheme. In the ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing the DRL algorithm, from which, E2S-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in the third (routing) phase using Ant Colony Optimization (ACO) and the Firefly Algorithm (FFA). Our work is modeled in Network Simulator 3.26 (NS3). The results are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that our E2S-DRL reduces energy consumption, reduces delays by up to 40% and enhances throughput and network lifetime up to 35% compared to the existing cTDMA, DRA, LDC and iABC methods.

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Section: Related Workmentioning

confidence: 99%

“…Mohamed et al [35] cTDMA It allocates slots to transmit data packets based on the cluster head occurrence count.…”

Section: Jaeyoung Et Al [30] E-leachmentioning

confidence: 99%

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Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling

Sinde

Begum

Njau³

et al. 2020

Sensors

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“…In order to solve a variety of EH-WSNs problems, especially utilize the harvesting energy as much as possible, most of researchers propose various routing protocols. Examples of which include energy-efficient medium access control (MAC) protocols [27], duty-cycling strategies [28], energy-efficient routing [8], and topology control mechanisms [29]. By considering the limited and dynamical energy supply for sensor nodes, various protocols for reducing E2E delay have been proposed; e.g., [30], [31].…”

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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“…HEED 4 is designed for multi-hop WSNs, and it focuses on CH selection by considering both the residual energy and load balancing. Recent WSN clustering works focus on the issues, such as re-clustering, 13,14 energy harvesting, 15 and integration into 5G Internet of Things (IoT) scenarios. 16 Clustering aims to save energy, whereas the major sources of energy waste are collision, overhearing, and idle listening.…”

Section: Introductionmentioning

confidence: 99%

Affinity propagation‐based interference‐free clustering for wireless sensor networks

Lin

Chen

2019

Int J Communication

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Summary Wireless sensor networks (WSNs), eg, industrial WSNs, require reliability and real‐time communication. Clustering technique together with schedule‐based access can provide the benefits, such as energy saving, reliability, and timeliness. However, integrating above two technologies into WSNs requires sophistical time slot allocation mechanism. To simplify the time slot allocation, the paper proposes a distributed interference‐free clustering algorithm for WSNs. The algorithm is inspired by affinity propagation (AP) clustering algorithm. By adapting and improving the original AP algorithm, the proposed clustering algorithm aims to jointly optimize energy saving and coverage issues while providing interference free between clusters. The performance analysis demonstrates that it can achieve high receiving rate (reliability), low delay (real time), and low‐energy consumption.

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A Cooperative Clustering Protocol With Duty Cycling for Energy Harvesting Enabled Wireless Sensor Networks

Cited by 85 publications

References 33 publications

Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling

Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling

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

Affinity propagation‐based interference‐free clustering for wireless sensor networks

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