J-RCA: A Joint Routing and Charging Algorithm With WCE Assisted Data Gathering in Wireless Rechargeable Sensor Networks

Lu, Zhilin; Fan, Bing; Cai, Jinqi; Tang, Liangrui

doi:10.1109/jsen.2020.3023301

Cited by 16 publications

(8 citation statements)

References 29 publications

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“…The rationale behind our emphasis on recent and relevant works on CDAC in this survey is its immense popularity among researchers due to its ability to conserve both time and energy within the network. A set of popular works 19,[24][25][26][27] of the joint design have been proposed by utilizing single or multiple MV in wireless sensor network (WSN). That is why in this section, we have included some existing schemes related to CDAC technique which are described as below.…”

Section: Literature Surveymentioning

confidence: 99%

“…The proposed work is compared with periodic multinode charging and data collection (PMCDC), 22 improved multipath scheduling scheme (IMPSS), 24 and joint routing and charging algorithm (J-RCA). 25 The remaining of the article is structured as follows. Section 2 provides a concise overview of the state-of-the-art works on CDAC schedule in WRSNs.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative data gathering and recharging using multiple mobile vehicles in wireless rechargeable sensor network

Das

Dash

2023

Int J Communication

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SummaryMany cutting‐edge studies on collaborative data gathering and charging (CDAC) assume that the mobile vehicle (MV) has enough energy to charge the sensors as well as collect data from them. The current studies also took into account that the sensors always receive full charge from the MV, resulting in a wireless rechargeable sensor network (WRSN) with very little dead period and very little data gathering latency. It is also believed that the energy consumption rates of the sensors are constant. However, in large‐scale WRSN, the aforementioned considerations are not always practical. In addition, the utilization of single base station (BS) in a large‐scale WRSN cannot guarantee improved network scalability, expedite charging decisions by minimizing the substantial overhead of the BS, and enhance the traveling distance for MV. In order to overcome the aforementioned problems, we present an effective on‐demand partial CDAC scheme using battery‐limited multiple MVs. The proposed scheme implements the CDAC process in such a way that the total dead periods of the sensors and the energy consumption of MVs are reduced. We use a multi‐objective‐based genetic algorithm (GA) to optimize the entire CDAC process. The simulation is carried out to demonstrate the usefulness and competitiveness of the proposed scheme. In comparison with existing works, the proposed work improves CDAC performance by reducing sensor dead time and energy consumption of MV.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collaborative data gathering and recharging using multiple mobile vehicles in wireless rechargeable sensor network

Das

Dash

2023

Int J Communication

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“…A joint charging and routing algorithm with WCE-assisted data gathering is also suggested by Lu et al [ 151 ]. The model suggested therein assumes that the BS (sink) can get the information of each node at any time, including node location, residual energy, and energy consumption rate.…”

Section: Routing For Data Gathering In Wsnmentioning

confidence: 99%

Data Gathering Techniques in WSN: A Cross-Layer View

Gurewitz

Shifrin

Dvir

2022

Sensors

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Wireless sensor networks (WSNs) have taken a giant leap in scale, expanding their applicability to a large variety of technological domains and applications, ranging from the Internet of things (IoT) for smart cities and smart homes to wearable technology healthcare applications, underwater, agricultural and environmental monitoring and many more. This expansion is rapidly growing every passing day in terms of the variety, heterogeneity and the number of devices which such applications support. Data collection is commonly the core application in WSN and IoT networks, which are typically composed of a large variety of devices, some constrained by their resources (e.g., processing, storage, energy) and some by highly diverse demands. Many challenges span all the conceptual communication layers, from the Physical to the Applicational. Many novel solutions devised in the past do not scale well with the exponential growth in the population of the devices and need to be adapted, revised, or new innovative solutions are required to comply with this massive growth. Furthermore, recent technological advances present new opportunities which can be leveraged in this context. This paper provides a cross-layer perspective and review of data gathering in WSN and IoT networks. We provide some background and essential milestones that have laid the foundation of many subsequent solutions suggested over the years. We mainly concentrate on recent state-of-the-art research, which facilitates the scalable, energy-efficient, cost-effective, and human-friendly functionality of WSNs and the novel applications in the years to come.

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“…WRSNs, by contrast to energy predicting technology, maintain networks operational also while controlling the charging process. As a result, the lifespan of WRSNs is projected to be infinite [3]. Angelopoulos et al has suggested a set of energy-saving features of the new Wireless Rechargeable Sensor Networks (WRSNs) paradigm, where a mobile charger travels through a WSN and distributes power to sensor motes wirelessly.…”

Section: Introductionmentioning

confidence: 99%

Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network

Vallirathi¹,

Juliet²

2023

Computer Systems Science and Engineering

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Wireless Sensor Network (WSNs) is an infrastructure-less wireless network deployed in an increasing number of wireless sensors in an ad-hoc manner. As the sensor nodes could be powered using batteries, the development of WSN energy constraints is considered to be a key issue. In wireless sensor networks (WSNs), wireless mobile chargers (MCs) conquer such issues mainly, energy shortages. The proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network (WRSN), which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the sensor. In this algorithm, each node gets sorted using the K-means technique, in which the data gets distributed into various clusters. The mobile charges execute a Short Hamiltonian cycle opposite direction to reach each cluster's anchor point. The position of the anchor points is calculated based on the energy distribution using the base station. In this case, the network will act as a spare MC, so that one of the two MCs will run out of energy before reaching the BS. After the current tours of the two MCs terminate, regression analysis for energy prediction initiates, enabling the updating of anchor points in the upcoming round. Based on the findings of the regression-based energy prediction model, the recommended algorithm could effectively refill network energy.

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J-RCA: A Joint Routing and Charging Algorithm With WCE Assisted Data Gathering in Wireless Rechargeable Sensor Networks

Cited by 16 publications

References 29 publications

Collaborative data gathering and recharging using multiple mobile vehicles in wireless rechargeable sensor network

Collaborative data gathering and recharging using multiple mobile vehicles in wireless rechargeable sensor network

Data Gathering Techniques in WSN: A Cross-Layer View

Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network

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