A Novel Joint Optimization Method Based on Mobile Data Collection for Wireless Rechargeable Sensor Networks

Boukerche, Azzedine; Wu, Qing; Sun, Peng

doi:10.1109/tgcn.2021.3080918

Cited by 17 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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 objective of the work is to determine optimal path to charge all the sensors and collect maximum amount of data from them. A cluster-based novel CDAC scheme 24 is proposed with the objective of enhancing charging rate and data gathering delay in WRSN. Similarly, the works 28,29 applied clustering concept in their CDAC schemes to optimize the path by visiting cluster heads only and collect data with minimum time delay.…”

Section: Data Gathering and Charging Using Same MVmentioning

confidence: 99%

“…Several studies [19][20][21][22][23][24] have investigated CDAC using MV, but these approaches have significant drawbacks:…”

Section: Introductionmentioning

confidence: 99%

“…• We have conducted thorough simulation experiments of the proposed scheme in respect of average tour length, total dead period, average network lifetime, average charging delay, and energy utilization efficiency. 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: Introductionmentioning

confidence: 99%

“…Several studies 19–24 have investigated CDAC using MV, but these approaches have significant drawbacks: Many schemes rely on a single MV to achieve CDAC concurrently, but this approach falls short for large‐scale WRSN. Furthermore, a data gathering and charging schedule that is periodic cannot ensure reduced charging delay and less dead period of sensors.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Das

Dash

2023

Int J Communication

View full text Add to dashboard Cite

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.

show abstract