Joint mobile wireless energy transmitter and data collector for rechargeable wireless sensor networks

Prasannababu, Devarapalli; Amgoth, Tarachand

doi:10.1007/s11276-022-03060-3

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…Numerous works include this sort of technique [40][41][42]. The data-partitioning module takes care of partitioning, grouping, and extracting the necessary information so the user can experiment, using a given time interval.…”

Section: Partitioningmentioning

confidence: 99%

TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

2023

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Pre-processing and analysis of sensor data present several challenges due to their increasingly complex structure and lack of consistency. In this paper, we present TSxtend, a software tool that allows non-programmers to transform, clean, and analyze temporal sensor data by defining and executing process workflows in a declarative language. TSxtend integrates several existing techniques for temporal data partitioning, cleaning, and imputation, along with state-of-the-art machine learning algorithms for prediction and tools for experiment definition and tracking. Moreover, the modular architecture of the tool facilitates the incorporation of additional methods. The examples presented in this paper using the ASHRAE Great Energy Predictor dataset show that TSxtend is particularly effective to analyze energy data.

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Section: Partitioningmentioning

confidence: 99%

TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

2023

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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 data gathering and routing takes place through the Hamiltonian cycle by MV. In order to prolong the energy efficiency, Prasannababu and Amgoth 26 propose a combined data collection and charging mechanism using single MV. They first partition the network using spectral clustering algorithm and then determine the schedule using cat swarm optimization technique (CSO) to serve the requesting sensors in order.…”

Section: Literature Surveymentioning

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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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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A New Joint Data Collection and Wireless Energy Transfer (SSDCWC) Strategy for Rechargeable Wireless Sensor Network

Chandra,

Soni

2024

SN COMPUT. SCI.

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Joint mobile wireless energy transmitter and data collector for rechargeable wireless sensor networks

Cited by 8 publications

References 34 publications

TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

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

A New Joint Data Collection and Wireless Energy Transfer (SSDCWC) Strategy for Rechargeable Wireless Sensor Network

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