Optimal rendezvous points selection to reliably acquire data from wireless sensor networks using mobile sink

Mehto, Anjula; Tapaswi, Shashikala; Pattanaik, K. K.

doi:10.1007/s00607-021-00917-x

Cited by 24 publications

(5 citation statements)

References 32 publications

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“…The above mainly summarizes the research in the direction of UAV-based data collection in the past two years, many of which refer to the theoretical research in the reference [17], and methods such as swarm intelligence and neural networks are current research hotspots in this direction. In addition to planning the trajectory of the UAV to reduce energy consumption, there are also studies on the latency of the sensing network [30], sensor lifetime [31], and sensing protocols [32] to ensure secure data transmission.…”

Section: Plos Onementioning

confidence: 99%

“…We found that there are some current studies on sensing networks focusing on data latency [30], sensor lifetime [31], and wireless transmission protocols [32]. In our next study, we will discuss these three issues based on the current research using environmental monitoring as an application environment to construct an automatic monitoring framework.…”

Section: Plos Onementioning

confidence: 99%

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A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning

Lin,

Wang,

Tian

et al. 2024

PLoS ONE

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With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is proposed to minimize the energy consumption of the unmanned aerial vehicle (UAV). DSLPSO refers to the operational principle of PSO and incorporates two new mechanisms. The first mechanism is to restrict the particle movement, improving the local search capability of the algorithm. The second mechanism dynamically adjusts the search range, which improves the algorithm’s global search capability. DSLPSO employs a variable population strategy that treats the entire population as a single mission plan for the UAV and dynamically adjusts the number of stopping points. In addition, the proposed algorithm was also simulated using public and random datasets. The effectiveness of the proposed DSLPSO and the two new mechanisms has been verified through experiments. The DSLPSO algorithm can effectively improve the lifetime of the UAV, and the two newly proposed mechanisms have potential for optimization work.

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Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning

Lin,

Wang,

Tian

et al. 2024

PLoS ONE

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“…However, the use of very few RVPs leads to maximizing the data gathering time and causing the buffer overflow problem. In Mehto et al's [28] study, squirrels search algorithm based rendezvous points selection (SSA-RVPS) is introduced for the MS to reliably acquire data from WSNs. The SSA-RVPS aims to extend the network lifetime by reducing the trajectory length of MS to gather SNs data generation with different rates.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Energy-Efficient Data Routing Scheme for Wireless Sensor Networks Utilizing Mobile Sink

Al-Mahdi,

Elshrkawey,

Saad

et al. 2024

Wireless Communications and Mobile Computing

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Data collection and energy consumption are critical concerns in Wireless sensor networks (WSNs). To address these issues, both clustering and routing algorithms are utilized. Therefore, this paper proposes an intelligent energy-efficient data routing scheme for WSNs utilizing a mobile sink (MS) to save energy and prolong network lifetime. The proposed scheme operates in two major modes: configure and operational modes. During the configure mode, a novel clustering mechanism is applied once, and a prescheduling cluster head (CH) selection is introduced to ensure uniform energy expenditure among sensor nodes (SNs). The scheduling technique selects successive CHs for each cluster throughout the WSNs’ lifetime rounds, managed at the base station (BS) to minimize SN energy consumption. In the operational mode, two main objectives are achieved: sensing and gathering data by each CH with minimal message overhead, and establishing an optimal path for the MS using the genetic algorithm. Finally, the MS uploads the gathered data to the BS. Extensive simulations are conducted to verify the efficiency of the proposed scheme in terms of stability period, network lifetime, average energy consumption, data transmission latency, message overhead, and throughput. The results demonstrate that the proposed scheme outperforms the most recent state-of-the-art methods significantly. The results are substantiated through statistical validation via hypothesis testing utilizing ANOVA, as well as post hoc analysis.

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“…Li et al [22] present a localized geographic routing scheme to ensure data delivery to the mobile sink in a WSN. Mehto et al [23] propose a squirrel search algorithm-based rendezvous points selection (SSA-RPS) method that chooses a set of optimal RPs for reliable data collection. In conclusion, these researches investigate mobility as a means of relieving traffic burden and enhancing energy efficiency of WSNs.…”

Section: Mobile Data Collecting In Wireless Sensormentioning

confidence: 99%

Data Collecting and Energy Charging Oriented Mobile Path Design for Rechargeable Wireless Sensor Networks

Zhang

Cai

2022

Journal of Sensors

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Energy efficiency is one of the most important concerns in wireless sensor networks (WSNs). As far as we know, almost all energy efficiency researches of WSNs focus on energy conservation in some respects such as wireless data transmission and minimal data collection. Recently, wireless energy transfer has been a promising technology to prolong the lifetime of microsensor nodes, and so the traditional WSNs can be extended to rechargeable WSNs. Rechargeable WSNs is a new type of wireless sensor networks, where each sensor node can replenish energy through wireless charging. For rechargeable WSNs, it is powered by reusable energy or harvested energy, so the energy efficiency problem can be completely solved. Furthermore, mobile data collection has been well recognized to have significant advantages over sensory data collection manner using static sinks. In this paper, by employing one or multiple recharging sinks to replenish energy for sensor nodes and collect sensory data concurrently, we propose a novel wireless charging and mobile data collecting method based on self-organizing map (SOM) unsupervised learning for rechargeable WSNs. In other words, the sink mobility and energy replenishment are jointly considered in this paper. Finally, we evaluate the performance of the proposed algorithms through software simulation. Extensive results verify that the performance of the proposed algorithm can reduce the travel cost of mobile sink and improve the residual energy level for sensor nodes. As a results, it is very promising in the field of data acquisition in wireless sensor networks.

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Optimal rendezvous points selection to reliably acquire data from wireless sensor networks using mobile sink

Cited by 24 publications

References 32 publications

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning

An Intelligent Energy-Efficient Data Routing Scheme for Wireless Sensor Networks Utilizing Mobile Sink

Data Collecting and Energy Charging Oriented Mobile Path Design for Rechargeable Wireless Sensor Networks

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