The Energy Replenishment Problem in Mobile WRSNs

Cheng, Chien-Fu; Wang, Chen-Chuan

doi:10.1109/mass.2018.00029

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…Nevertheless, it falls short in accounting for latency and other real-time variations in traffic-related scenarios. Energy replenishment issues in mobiles through a rechargeable sensor network covering the balanced consumption of energy and the separation of multiple redundant mobile sensors is addressed in [25], and a solution is proposed through novel balancing and sensor dispatch approaches For the first issue, an energy balancing algorithm is proposed that uses cascaded movement to improve the cascading schedule. For the second issue, a redundant mobile sensor dispatch algorithm is proposed that prioritizes the mobile sensors most in need of energy replenishment for replacement via a charged and calibrated redundant mobile sensor.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

Gupta,

Rathi

2024

Sensors

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The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

“…For each node in a particular LL group among N ′ , a score based on the parameters and their weights is computed, considering the values of parameters (P) and the respective dynamic weight (W). Score (Sc) computation for any node A in LL Q is shown below from Equations ( 23) to (25).…”

Section: Monkeys' Weights In Ll For Relay Identificationmentioning

confidence: 99%

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

Gupta,

Rathi

2024

Sensors

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“…Hybrid LSTM Predicting Algorithm. The LSTM technique is used in TPA because of its integral gain in analyzing sequence data and predicting [24][25][26]. Calculating an integrated mobility prototype for the entire sensor nodes would be exceedingly time consuming because individual nodes have distinct movement patterns.…”

Section: 2mentioning

confidence: 99%

Energy‐Efficient Enhancement for the Prediction‐Based Scheduling Algorithm for the Improvement of Network Lifetime in WSNs

Mohiddin

Kohli

Dutt

et al. 2021

Wireless Communications and Mobile Computing

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In wireless sensor networks, due to the restricted battery capabilities of sensor nodes, the energy issue plays a critical role in network efficiency and lifespan. In our work, an upgraded long short-term memory is executed by the base station to frequently predict the forecast positions of the node with the help of load-adaptive beaconing scheduling algorithm. In recent years, new technologies for wireless charging have offered a feasible technique in overcoming the WSN energy dilemma. Researchers are deploying rechargeable wireless sensor networks that introduce high-capacity smartphone chargers for sensor nodes for charging. Nearly all R-WSN research has focused on charging static nodes with relativistic routes or mobile nodes. In this work, it is analysed how to charge nondeterministic mobility nodes in this work. In this scenario, a new mechanism is recommended, called predicting-based scheduling algorithm, to implement charging activities. In the suggested technique, it directs them to pursue the mobile charger and recharge the sensor, which is unique for the present work. The mobile charger will then choose a suitable node, utilizing a scheduling algorithm, as the charging object. A tracking algorithm based on the Kalman filter is preferred during energy transfer to determine the distance needed for charging between the destination node & mobile charger. Here, the collecting & processing of data are performed through the big data collection in WSNs. The R-WSN charging operations of nondeterministic mobility nodes will be accomplished using the proposed charging strategy.

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“…For instance, [23] proposed a scheme to adapt the working power of the static charging pile to charge the sensor nodes moving nearby. The authors in [12] presented an approach to schedule mobile nodes with critical energy to the static charging piles for recharging, while redundant nodes take over the monitoring tasks of recharging nodes automatically. To maintain the performance of WRSN, [11] presented a tree-based schedule to charge the missioncritical robots, which arranges the MC waiting in the trajectory of exhausted sensor nodes without causing robot energy depletion.…”

Section: Related Workmentioning

confidence: 99%

“…To charge the mission-critical robots, Liu, Tang et al [11] presented a tree-based schedule, which reduces the MC's travel distance without causing robot energy depletion. C. F. Cheng et al [12] explored the problem of static charging piles deployment so that mobile sensor nodes can get close to the piles for charging when their energy exhausts. These researches above generally assumed that the trajectories of sensor nodes are deterministic or the movements of nodes are controlled.…”

Section: Introductionmentioning

confidence: 99%