Demand‐based charging strategy for wireless rechargeable sensor networks

Dong, Ying; Wang, Yuhou; Shi-yuan, LI; Cui, Mengyao; Wu, Hui

doi:10.4218/etrij.2018-0126

Cited by 31 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We undertook simulation experiments so as to evaluate and analyze the performance of our proposed scheme. We performed a comparability study of our scheme with two other different state-of-the-art charging models in literature, which were the HCCA-TP charging model in [46] and the DBCS charging model in [47]. These models adopted clear and well-defined network topologies instead of complex topologies that are often initiated by network dynamics requiring collaborative charging mechanisms and large re-computational cost.…”

Section: Simulation Analysis and Discussionmentioning

confidence: 99%

“…In [46], the authors developed a K-means cluster algorithm to calculate the energy core set while an optimizing algorithm was proposed to convert the energy charging stage into a task splitting model so as to increase its energy efficiency. Additionally, in [47], another K-means clustering algorithm was introduced to balance the energy consumption among sensor nodes, while a dynamic selection algorithm was proposed to charge sensor nodes in order to reduce the charging time spent in the network. There are quite some issues in most of the models, as mentioned above, and to solve these problems to an extent, we proposed a set of algorithms that can inspect the network and make decisions on the best possible sensor nodes to charge.…”

Section: Optimizationsmentioning

confidence: 99%

See 1 more Smart Citation

On Increasing the Energy Efficiency of Wireless Rechargeable Sensor Networks for Cyber-Physical Systems

Orumwense¹,

Abo-Al-Ez²

2022

Energies

View full text Add to dashboard Cite

In recent times, wireless energy transfer has become an effective solution to charge devices due to its efficiency and reliability. In a typical Wireless Rechargeable Sensor Networks (WRSN), wireless energy transfer technique can solve the energy depletion problem with the aid of a Wireless Charging Vehicle (WCV), thereby enabling the network to extend its lifetime. However, sensor nodes in a WRSN still have their energies depleted before it gets replenished by the WCV. In this paper, we proposed a scheme that prioritizes sensor nodes for charging and also developed efficient algorithms to improve on existing charging schemes so as to extend the lifetime of the WRSN. Firstly, an inspection algorithm was developed to visit and inspect sensor nodes in the network so as to determine the sensor nodes to charge. Secondly, a greedy charge algorithm was introduced to ascertain the shortest distance the WCV needs to travel and, lastly, an energy for nodes’ algorithm was proposed to determine the stopping point and when the WCV needs to return to the base station. Simulation experiments were also conducted to determine the performance of our scheme. The simulation experiments revealed that our proposed scheme made significant improvements when compared to other schemes in literature using several metrics.

show abstract

Section: Simulation Analysis and Discussionmentioning

confidence: 99%

Section: Optimizationsmentioning

confidence: 99%

On Increasing the Energy Efficiency of Wireless Rechargeable Sensor Networks for Cyber-Physical Systems

Orumwense¹,

Abo-Al-Ez²

2022

Energies

View full text Add to dashboard Cite

show abstract

“…In the simulation experiment, 200 to 250 sensors are randomly placed on flat surface 16,43,44 of area 1000 Â 1000 m 2 . The maximum energy of each hybrid rechargeable sensor is 10.8 KJ, 43,45 and its charging radius is 2.7 m. 43,46 The maximum energy of MV is 4000 KJ.…”

Section: Simulation Settingsmentioning

confidence: 99%

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

“…By using a low‐cost method, the optimized path for mobile charger can be designed for the sensors that have sent charging requests in [21]. A demand‐based charging strategy for rechargeable wireless sensor networks was designed in [22], where the charging path for a mobile charger is optimized to prolong the recharging time. However, some devices may be out of power prematurely, given they may wait energy replenishment for a long time.…”

Section: Related Workmentioning

confidence: 99%

Payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger in IoT

Guo

et al. 2021

IET Communications

View full text Add to dashboard Cite

In order to maximize the work efficiency of wireless mobile charger, a payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger is proposed. Based on the mesh structure and multi‐node charging technology, the recharging optimization for massive devices is modelled as a problem of payoff maximization. According to energy allocation, anchor point deployment and time allocation, we decompose it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimized in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy, respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by our innovative gain recall mechanism. The trade‐off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharging time in a fixed cycle, and mobile charger can always work in efficient recharging positions, whose effect is exploited utmost.

show abstract

Demand‐based charging strategy for wireless rechargeable sensor networks

Cited by 31 publications

References 33 publications

On Increasing the Energy Efficiency of Wireless Rechargeable Sensor Networks for Cyber-Physical Systems

On Increasing the Energy Efficiency of Wireless Rechargeable Sensor Networks for Cyber-Physical Systems

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

Payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger in IoT

Contact Info

Product

Resources

About