Adaptive online mobile charging for node failure avoidance in wireless rechargeable sensor networks

Zhu, Jinqi; Feng, Yong; Liu, Ming; Chen, Guihai; Huang, Yongxin

doi:10.1016/j.comcom.2018.05.002

Cited by 53 publications

(31 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parameter α is cluster selection coefficient which satisfies 0 ≤ α ≤ 1. From (13), it can be found that during the optimal to-be-charged cluster selection, goals vary with the value of α. When α becomes larger, selected clusters tend to have less residual lifetime, while the total amount traffic relayed by the cluster is given more consideration if 1 − α is larger.…”

Section: A Algorithmmentioning

confidence: 99%

“…[12] proposed a charging method based fussy logic to find the charging sequence of sensor nodes to minimize the node failure. By scheduling the WCV to charge partial sensor nodes, the lifetime of the whole network is maximized in [13]. Authors in [14] investigated the charging method for the wireless sensor network deployed in a rectangular street grid.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Charging Strategy of Mobile Charging Vehicles in Wireless Rechargeable Sensor Networks With Heterogeneous Sensors

2020

View full text Add to dashboard Cite

Energy shortage obstructs the applications of the wireless rechargeable sensor network (WRSN). With the development of the wireless energy transfer technology, the mobile wireless charging vehicle (WCV) becomes a promising solution to solve that problem. However, the importance of different sensor nodes in the data transmission and uneven energy consumptions are often ignored. In this paper, the charging strategy of the WCV is studied in the WRSN considering these two phenomena. According to the importance of the sensor node, which is associated with the distance to the base station, we divide sensor nodes into two types: sensor nodes in ring 0 and sensor nodes in outer ring. We propose a novel charging model, the WCV adopts different charging strategies for different sensor nodes. To make the charging more efficient, the WCV charges sensor nodes one by one in ring 0 first, and then charges multiple sensor nodes simultaneously in outer ring. To estimate the lifetime of the network, a new metric named as the normalized dead time is proposed. Maximizing the lifetime of the network is modeled as minimizing the sum normalized dead time, and an efficient algorithm is proposed to minimize the sum normalized dead time through searching the optimal charging timeslots sequences. Then, through reassigning charging timeslots of sensor nodes, the proposed minimum travel cost algorithm minimizes the travel distance of the WCV and guarantee the lifetime of the network. We further deploy a cluster head node which has larger battery capacity in each cluster and can charge other sensor nodes within a limited distance. An algorithm is proposed to pre-distribute energy of the cluster head node. At last, the performance of proposed algorithms is verified by MATLAB. The results indicate that the performance of the WRSN can be improved by our proposed algorithms. INDEX TERMS Wireless rechargeable sensor network, wireless energy transfer technology, mobile wireless charging vehicle, charging strategy, sum normalized dead time minimization, travel cost minimization.

show abstract

Section: A Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Charging Strategy of Mobile Charging Vehicles in Wireless Rechargeable Sensor Networks With Heterogeneous Sensors

2020

View full text Add to dashboard Cite

show abstract

“…i.e., Q req . We use the same energy model as in [21] in [32]. Following the charging model in [21], the charging energy received by s i (i.e., P r ðs i Þ) is calculated as posed approach is not affected by the size as well as the density of the network.…”

Section: System Descriptionmentioning

confidence: 99%

A Fuzzy Logic-Based On-Demand Charging Algorithm for Wireless Rechargeable Sensor Networks With Multiple Chargers

Tomar¹

2020

Preprint

View full text Add to dashboard Cite

Mobile chargers have greatly promoted the wireless rechargeable sensor networks (WRSNs). While most recent works have focused on recharging the WRSNs in an on-demand fashion, little attention has been paid on joint consideration of multiple mobile chargers (MCs) and multi-node energy transfer for determining the charging schedule of energy-hungry nodes. Moreover, most of the schemes leave out the contemplation of multiple network parameters while making scheduling decisions and even they overlook<br>the issue of ill-timed charging response to the nodes with uneven energy consumption rates. In this paper, we address the aforesaid issues together and propose a novel scheduling scheme for on-demand charging in WRSNs. We first present an efficient network partitioning method for distributing the MCs so as to fairly balance their workload. We next adopt the fuzzy logic which blends various network parameters for determining the charging schedule of the MCs. We also formulate an expression to determine the charging threshold for the nodes that vary depending on their energy consumption rate. Extensive simulations are conducted to demonstrate the effectiveness and competitiveness of our scheme. The comparison results reveal that the proposed scheme improves charging<br>performance compared to the state-of-the-art schemes with respect to various performance metrics.<br>

show abstract

“…Zou et al [11] focused on the charging path for the mobile charge in MWSNs by using reinforcement learning (RL) and prolonged the lifetime of the network and efficiency of the mobile charger. Zhu et al [12] addressed the node failure of mobile charging for WRSNs which targets to lower the number of invalid nodes. Here the charging nodes are selected as the next charging node for online charging.…”

Section: Charging Strategiesmentioning

confidence: 99%

Novel wireless charging algorithms to charge mobile wireless sensor network by using reinforcement learning

Soni

Shrivastava

2019

SN Appl. Sci.

View full text Add to dashboard Cite

Generally, mobile wireless sensor network (MWSN) senses the sensitive and typical kind of events in various application areas along with the frequent mobility of sensor nodes as compared to traditional wireless sensor network. Due to mobility feature, MWSN extended the working of WSN. To design MWSN, certain key factors like energy efficiency, mobility, data routing, localization and charging strategy are involved. Mobile sensor nodes consume extra amount of energy due to mobility along with sensing and data routing task which exhaust network lifetime rapidly. In this research study, authors have proposed two wireless chargers (fixed and mobile) for sensor nodes. The main difference between fixed and mobile wireless charger is: fixed wireless charger invites mobile sensor nodes for wireless charging at base station whereas mobile wireless charger travels to the position of mobile sensor nodes for wireless charging. Both chargers fulfilled the charging request of sensor nodes efficiently. This is the first research study to propose such type of charging strategy for mobile wireless sensor network (MWSN) as per the available literature. The RL (reinforcement learning) technique is used here for optimization purpose of charging cost. These wireless chargers simulated in MATLAB enviorment and results showed that there is a significant improvement in network lifetime due to these charging strategies when compared to the existing algorithms.

show abstract

Adaptive online mobile charging for node failure avoidance in wireless rechargeable sensor networks

Cited by 53 publications

References 8 publications

The Charging Strategy of Mobile Charging Vehicles in Wireless Rechargeable Sensor Networks With Heterogeneous Sensors

The Charging Strategy of Mobile Charging Vehicles in Wireless Rechargeable Sensor Networks With Heterogeneous Sensors

A Fuzzy Logic-Based On-Demand Charging Algorithm for Wireless Rechargeable Sensor Networks With Multiple Chargers

Novel wireless charging algorithms to charge mobile wireless sensor network by using reinforcement learning

Contact Info

Product

Resources

About