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Wireless Rechargeable Sensor Networks 2019

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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 14684

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Prof. Dr. Chang Wu Yu

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Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu City, Taiwan
Interests: wireless networks, graph algorithms, distributed computing
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Dear Colleagues,

Wireless sensor networks have attracted a great deal of attention recently due to their various applications in many fields. Due to limited power consumption, these sensor nodes may experience power shortages and thus lead to many problems including network disconnection. Most previous methods focused on providing energy-saving strategies to elevate the lifetime of sensor networks. Another aggressive but different approach is to wirelessly re-charge the sensor nodes to increase the lifetime of the sensor networks. This Special Issue, entitled “Wireless Rechargeable Sensor Networks”, invites articles that address state-of-the-art technologies and new developments for wireless rechargeable sensor networks (WRSNs). Articles that deal with the latest hot topics in WRSNs are particularly encouraged, such as charger deployment, charger scheduling, wireless energy transfer, mobile charger design, energy-harvesting techniques, and energy provisioning. In addition, articles that discuss protocols, algorithms, and optimization in WRSN, are of particular interest.

Prof. Dr. Chang Wu Yu
Guest Editor

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Keywords

recharging scheduling
wireless energy transfer techniques
energy-harvesting technique
charger deployment
protocol design
mobile charger design
energy provisioning
wireless sensor networks

Published Papers (6 papers)

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Editorial

Jump to: Research

3 pages, 163 KiB

Open AccessEditorial

Wireless Rechargeable Sensor Networks

by Chang-Wu Yu

Energies 2021, 14(23), 7895; https://doi.org/10.3390/en14237895 - 25 Nov 2021

Viewed by 1374

Abstract

Wireless sensor networks have attracted much attention recently due to their various applications in many fields [...] Full article

(This article belongs to the Special Issue Wireless Rechargeable Sensor Networks 2019)

Research

Jump to: Editorial

17 pages, 4830 KiB

Open AccessArticle

A Novel Hybrid Search and Remove Strategy for Power Balance Wireless Charger Deployment in Wireless Rechargeable Sensor Networks

by Tu-Liang Lin, Hong-Yi Chang and Yu-Hsin Wang

Energies 2020, 13(10), 2661; https://doi.org/10.3390/en13102661 - 25 May 2020

Cited by 9 | Viewed by 2340

Abstract

Conventional sensor nodes are often battery-powered, and battery power limits the overall lifetime of the wireless sensor networks (WSNs). Wireless charging technology can be implemented in WSNs to supply power to sensor nodes and resolve the problem of restricted battery power. This type of mixed network is called wireless rechargeable sensor networks (WRSNs). Therefore, wireless charger deployment is a crucial task in WRSNs. In this study, the method of placing wireless chargers to efficiently extend the lifetime of the WRSNs is addressed. Owing to the data forwarding effect in WSNs, sensor nodes that are closer to the data collection or sink node drain more power than nodes that are further away from the data collection or sink node. Therefore, this study proposes a novel hybrid search and removal strategy for the power balance charger deployment method. The wireless chargers are placed in the chosen nodes of the WRSNs. The node-chosen problem we address is called the dominating set problem. The proposed hybrid search and removal strategy attempts to discover the minimum number of chargers required to cover all sensor nodes in the WRSN. The proposed algorithm considers the charging power of the wireless directional charger when arranging its placement to maximize the charging capacity in a power-balanced prerequisite. Therefore, the proposed deployment strategy preserves the awareness of the presence of the sink node that could result in unbalanced power distribution in WRSNs. The simulation results show that the proposed strategy spares more chargers and achieves better energy efficiency than other deployment approaches. Full article

(This article belongs to the Special Issue Wireless Rechargeable Sensor Networks 2019)

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23 pages, 6489 KiB

Open AccessArticle

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

by Antonio Del Corte-Valiente, José Manuel Gómez-Pulido, Oscar Gutiérrez-Blanco and José Luis Castillo-Sequera

Energies 2019, 12(15), 2943; https://doi.org/10.3390/en12152943 - 31 Jul 2019

Cited by 15 | Viewed by 3133

Abstract

The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can be obtained in the location process using the delay between rays as a cost function and the Mahalanobis distance as a metric instead of traditional methods based on power levels and the Euclidean distance. The proposed method appears well suited for localization systems applied to indoor and outdoor scenarios and avoids large and costly measurement campaigns. Full article

(This article belongs to the Special Issue Wireless Rechargeable Sensor Networks 2019)

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14 pages, 3463 KiB

Open AccessArticle

Enhancing the Performance of Energy Harvesting Sensor Networks for Environmental Monitoring Applications

by Mahdi Zareei, Cesar Vargas-Rosales, Mohammad Hossein Anisi, Leila Musavian, Rafaela Villalpando-Hernandez, Shidrokh Goudarzi and Ehab Mahmoud Mohamed

Energies 2019, 12(14), 2794; https://doi.org/10.3390/en12142794 - 20 Jul 2019

Cited by 17 | Viewed by 2404

Abstract

Fast development in hardware miniaturization and massive production of sensors make them cost efficient and vastly available to be used in various applications in our daily life more specially in environment monitoring applications. However, energy consumption is still one of the barriers slowing down the development of several applications. Slow development in battery technology, makes energy harvesting (EH) as a prime candidate to eliminate the sensor’s energy barrier. EH sensors can be the solution to enabling future applications that would be extremely costly using conventional battery-powered sensors. In this paper, we analyze the performance improvement and evaluation of EH sensors in various situations. A network model is developed to allow us to examine different scenarios. We borrow a clustering concept, as a proven method to improve energy efficiency in conventional sensor network and brought it to EH sensor networks to study its effect on the performance of the network in different scenarios. Moreover, a dynamic and distributed transmission power management for sensors is proposed and evaluated in both networks, with and without clustering, to study the effect of power balancing on the network end-to-end performance. The simulation results indicate that, by using clustering and transmission power adjustment, the power consumption can be distributed in the network more efficiently, which result in improving the network performance in terms of a packet delivery ratio by 20%, 10% higher network lifetime by having more alive nodes and also achieving lower delay by reducing the hop-count. Full article

(This article belongs to the Special Issue Wireless Rechargeable Sensor Networks 2019)

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20 pages, 2549 KiB

Open AccessArticle

Joint Balanced Routing and Energy Harvesting Strategy for Maximizing Network Lifetime in WSNs

by Chih-Min Yu, Mohammad Tala’t, Chun-Hao Chiu and Chin-Yao Huang

Energies 2019, 12(12), 2336; https://doi.org/10.3390/en12122336 - 18 Jun 2019

Cited by 8 | Viewed by 2261

Abstract

Nowadays, wireless sensor networks (WSNs) are becoming increasingly popular due to the wide variety of applications. The network can be utilized to collect and transmit numerous types of messages to a data sink in a many-to-one fashion. The WSNs usually contain sensors with low communication ability and limited battery power, and the battery replacement is difficult in WSNs for large amount embedded nodes, which indicates a balanced routing strategy is essential to be developed for an extensive operation lifecycle. To realize the goal, the research challenges require not only to minimize the energy consumption in each node but also to balance the whole WSNs traffic load. In this article, a Shortest Path Tree with Energy Balance Routing strategy (SPT-EBR) based on a forward awareness factor is proposed. In SPT-EBR, Two methods are presented including the power consumption and the energy harvesting schemes to select the forwarding node according to the awareness factors of link weight. First, the packet forwarding rate factor is considered in the power consumption scheme to update the link weight for the sensors with higher power consumption and mitigate the traffic load of hotspot nodes to achieve the energy balance network. With the assistance of the power consumption scheme, hotspot nodes can be transferred from the irregular location to the same intra-layer from the sink. Based on this feature, the energy harvesting scheme combines both the packet forwarding rate and the power charging rate factors together to update the link weight with a new battery charging rate factor for hotspot nodes. Finally, simulation results validate that both power consumption and energy harvesting schemes in SPT-EBR achieve better energy balance performance and save more charging power than the conventional shortest path algorithm and thus improve the overall network lifecycle. Full article

(This article belongs to the Special Issue Wireless Rechargeable Sensor Networks 2019)

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20 pages, 2018 KiB

Open AccessArticle

A Genetic Approach to Solve the Emergent Charging Scheduling Problem Using Multiple Charging Vehicles for Wireless Rechargeable Sensor Networks

by Rei-Heng Cheng, ChengJie Xu and Tung-Kuang Wu

Energies 2019, 12(2), 287; https://doi.org/10.3390/en12020287 - 17 Jan 2019

Cited by 15 | Viewed by 2611

Abstract

Wireless rechargeable sensor networks (WRSNs) have gained much attention in recent years due to the rapid progress that has occurred in wireless charging technology. The charging is usually done by one or multiple mobile vehicle(s) equipped with wireless chargers moving toward sensors demanding energy replenishing. Since the loading of each sensor in a WRSN can be different, their time to energy exhaustion may also be varied. Under some circumstances, sensors may deplete their energy quickly and need to be charged urgently. Appropriate scheduling of available mobile charger(s) so that all sensors in need of recharge can be served in time is thus essential to ensure sustainable operation of the entire network, which unfortunately has been proven to be an NP-hard problem (Non-deterministic Polynomial-time hard). Two essential criteria that need to be considered concurrently in such a problem are time (the sensor’s deadline for recharge) and distance (from charger to the sensor demands recharge). Previous works use a static combination of these two parameters in determining charging order, which may fail to meet all the sensors’ charging requirements in a dynamically changing network. Genetic algorithms, which have long been considered a powerful tool for solving the scheduling problems, have also been proposed to address the charging route scheduling issue. However, previous genetic-based approaches considered only one charging vehicle scenario that may be more suitable for a smaller WRSN. With the availability of multiple mobile chargers, not only may more areas be covered, but also the network lifetime can be sustained for longer. However, efficiently allocating charging tasks to multiple charging vehicles would be an even more complex problem. In this work, a genetic approach, which includes novel designs in chromosome structure, selection, cross-over and mutation operations, supporting multiple charging vehicles is proposed. Two unique features are incorporated into the proposed algorithm to improve its scheduling effectiveness and performance, which include (1) inclusion of EDF (Earliest Deadline First) and NJF (Nearest Job First) scheduling outcomes into the initial chromosomes, and (2) clustering neighboring sensors demand recharge and then assigning sensors in a group to the same mobile charger. By including EDF and NJF scheduling outcomes into the first genetic population, we guarantee both time and distance factors are taken into account, and the weightings of the two would be decided dynamically through the genetic process to reflect various network traffic conditions. In addition, with the extra clustering step, the movement of each charger may be confined to a more local area, which effectively reduces the travelling distance, and thus the energy consumption, of the chargers in a multiple-charger environment. Extensive simulations and results show that the proposed algorithm indeed derives feasible charge scheduling for multiple chargers to keep the sensors/network in operation, and at the same time minimize the overall moving distance of the mobile chargers. Full article

(This article belongs to the Special Issue Wireless Rechargeable Sensor Networks 2019)

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