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

2020 ◽  
Author(s):  
Abhinav Tomar
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Performance Metrics ◽  
Network Partitioning ◽  
On Demand ◽  
Network Parameters ◽  
Comparison Results ◽  
Wireless Rechargeable Sensor Networks ◽  
Joint Consideration

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>

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

2020 ◽  
Author(s):  
Abhinav Tomar
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Performance Metrics ◽  
Network Partitioning ◽  
On Demand ◽  
Network Parameters ◽  
Comparison Results ◽  
Wireless Rechargeable Sensor Networks ◽  
Joint Consideration

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>

scholarly journals Smart Campus: An Experimental Performance Comparison of Collaborative and Cooperative Schemes for Wireless Sensor Network

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3135 ◽  
Author(s):  
Carolina Del-Valle-Soto ◽  
Leonardo J. Valdivia ◽  
Ramiro Velázquez ◽  
Luis Rizo-Dominguez ◽  
Juan-Carlos López-Pimentel
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Performance Metrics ◽  
Smart Cities ◽  
Performance Comparison ◽  
Wireless Sensor ◽  
University Campus ◽  
Multiple Sensors ◽  
Energy Consumption Analysis ◽  
Wireless Environment

Presently, the Internet of Things (IoT) concept involves a scattered collection of different multipurpose sensor networks that capture information, which is further processed and used in applications such as smart cities. These networks can send large amounts of information in a fairly efficient but insecure wireless environment. Energy consumption is a key aspect of sensor networks since most of the time, they are battery powered and placed in not easily accessible locations. Therefore, and regardless of the final application, wireless sensor networks require a careful energy consumption analysis that allows selection of the best operating protocol and energy optimization scheme. In this paper, a set of performance metrics is defined to objectively compare different kinds of protocols. Four of the most popular IoT protocols are selected: Zigbee, LoRa, Bluethooth, and WiFi. To test and compare their performance, multiple sensors are placed at different points of a university campus to create a network that can accurately simulate a smart city. Finally, the network is analyzed in detail using two different schemes: collaborative and cooperative.

MNVPCS: Multinode Virtual-Point–Based Charging Scheme to Prolong the Lifetime of Wireless Rechargeable Sensor Networks

2019 ◽  
Vol 63 (2) ◽  
pp. 283-294
Author(s):  
Hong-Yi Chang ◽  
Zih-Huan Hang ◽  
Yih-Jou Tzang
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Experimental Results ◽  
Sensor Nodes ◽  
Wireless Charging ◽  
Mobile Wireless ◽  
Wireless Rechargeable Sensor Networks ◽  
Virtual Point ◽  
Over Time

Abstract Wireless-charging technology can utilize a mobile wireless charging vehicle (WCV) to rescue dying nodes by supplementing their remaining energy, and using WCVs in this way forms wireless rechargeable sensor networks (WRSNs). However, a WCV in a WRSN encounters several challenges, collectively called the optimized charging problem. This problem involves a set of sensor nodes randomly distributed on the ground for which the WCV must determine an appropriate travel path to charge the sensor nodes. Because these sensor nodes have different workloads, they exhibit different energy consumption profiles over time. Resolving the above-mentioned problem requires the determination of the priority of charging the sensor nodes based on the order in which they are expected to die and subsequently finding the most efficient path to charge the sensor nodes such that sensor death is avoided for as long as possible. Furthermore, the most efficient placement of the charging point needs to be considered when planning the charging path. To address this, the proposed multinode virtual point-based charging scheme (MNVPCS) considers both the planning of an efficient charging and the best location for the charging point. Experimental results show that MNVPCS can improve the lifetime of the entire WRSN and substantially outperform other methods on this measure.

scholarly journals Maximizing Network Lifetime Operator for Wireless Sensor Networks

2013 ◽  
Vol 4 (2) ◽  
pp. 267-272
Author(s):  
Dr. Deepali Virmani
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Topology Control ◽  
Performance Metrics ◽  
Minimum Energy ◽  
Energy Utilization ◽  
Wireless Sensor ◽  
Minimum Energy Consumption

Optimizing and enhancing network lifetime with minimum energy consumption is the major challenge in field of wireless sensor networks. Existing techniques for optimizing network lifetime are based on exploiting node redundancy, adaptive radio transmission power and topology control. Topology control protocols have a significant impact on network lifetime, available energy and connectivity. In this paper we categorize sensor nodes as strong and weak nodes based on their residual energy as well as operational lifetime and propose a Maximizing Network lifetime Operator (MLTO) that defines cluster based topology control mechanism to enhance network lifetime while guarantying the minimum energy consumption and minimum delay. Extensive simulations in Java-Simulator (J-Sim) show that our proposed operator outperforms the existing protocols in terms of various performance metrics life network lifetime, average delay and minimizes energy utilization.

A deep reinforcement learning-based on-demand charging algorithm for wireless rechargeable sensor networks

2021 ◽  
Vol 110 ◽  
pp. 102278 ◽  
Author(s):  
Xianbo Cao ◽  
Wenzheng Xu ◽  
Xuxun Liu ◽  
Jian Peng ◽  
Tang Liu
Keyword(s):  
Reinforcement Learning ◽  
Sensor Networks ◽  
On Demand ◽  
Wireless Rechargeable Sensor Networks

scholarly journals A Cluster Based Localization Scheme with Partition Handling for Mobile Underwater Acoustic Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1039 ◽  
Author(s):  
Tariq Islam ◽  
Yong Kyu Lee
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Sensor Nodes ◽  
Underwater Sensor Networks ◽  
Network Partitioning ◽  
Acoustic Sensor Networks ◽  
Localization Scheme ◽  
Control Scheme ◽  
Partitioned Networks ◽  
Retransmission Control

Many applications of underwater sensor networks (UWSNs), such as target tracking, reconnaissance and surveillance, and marine life monitoring require information about the geographic locations of the sensed data. This makes the localization of sensor nodes a crucial part of such underwater sensing missions. In the case of mobile UWSNs, the problem becomes challenging, not only due to a need for the periodic tracking of nodes, but also due to network partitioning as a result of the pseudo-random mobility of nodes. In this work, we propose an energy efficient solution for localizing nodes in partitioned networks. Energy consumption is minimized by clustering unlocalized partitioned nodes and allowing only clusterheads to carry out a major part of the localization procedure on behalf of the whole cluster. Moreover, we introduce a retransmission control scheme that reduces energy consumption by controlling unnecessary transmission. The major design goal of our work is to maximize localization coverage while keeping communication overheads at a minimum, thus achieving better energy efficiency. The major contributions of this paper include a clustering technique for localizing partitioned nodes and a retransmission control strategy that reduces unnecessary transmissions.

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