Fair Energy Division Scheme to Permanentize the Network Operation for Wireless Rechargeable Sensor Networks

Almagrabi, Alaa Omran

doi:10.1109/access.2020.3027615

Cited by 15 publications

(5 citation statements)

References 31 publications

(41 reference statements)

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“…Secondly, the impact of the introduced model for optimizing of the locations of SDCs on the network behavior is evaluated and compared with random deployment of SDC and position selection model proposed in [23]. Finally, the proposed UAV' tour optimization model is compared with related schemes proposed in [26,27,33]. The relevant parameters are shown in the Tab.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Then, the charging time of charger is determined in such a way that the nodes belonging to the respective charging region is fully recharged. Furthermore, a Fair Energy Division Scheme (FEDS) is proposed in [27] aims to sustain the operation time of sensor nodes using two separate WMCs. The main objectives of FEDS are as follows: firstly, an energy share is assigned to each node, which is determined with respect to the energy consumption rate of the nodes and secondly, the energy levels and energy replenishment time of WMCs are taken into consideration in charging time optimization.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, in this work, it is attempted to optimize the trajectory and charging time of WMC during a charging cycle in such a way that keeping SDCs alive is guaranteed. Among all WPT technologies, exploiting UAVs as energy transmitters for energy supplies [26][27][28][29], is most proper for smart cities environment due to existence of obstacles between rechargeable devices. Consequently, in this paper, we utilize an energy-limited UAV equipped with wireless charger which is able to recharge the SDCs.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Static Data Collector-based Scheme in Smart Cities

Rajab¹

2022

Computers, Materials &Amp; Continua

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In the Internet of Things (IoT)-based smart city applications, employing the Data Collectors (DC) as the data brokers between the nodes and Base Station (BS) can be a promising solution to enhance the energy efficiency of energy-constrained IoT sensor nodes. There are several schemes that utilize mobile DCs to collect the data packets from sensor nodes. However, moving DCs along the hundreds of thousands of sensors sparsely distributed across a smart city is considered a design challenge in such schemes. Another concern lies in how these mobile DCs are being powered. Therefore, to overcome these limitations, we exploit multiple energy-limited Static Data Collectors (SDC) which are deployed at the locations optimized so that the energy consumption of both nodes and SDCs is minimized. Likewise, an Unmanned Aerial Vehicle (UAV)-enabled wireless power transfer is considered for sustaining the SDCs to avoid their energy depletion and data packet loss. The sustainable charging process operates periodically in each cycle so that the UAV flies from a charging station and after recharging the SDCs, it comes back to the station to be recharged. In this study, we formulate a problem to optimize the movement trajectory and charging time of UAV so that sustainable operation of SDCs during a cycle can be achieved. Unlike the prior studies, our proposed scheme determines the optimal trajectory and charging time at the beginning of each cycle which leads to increase accuracy in comparison with long-term optimization-based schemes. The outcomes of simulation experiments show that the proposed scheme achieves improved network performance in terms of data delivery ratio (12.5%); system throughput (6.6%); total energy consumption (59.19%); and network lifetime (58%) as compared to previous related works.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Static Data Collector-based Scheme in Smart Cities

Rajab¹

2022

Computers, Materials &Amp; Continua

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“…The current investigation usually uses a periodic and deterministic charging process, but the limited energy and impact of nondeterministic factors such as topological changes and sensor failures can be ignored, making them unsuitable for real networks. In [15], the goal is to minimize the number of dead sensors, while the maximum use of WCE energy is given by considering its limited energy. In this effort, the swarm reinforcement learning (SRL) method is first presented to attain the independent planning ability of WCE.…”

Section: Related Workmentioning

confidence: 99%

Offering a Demand‐Based Charging Method Using the GBO Algorithm and Fuzzy Logic in the WRSN for Wireless Power Transfer by UAV

et al. 2023

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An extremely high number of geographically dispersed, energy-limited sensor nodes make up wireless sensor networks. One of the critical difficulties with these networks is their network lifetime. Wirelessly charging the sensors continuously is one technique to lengthen the network’s lifespan. In order to compensate for the sensor nodes’ energy through a wireless medium, a mobile charger (MC) is employed in wireless sensor networks (WRSN). Designing a charging scheme that best extends the network’s lifetime in such a situation is difficult. In this paper, a demand-based charging method using unmanned aerial vehicles (UAVs) is provided for wireless rechargeable sensor networks. In this regard, first, sensors are grouped according to their geographic position using the K-means clustering technique. Then, with the aid of a fuzzy logic system, these clusters are ranked in order of priority based on the parameters of the average percentage of battery life left in the sensor nodes’ batteries, the number of sensors, and critical sensors that must be charged, and the distance between each cluster’s center and the MC charging station. It then displays the positions of the UAV to choose the crucial sensor nodes using a routing algorithm based on the shortest and most vital path in each cluster. Notably, the gradient-based optimization (GBO) algorithm has been applied in this work for intracluster routing. A case study for a wireless rechargeable sensor network has been carried out in MATLAB to assess the performance of the suggested design. The outcomes of the simulation show that the suggested technique was successful in extending the network’s lifetime. Based on the simulation results, compared to the genetic algorithm, the proposed algorithm has been able to reduce total energy consumption, total distance during the tour, and total travel delay by 26%, 17.2%, and 25.4%, respectively.

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“…A Fair Energy Division Scheme (FEDS) is introduced in [33] aims to permanentize operation time of MNs and CHs. To this end, authors dispatch two separate wireless mobile chargers namely WMC-MN and WMC-CH to recharge the MNs and CHs individually.…”

Section: B Employing Mobile Devices To Balance Energy Consumption Of Chsmentioning

confidence: 99%

A Framework for Energy-Efficient Clustering With Utilizing Wireless Energy Balancer

Alabdali

Gharaei²,

Mashat

2021

IEEE Access

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Clustering has been accepted as one of the most efficient techniques for conserving the energy of wireless sensor networks (WSNs). In cluster-based WSN, the cluster heads (CH) consume more energy compared with normal nodes, which results in rising their energy consumption and reducing the network lifetime. There are several energy-efficient routing schemes which have been presented to reduce the energy consumption of CHs. In such schemes, further clustering of CHs will result in reducing the energy consumption of CHs. However, this rule has not been considered in previous related works. Likewise, unbalanced energy consumption of CHs is one the main design challenges which leads to increase the wasted energy and premature network death. A number of recent studies have sought to improve the balanced energy via utilizing devices equipped with large and expensive energy harvesters, which leads to extra cost Therefore, a feasible solution is needed to achieve balanced energy consumption of CHs. Consequently, to overcome the aforementioned limitations, we proposed a framework for energy-efficient clustering by utilizing a wireless energy balancer. Firstly, an n-level clustering is presented which results in fully leveraging and reducing the energy consumption of CHs. Secondly, an energy balancer is utilized in order to reduce the wasted energy to the fullest possible extent by zeroing the variance between the remaining energy of CHs. The performance of the proposed scheme has been compared against CMS2TO and DGOB mechanisms. Simulation results show that the proposed scheme achieved improved performance in terms of network lifetime by 20%, total energy consumption by 52%, network overhead by 20%, computation time by 46%, and wasted energy by 86% compared to existing schemes. In conclusion, the proposed framework proves to be a viable solution for enhancing the network lifetime and energy efficiency.

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Fair Energy Division Scheme to Permanentize the Network Operation for Wireless Rechargeable Sensor Networks

Cited by 15 publications

References 31 publications

Energy-Efficient Static Data Collector-based Scheme in Smart Cities

Energy-Efficient Static Data Collector-based Scheme in Smart Cities

Offering a Demand‐Based Charging Method Using the GBO Algorithm and Fuzzy Logic in the WRSN for Wireless Power Transfer by UAV

A Framework for Energy-Efficient Clustering With Utilizing Wireless Energy Balancer

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