Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks

Zhong, Ping; Li, Yating; Liu, Weirong; Duan, Guihua; Chen, Yingwen; Xiong, Naixue

doi:10.3390/s17081881

Cited by 37 publications

(27 citation statements)

References 49 publications

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“…Guo [23] presented a framework that integrated wireless charging and mobile data gathering, which analyzed the causes of energy balance. An anchor selection algorithm that considered neighbor distribution and residual energy was proposed to collect zonal data [24]. Xie [25] pursued a novel optimization mode by considering the moving path, traffic routing, and charging time.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm in Rechargeable Wireless Sensor Networks for Internet of Things

Tang

Guo

et al. 2020

Applied Sciences

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Great improvement recently appeared in terms of efficient service delivery in wireless sensor networks (WSNs) for Internet of things (IoT). The IoT is mainly dependent on optimal routing of energy-aware WSNs for gathering data. In addition, as the wireless charging technology develops in leaps and bounds, the performance of rechargeable wireless sensor networks (RWSNs) is greatly ameliorated. Many researches integrated wireless energy transfer into data gathering to prolong network lifetime. However, the mobile collector cannot visit all nodes under the constraints of charging efficiency and gathering delay. Thus, energy consumption differences caused by different upload distances to collectors impose a great challenge in balancing energy. In this paper, we propose an adaptive dual-mode routing-based mobile data gathering algorithm (ADRMDGA) in RWSNs for IoT. The energy replenishment capability is reasonably allocated to low-energy nodes according to our objective function. Furthermore, the innovative adaptive dual-mode routing allows nodes to choose direct or multi-hop upload modes according to their relative upload distances. The empirical study confirms that ADRMDGA has excellent energy equilibrium and effectively extends the network lifetime.

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Section: Related Workmentioning

confidence: 99%

Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm in Rechargeable Wireless Sensor Networks for Internet of Things

Tang

Guo

et al. 2020

Applied Sciences

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“…As discussed earlier, the sensor nodes are working with batteries, which are energy limited [9][10][11]. For solving the battery constrained issues and energy refilling problems in wireless rechargeable sensor networks, various researches have been accomplished and models are developed.…”

Section: Related Workmentioning

confidence: 99%

Schedule-based Optimized Node Recharging Model (SONRM) For Increasing Longevity of Wireless Rechargeable Sensor Network (WRSN)

Sakthidharan¹,

Reddy²

2019

IJIES

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There has been a tremendous growth in research concern in Wireless Sensor Networks (WSNs), due to the broad range of applications in distinctive areas such as environmental observation, security domains, home automation, etc. Despite advantages, the prevalent adoption of WSN, specifically in remote and isolated regions, has become the major challenge, because of this battery constrained network with limited energy source. As advancement, the nodes are framed under Wireless Rechargeable Sensor Network (WRSN) to enhance the lifetime of sensor nodes with the establishment of charging mechanisms. With those considerations, this paper presents a Schedule-based Optimized Node Recharging Model (SONRM), in accordance to the condition of limited capability of mobile charging. In the process of the construction of charging circuit, the SONRM effectively considers the dynamic changes of energy utilization among nodes at different factors and develops a Recharge Scheduling Algorithm (RSA) that helps in finding the next Energy Starving Sensor Node (ESSN) to be recharged. Simultaneously, a model for the establishment of feasible circuit for recharging has also been done for ensuring the effectiveness of charging. During the operation, the node mortality can be reduced by the proposed model with the incorporation of dynamic charging threshold. When compared with the existing mechanisms, the SONRM attains better results on analysing decisive factors such as network longevity, service time, efficient charging, throughput and recharging time.Keywords: Wireless rechargeable sensor network (WRSN), Schedule-based optimized node recharging model (SONRM), Recharge scheduling algorithm (RSA), Energy starving sensor node (ESSN).

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“…For this reason, it is unreasonable to centralize data collection and recharging on one mobile device, which would result in a higher data gathering delay, data packet loss as well as poor network performance, especially in densely deployed network. Thus, in 2016 and 2017, Wang et al [ 18 ] and Zhong et al [ 31 ] have respectively proposed two types of data collection models by employing multiple MDCs and WCVs. Furthermore, Mehrabi et al [ 20 ] and Khan et al [ 32 ] have also put forward similar methods to avoid high delay during data collection caused by low recharging efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, when the sensing mode changes, this method is not fully applicable. In [ 31 ], the rectangular network has been divided into several parts according to the number of MDCs. Subsequently, a twice-partition algorithm based on center points and an Anchor Selection algorithm based on the tradeoff between Neighbor Amount and residual Energy (AS-NAE) have been proposed respectively to handle the complex scheduling problem of multiple vehicles.…”

Section: Multi-mdcs Based Data Collection Strategy With Maximum Dementioning

confidence: 99%

A High-Efficiency Data Collection Method Based on Maximum Recharging Benefit in Sensor Networks

Sha

Wang

Zhang

et al. 2018

Sensors

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To reduce time delays during data collection and prolong the network lifetime in Wireless Rechargeable Sensor Networks (WRSNs), a type of high-efficiency data collection method based on Maximum Recharging Benefit (DCMRB) is proposed in this paper. According to the minimum number of the Mobile Data Collectors (MDCs), the network is firstly divided into several regions with the help of the Virtual Scan Line (VSL). Then, the MDCs and the Wireless Charging Vehicles (WCVs) are employed in each region for high efficient data collection and energy replenishment. In order to ensure the integrity of data collection and reduce the rate of packet loss, a speed adjustment scheme for MDC is also proposed. In addition, by calculating the adaptive threshold of the recharging request, those nodes with different energy consumption rates are recharged in a timely way that avoids their premature death. Finally, the limited battery capacity of WCVs and their energy consumption while moving are also taken into account, and an adaptive recharging scheme based on maximum benefit is proposed. Experimental results show that the energy consumption is effectively balanced in DCMRB. Furthermore, this can not only enhance the efficiency of data collection, but also prolong the network lifetime compared with the Energy Starvation Avoidance Online Charging scheme (ESAOC), Greedy Mobile Scheme based on Maximum Recharging Benefit (GMS-MRB) and First-Come First-Served (FCFS) methods.

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Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks

Cited by 37 publications

References 49 publications

Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm in Rechargeable Wireless Sensor Networks for Internet of Things

Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm in Rechargeable Wireless Sensor Networks for Internet of Things

Schedule-based Optimized Node Recharging Model (SONRM) For Increasing Longevity of Wireless Rechargeable Sensor Network (WRSN)

A High-Efficiency Data Collection Method Based on Maximum Recharging Benefit in Sensor Networks

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