Distributed Cooperative Wireless Charging for the Mine Internet of Things

Hu, Qingsong; Yang, Junwei; Zhang, Ran; Chen, Wei; Li, Binghao

doi:10.1109/access.2019.2923229

Cited by 8 publications

(11 citation statements)

References 23 publications

(31 reference statements)

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“…KMEC‐Charging adopts a cluster‐first route‐second strategy to charge the sensor nodes efficiently under the constraint of MC capacity; the cluster‐first strategy shares common features with SCTD‐Charging of choose candidate charging nodes. The simulation parameters and their default settings are listed in Table 1 [27–29]. Event distortion : Figure 10 shows the effect of the number of active nodes on the distortion under different values of distance parameter of 10, 50, 100, 500, 1000, 5000 and 10,000.…”

Section: Performance Analysismentioning

confidence: 99%

“…OfWR‐Charging can effectively shorten the moving distance compared to KMEC‐Charging by using the Hamiltonian circle to find the nearest sensor to charge. SCTD‐Charging has the shortest moving distance because the number of participating observation nodes is reduced by the spatial correlation. Energy usage efficiency : The energy usage effectiveness (EUE) refers to the ratio of the charged power of nodes to the consumed power of the MC, defined as [27, 28]

EUE = \frac{E^{p l}}{E^{p l} + E^{o h}}

where

E^{p l}

is the energy an MC transfers to a node and

E^{o h}

is the energy loss in the charging process, including the energy consumption in movement (

E_{m}^{o h}

) and the loss due to limited efficiency in charging (

E_{c}^{o h}

). We note that

E_{m}^{o h}

consists of the energy consumed by the movement module and that consumed by other electronic modules of the MC, noted as

E_{m}^{o h 1}

and

E_{m}^{o h 2}

, respectively. As can be seen from Figure 12(a), the EUE of the SCTD‐Charging is much higher than that of the other two charging methods, because the space correlation reduces the node number participating in the event detection.…”

Section: Performance Analysismentioning

confidence: 99%

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Space‐correlation‐based joint data transmission and on‐demand charging for rechargeable wireless sensor networks

Yang

et al. 2020

IET Communications

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It is of great importance to power the nodes of the rechargeable wireless sensor network to detect events continuously in the area of interest. This paper proposes a joint data transmission and on‐demand charging algorithm based on the space correlation. The new algorithm optimises the event detection, data forwarding and node charging jointly to improve the charging efficiency. First, the active nodes participating in the event detection are selected using an improved iterative node selection method to reduce the number of nodes working concurrently. Then, the greedy data transmission scheme based on grid partition is proposed to transmit the observed data to the sink node. Finally, the nodes in the networks are charged using the on‐demand charging method based on grid partition, which greatly decreases the charging frequency and energy loss of the mobile charger. The simulation results demonstrate that the proposed method has superior performance in the distance travelled by the mobile charger, the energy utilisation, the average energy consumption of the mobile charger and the node charging latency.

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Section: Performance Analysismentioning

confidence: 99%

EUE = \frac{E^{p l}}{E^{p l} + E^{o h}}

where

E^{p l}

is the energy an MC transfers to a node and

E^{o h}

is the energy loss in the charging process, including the energy consumption in movement (

E_{m}^{o h}

) and the loss due to limited efficiency in charging (

E_{c}^{o h}

). We note that

E_{m}^{o h}

consists of the energy consumed by the movement module and that consumed by other electronic modules of the MC, noted as

E_{m}^{o h 1}

and

E_{m}^{o h 2}

Section: Performance Analysismentioning

confidence: 99%

Space‐correlation‐based joint data transmission and on‐demand charging for rechargeable wireless sensor networks

Yang

et al. 2020

IET Communications

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“…

E_{k}^{O {normalM}^{normal′}}

is the charged power of

Nod e_{k}

during the period of

t_{k}^{O {normalM}^{normal′}}

when MC moves from point O to the M′. The charged power when the MC moves from M to M′ can be expressed as [8]

\begin{matrix} right leftthickmathspace.5em \\ E_{k}^{normalM} & = E_{k}^{MO} + E_{k}^{normalO M^{'}} \\ = \int_{0}^{t_{MO}} \frac{α}{{()(, \sqrt{{(\sqrt{r_{ch}^{2} - w^{2} / 4} - v t)}^{2} + w^{2} / 4} + β)}^{2}} d t \\ + \int_{0}^{t_{normalO M^{'}}} \frac{α}{{()(, \sqrt{v^{2} t^{2} + w^{2} / 4} + β)}^{2}} d t \end{matrix}

E_{k}^{M}

cannot meet the energy demand of

Nod e_{k}

, the controlled MC must be scheduled to charge it. Similar to the opportunistic MC, the controlled MC can also charge

Nod e_{k}

during the moving process.…”

Section: Hybrid Charging Of Controlled MC and Opportunistic Mcmentioning

confidence: 99%

“…To charge the Mine IoT nodes wirelessly, a certain number of stationary chargers (SCs) need to be deployed in the coal mine roadway, which are directly connected to the underground power grid, and have a stable power supply [8, 9]. Each SC also needs to be equipped with several mobile chargers (MCs).…”

Section: Introductionmentioning

confidence: 99%

“…The coal mine roadway can be divided into sections, and multiple MCs take turns to charge the nodes in these sections. Considering the crowding problem of MCs in existing wireless charging methods for one‐dimensional space, a distributed cooperative wireless charging method was suggested in [8]. Furthermore, a method of directional mobile charging for the Mine IoT based on smart antenna was proposed to increase the charging efficiency [9].…”

Section: Introductionmentioning

confidence: 99%

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