Mobile data gathering and energy harvesting in rechargeable wireless sensor networks

Liu, Yong; Lam, Kam-Yiu; Han, Song; Chen, Qingchun

doi:10.1016/j.ins.2019.01.014

Cited by 55 publications

(39 citation statements)

References 34 publications

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“…What's more, closer distance and less remaining energy also lead to higher priority. The initial priority P(i) is calculated according to formula (6).…”

Section: Determining Node Charging Ordermentioning

confidence: 99%

“…For those nodes which have a unique initial priority, P(i) is its final priority. For the nodes with the same initial priority, we need to sort them again, using formula (6) to calculate the node's exact priority P a (i).…”

Section: Determining Node Charging Ordermentioning

confidence: 99%

“…In formula (7), if a node has little energy, the WCV will certainly give priority to it. We can adjust the weight of the energy priority in formula (6) to maintain a very low sensor node failure rate.…”

Section: Determining Node Charging Ordermentioning

confidence: 99%

See 2 more Smart Citations

Prolonging lifetime for wireless rechargeable sensor networks through sleeping and charging scheduling

Wang

Lin

et al. 2020

Int J Communication

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Summary Prolonging network lifetime is a fundamental requirement in wireless sensor network (WSN). Existing charging scheduling algorithms suffer from high node redundancy and energy consumption issues. In this paper, we study WSN charging problem from the perspectives of energy conservation combined with energy replenishment scheduling. Firstly, we detect the redundant nodes whose energy is wasted in the network functionality and develop a K‐covering redundant nodes sleeping scheduling algorithm (KRSS) for reducing energy. Secondly, we employed multiple wireless charging vehicles (WCVs) for energy replenishment and optimize the charging scheduling algorithm to prevent any exhaustion of nodes, and we proposed a distance and energy–oriented charging scheduling algorithm (DECS) with multiple WCVs. Simulation experiments are conducted to show the advantages of the proposed KRSS+DECS, confirming that our scheme is capable of removing redundant nodes, lowering node failures, and prolonging network lifetime.

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“…What's more, closer distance and less remaining energy also lead to higher priority. The initial priority P(i) is calculated according to formula (6).…”

Section: Determining Node Charging Ordermentioning

confidence: 99%

Section: Determining Node Charging Ordermentioning

confidence: 99%

See 1 more Smart Citation

Prolonging lifetime for wireless rechargeable sensor networks through sleeping and charging scheduling

Wang

Lin

et al. 2020

Int J Communication

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“…A joint data gathering and energy harvesting (JoDGE) problem with a Mobile BS is studied in [13], which aims to enhance the network lifetime by taken into consideration the power allocation, time scheduling problems. To achieve this goal, first the optimal joint data collection and energy harvesting scheduling algorithm is presented.…”

Section: Related Workmentioning

confidence: 99%

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

Almagrabi

2020

IEEE Access

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In the past years, the energy constraint problem is known as a design issue of Wireless Sensor Networks (WSNs) due to equipping the sensor nodes with limited power supplies. During the last few years, Wireless Rechargeable Sensor Networks (WRSNs) have gained researchers attention. In a WRSN, the sensor nodes are equipped with RF circuits, which enables them to receive energy from Wireless Mobile Chargers (WMC). However, most of the existing wireless charging algorithms consider the unlimited power budget for WMCs, which is the opposite of feasibility of a real network environment. Likewise, most of the previous works fail to take full advantage of WMC as it starts recharging the nodes when their energy level reaches a threshold, which leads to increasing the inactive time of WMC. Moreover, although previous works employed WMCs, the network lifetime is limited. However, optimal division of the energy of WMC among nodes can guarantee the perpetual network operation. Therefore, proposing an efficient method that jointly solves these challenges is required. In this paper, a new Fair Energy Division Scheme (FEDS) is presented, which undertakes the permanent network operation by optimizing the energy division at the beginning of each cycle. Simulation results exhibit that FEDS achieves perpetual network lifetime. In addition, the proposed scheme improves energy efficiency, (25%) compared to Uneven Cluster-based Mobile Charging (UCMC) and (75%) compared to Nearest-Job-Next Preemption NJNP; travelling time of WMC, (50%) compared to UCMC and (75%) compared to NJNP. In conclusion, the proposed protocol significantly improves network. INDEX TERMS perpetual network operation; wireless rechargeable sensor networks; mobile charger scheduling.

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“…To solve this problem, two significant strategies are usually proposed to satisfy energy-efficient requirements in applications. One strategy is to introduce ME, like what proposed in [10][11][12] to achieve energy efficiency for data collection. The other one is to build clustering algorithm, which reduces and balances energy consumption in order to prolong the lifetime of WSNs [13].…”

Section: Introductionmentioning

confidence: 99%

Load-Balancing Rendezvous Approach for Mobility-Enabled Adaptive Energy-Efficient Data Collection in WSNs

2020

KSII TIIS

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The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs.

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Mobile data gathering and energy harvesting in rechargeable wireless sensor networks

Cited by 55 publications

References 34 publications

Prolonging lifetime for wireless rechargeable sensor networks through sleeping and charging scheduling

Prolonging lifetime for wireless rechargeable sensor networks through sleeping and charging scheduling

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

Load-Balancing Rendezvous Approach for Mobility-Enabled Adaptive Energy-Efficient Data Collection in WSNs

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