Network Under Limited Mobile Devices: A New Technique for Mobile Charging Scheduling With Multiple Sinks

Nguyen, Ngoc‐Tu; Liu, Bing-Hong; Pham, Van-Trung; Huang, Chen-Yong

doi:10.1109/jsyst.2016.2628043

Cited by 50 publications

(13 citation statements)

References 28 publications

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“…Showing this problem is NPC [ 45 ]. Combining energy replenishment and data collection with multiple sinks was also shown to be an NP-Hard problem [ 13 ], requiring full knowledge of sensors’ power levels. A battery-aware mobile energy replenishment and data collection method is proposed in [ 46 ] to visit locations such that sensors within the MCR range are charged, while decisions are made according to real-time energy information obtained when visiting head nodes, moving to areas with lower power levels.…”

Section: Related Workmentioning

confidence: 99%

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Extending Wireless Rechargeable Sensor Network Life without Full Knowledge

Najeeb¹,

Detweiler²

2017

Sensors

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When extending the life of Wireless Rechargeable Sensor Networks (WRSN), one challenge is charging networks as they grow larger. Overcoming this limitation will render a WRSN more practical and highly adaptable to growth in the real world. Most charging algorithms require a priori full knowledge of sensor nodes’ power levels in order to determine the nodes that require charging. In this work, we present a probabilistic algorithm that extends the life of scalable WRSN without a priori power knowledge and without full network exploration. We develop a probability bound on the power level of the sensor nodes and utilize this bound to make decisions while exploring a WRSN. We verify the algorithm by simulating a wireless power transfer unmanned aerial vehicle, and charging a WRSN to extend its life. Our results show that, without knowledge, our proposed algorithm extends the life of a WRSN on average 90% of what an optimal full knowledge algorithm can achieve. This means that the charging robot does not need to explore the whole network, which enables the scaling of WRSN. We analyze the impact of network parameters on our algorithm and show that it is insensitive to a large range of parameter values.

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Section: Related Workmentioning

confidence: 99%

“…This renders the charging problem hard, nondeterministic polynomial complete (NPC) [ 12 ], causing the solution to be infeasible for large-scale WRSN. Even when utilizing several MCR units, the problem is still shown to be nondeterministic polynomial hard (NP-Hard) [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Extending Wireless Rechargeable Sensor Network Life without Full Knowledge

Najeeb¹,

Detweiler²

2017

Sensors

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“…Authors in [19,20] have studied the network to provide adequate quality of service (using energy replenishment and data collection) through limited mobile devices in WRSNs with multiple sinks. Scheduling the mobile devices' recharging and data collection capabilities ensures that the total weight of the recharged sensors can be operated without time limits being maximized.…”

Section: Related Workmentioning

confidence: 99%

“…However, the scheme only considered point coverage, similar to that proposed by [13] (in which area coverage is considered). The nodes were grouped in sets, a staircase-based scheduling model was implemented, and a fixed number of backup nodes were deployed in sets.Authors in [19,20] have studied the network to provide adequate quality of service (using energy replenishment and data collection) through limited mobile devices in WRSNs with multiple sinks. Scheduling the mobile devices' recharging and data collection capabilities ensures that the total weight of the recharged sensors can be operated without time limits being maximized.The mobile robot is utilized to perform replacement and recharging tasks.…”

mentioning

confidence: 99%

Adopting Sector-Based Replacement (SBR) and Utilizing Air-R to Achieve R-WSN Sustainability

2017

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Sensor replacement in the rechargeable wireless sensor network (R-WSN) is important to provide continuous sensing services once sensor node failure or damage occurs. However, satisfactory solutions have not been found yet in developing a sustainable network and effectively prolonging its lifetime. Thus, we propose a new technique for detecting, reporting, and handling sensor failure, called sector-based replacement (SBR). Base station (BS) features are utilized in dividing the monitoring field into sectors and analyzing the incoming data from the nodes to detect the failed nodes. An airplane robot (Air-R) is then sent to a replacement task trip. The goals of this study are to (i) increase and guarantee the sustainability of the R-WSN; (ii) rapidly detect the failed nodes in sectors by utilizing the BS capabilities in analyzing data and achieving the highest performance for replacing the failed nodes using Air-R; and (iii) minimize the Air-R effort movement by applying the new field-dividing mechanism that leads to fast replacement. Extensive simulations are conducted to verify the effectiveness and efficiency of the SBR technique. Information 2017, 8, 70 2 of 16the potential for actual industrial applications [7,8]. This technology possesses high-energy transfer efficiency, even under omni-directional transmission; it does not require line of sight, and it is insensitive to the neighboring environment [9,10]. The charging energy of R-WSNs is reliable and stable.Progress in wireless power transfer techniques has advanced research activities in the direction of battery recharging, with high expectations for its application to WSNs. A hybrid charging scheme combines charging by a mobile charger with a directional antenna and energy trading (e.g., transferring and harvesting between neighboring sensor nodes) [11]. Sensor replacement techniques have also been widely studied as a means of providing service continuity in the network. Recharging or replacing the sensor nodes that are running out of energy can be identified and researched as two main objectives. Mobile robots are used as solutions for the execution of either recharging or replacing tasks automatically and autonomously to maintain the WSN, thus reducing human intervention [12].This study focuses on the manner in which R-WSNs are deployed and efficiently serve a complex geography. R-WSNs can collect information, monitor, and perform other multiple tasks. They are sustainable networks and have high performance, which are important factors for WSNs. For example, in a forest-monitoring system, we need to deploy numerous sensor nodes to cover the entire sensing field. We assume that all nodes have the same amount of energy, capacity, and transmission capabilities. These nodes can determine their location automatically from the first time after deployment. In the case of detecting failure or damaged nodes in the network, several problems may affect network sustainability, data collection, and field-monitoring results. Our approach shows good results in detecting...

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“…Several areas of application may include various critical clip environmental monitoring, hospital management forest fire detection, cracker fire monitoring, military surveillance [1]- [6]. The foremost operation is the periodically information collection, reported my sensors to the sink node [7]- [10]. The latency of information collection becomes predominates nowadays.…”

Section: Introductionmentioning

confidence: 99%

Prioritized scheduling scheme for critical and non-critical information packets

Saravanan

Periyakgounder

2018

IJET

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The process of energy saving mechanisms is been utilized by proper planning to transmit both critical clip and non-real infor-mation by reading overheads there by reducing throughput and bandwidth in large scale critical clip networks. The existing bundle sched-uling mythologies used were based on the First -in First-out (FIFO) manner in such cases the critical information at particular instant cannot be processed quickly but proposed system is designed in such a way it consists of three tier precedence structure .the critical in-formation bundles are placed in the higher status prioritizing queue and processed immediately and sent to the destination node where oth-er information bundles are given less precedence on the basis of position for non critical information bundles. The proposed scheme, ener-gy efficient in reducing the number of transmission by using merger technique. The lowest precedence bundles are processed after the higher precedence bundles. The proposed algorithm proves its uniqueness based on end -to-end delay than Energy efficient wake up scheduling MAC (EEWS) and Traffic Adaptive MAC protocol (TAMAC)

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Network Under Limited Mobile Devices: A New Technique for Mobile Charging Scheduling With Multiple Sinks

Cited by 50 publications

References 28 publications

Extending Wireless Rechargeable Sensor Network Life without Full Knowledge

Extending Wireless Rechargeable Sensor Network Life without Full Knowledge

Adopting Sector-Based Replacement (SBR) and Utilizing Air-R to Achieve R-WSN Sustainability

Prioritized scheduling scheme for critical and non-critical information packets

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