A Bayesian Compressive Data Gathering Scheme in Wireless Sensor Networks With One Mobile Sink

Gu, Xiaofeng; Zhou, Xiaofeng; Yuan, Baozong; Sun, Yanjing

doi:10.1109/access.2018.2867538

Cited by 16 publications

(16 citation statements)

References 27 publications

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“…Besides, other parameters, such as the network topology, and the network energy distribution can be also applied in determining the Sink s moving path in advance, such as the (1 )   algorithm [92] which establishes the sojourn locations and the corresponding sojourn time according to the network performance requirements. Similar strategies included CSPLI [93] [94] and MSFLA [95] which takes the network delay into consideration. In addition, the strategies which combine the mobile and static Sinks can be utilized to improve EE further, such as the Energy-saving Strategy by Combining Mobile and Static sink scheme (ESCMS) [96] .…”

Section: Classifications Of Mnasmentioning

confidence: 99%

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

Lin

Wang

Min

et al. 2020

ACM Trans. Sen. Netw.

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A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) which are not equipped with any energy harvesting modules was conducted in this paper. Firstly, a novel generic mathematical definition of Energy Efficiency was proposed, which took the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, it is for the first time that the Energy Efficiency of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network were expounded at length. Accordingly, the concepts concerning the Energy Efficiency, namely the Energy-efficient Means, the Energy-efficient Tier, and the Energy-efficient Perspective, were proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 were tracked and reviewed. Specifically, they were classified into five categories, i.e., the Energy-Efficient Media Access Control (EEMAC) protocol, the Mobile Node Assistance Scheme (MNAS), the Energy-Efficient Clustering Scheme (EECS), the Energy-Efficient Routing Scheme (EERS), and the Compress Sensing based Scheme (CSS) respectively. A detailed elaboration on both the basic principle and the evolution of them was made. Finally, further analysis on the categories was made and the related conclusion was drawn. To be specific, the interdependence among them, the relationships between each of them and the Energy-efficient Means, the Energy-efficient Tier as well as and the Energy-efficient Perspective were analyzed in detail. Besides, the specific applicable scenarios for each of them and the relevant statistics analysis were detailed. The proportion and the number of citations of each category were illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm, and the feasible direction concerning the energy efficiency in the future were pointed out.

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Section: Classifications Of Mnasmentioning

confidence: 99%

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

Lin

Wang

Min

et al. 2020

ACM Trans. Sen. Netw.

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“…With regard to the data collection methods for the mobile sink, analytical models have been proposed in [11][12][13][14][15][16][17][18][19][20]. In [11], an energy-aware path construction algorithm is proposed to prolong the network lifetime in WSNs with mobile sink by selecting an appropriate set of data collection points, constructing a data collection path, and collecting data from the points burdened with data.…”

Section: Related Workmentioning

confidence: 99%

“…In [12], a mobile data gathering algorithm with multi-hop transmission is proposed to reduce the delay time of data gathering in WSNs by visiting the convergence area of sensors' communication ranges. In [13], the authors study the compressive data gathering problem in terms of Bayesian theory for WSNs with mobile sink. The main goal of this scheme is to reduce the network latency by selecting schedule of gathering nodes.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sink

Chang

Jeng

Sheu

et al. 2020

Preprint

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Wireless sensor networks with mobile sink provide a mobile device to move into the sensing area for collecting the sensing data. It increases the flexibility and convenience of data gathering in such systems. Taking into account the energy consumption of mobile sink, the moving distance of mobile sink must be reduced efficiently. Hence, it is important and necessary to develop an efficient path planning scheme for the mobile sink in large-scale wireless sensor networks systems. According to the greedy-based algorithms, we adopt an angle bisector concept to create the moving path for the mobile sink. In this paper, a novel efficient data collection path planning scheme with an inner center approach is proposed to reduce the moving distance and to prolong the lifetime of mobile sink in wireless sensor networks. The relationship among moving path, moving distance, and number of sensor nodes are analyzed and discussed. Considering the communication range limitation of sensor nodes and the sensing area with obstacles, the proposed scheme makes an adaptive decision for creating the moving path of mobile sink. Simulation results demonstrate that the reasonable moving path planning can be achieved and the moving distance can be reduced for a mobile sink in wireless sensor networks.

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“…With regard to the data collection methods for mobile sinks, analytical models have been proposed in [11][12][13][14][15][16][17][18][19][20]. In [11], an energy-aware path construction algorithm was proposed to prolong the lifetimes of WSNs with mobile sinks by selecting an appropriate set of data collection points, constructing a data collection path, and collecting data from the points burdened with too much data.…”

Section: Related Workmentioning

confidence: 99%

“…In [12], a mobile data gath-ering algorithm with multihop transmission was proposed to reduce the delay time of data gathering in WSNs by visiting the convergence area of sensors' communication ranges. In [13], the authors studied the compressive data gathering problem in terms of Bayesian theory for WSNs with mobile sinks. The main goal of this scheme is to reduce the latency of the network by manually selecting the schedule of gathering nodes.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sinks

Chang

Jeng

Sheu

et al. 2020

Preprint

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Wireless sensor networks with mobile sinks enable a mobile device to move into the sensing area for the purpose of collecting the sensing data. Mobile sinks increase the flexibility and convenience of data gathering in such systems. Taking the energy consumption of the mobile sink into account, the moving distance of the mobile sink must be reduced efficiently. Hence, it is important and necessary to develop an efficient path planning scheme for mobile sinks in large-scale wireless sensor network systems. According to several greedy-based algorithms, we adopt an angle bisector concept to create the moving path for the mobile sink. In this paper, a novel and efficient data collection path planning scheme is proposed to reduce the moving distances and to prolong the lifetimes of mobile sinks in wireless sensor networks. Considering the communication range limitations of sensor nodes and the obstacles within sensing areas, we design an inner center path planning algorithm to reduce the moving distance for the mobile sink. A back-routing avoidance method is included to address the moving path backpropagation problem. We account for the obstacles in sensing area. The reference point of obstacle avoidance is employed to address the obstacle problem. The proposed scheme makes an adaptive decision for creating the moving path of the mobile sink. A suitable moving path planning scheme can be achieved, and the moving distance of the mobile sink can be reduced. The proposed scheme is promising in large-scale wireless sensor networks. When the number of sensor nodes in the sensing area is increased by 50, the proposed scheme yields an average moving distance that is 1.1 km shorter than that of the heuristic tour-planning algorithm, where the sensing area is 5 km × 5 km. Simulation results demonstrate that the proposed data collection path planning scheme outperforms the previously developed greedy-based scheme in terms of the moving paths and moving distances of mobile sinks in wireless sensor networks.

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A Bayesian Compressive Data Gathering Scheme in Wireless Sensor Networks With One Mobile Sink

Cited by 16 publications

References 27 publications

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sink

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sinks

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