A Linear Programming with Alliance Strategy Coverage in Wireless Sensor Networks

Sun, Zeyu; Yang, Tao; Dong, Xiaoming

doi:10.14257/ijfgcn.2016.9.6.27

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…Sun et al proposed a linear Fig. 1 Coverage control structure of mobile sensor networks programming optimization coverage scheme (LPOCS) [19] which divides all of the sensor nodes into many subsets. The connectivity and coverage for a specific target within every subset can then be guaranteed.…”

Section: Related Workmentioning

confidence: 99%

“…6 that the number of working nodes is only related with the desired QoC, instead of the number of deployed nodes. Furthermore, the number of working nodes is almost equal to the To further verify the enhancement of the equalization for the network power, we compare the ENCP with the algorithms in [18,19]. The simulations are performed based on three different sensing fields, and the coverage probability is fixed to 99.9%.…”

Section: Encp Evaluation Systemsmentioning

confidence: 99%

“…Figures 8, 9, 10, and 11 depict the relationship between the network lifetime and the target nodes within different sensing fields. The algorithms we focus on are the Multiple Target Tracking Algorithm (MTTA) [18] and linear programming optimization coverage scheme (LPOCS) [19]. It can be observed from Fig.…”

Section: Encp Evaluation Systemsmentioning

confidence: 99%

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ENCP: a new Energy-efficient Nonlinear Coverage Control Protocol in mobile sensor networks

Sun

Zhao

Xing

2018

J Wireless Com Network

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The node deployment in mobile sensor networks (MSNs) is mostly performed in a random method. However, a large number of redundant nodes may exist due to the randomness. As a result, severe data congestion may be caused and the quality of coverage (QoC) is undermined. In order to solve this QoC problem, we propose an Energy-efficient Nonlinear Coverage Control Protocol (ENCP). This protocol utilizes the normal distribution to calculate the minimal number of sensors which is required to guarantee coverage over the monitoring area. We also balance the node energy consumption and achieve the collaborative scheduling among all the sensor nodes. Meanwhile, when a certain QoC is guaranteed, we present the calculation model for the normal distribution of the sensing ranges and the proportional relationship between different parameters in the QoC function. Finally, simulation results show that the ENCP could not only improve the network QoC and network coverage rate but also effectively control the energy exhaustion at the nodes. Therefore, the network lifetime can be effectively prolonged.

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Section: Related Workmentioning

confidence: 99%

Section: Encp Evaluation Systemsmentioning

confidence: 99%

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ENCP: a new Energy-efficient Nonlinear Coverage Control Protocol in mobile sensor networks

Sun

Zhao

Xing

2018

J Wireless Com Network

Self Cite

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“…In this protocol, a sparsest measurement matrix is constructed according to the reception condition at the Sink end, which is further employed to reconstruct the original sensing data for all the nodes in the network and alleviate the influence of packet losses on CS data reconstruction [19][20][21][22]. However, this algorithm is only limited to the application scenarios where the spatial correlation of the sensing data in the network is relatively strong.…”

Section: Related Workmentioning

confidence: 99%

CS‐PLM: Compressive Sensing Data Gathering Algorithm Based on Packet Loss Matching in Sensor Networks

Sun

Tao

Xiong

2018

Wireless Communications and Mobile Computing

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The data transmission process in Wireless Sensor Networks (WSNs) often experiences errors and packet losses due to the environmental interference. In order to address this problem, we propose a Compressive Sensing data gathering algorithm based on Packet Loss Matching (CS-PLM). It is proven that, under tree routing, the packet loss on communication links would severely undermine the data reconstruction accuracy in Compressive Sensing (CS) based data gathering process. It is further pointed out that the packet loss in CS based data gathering exhibits the correlation effect. Meanwhile, we design a sparse observation matrix based on packet loss matching and verify that the designed matrix satisfies the Restricted Isometry Property (RIP) with a probability arbitrarily close to 1. Therefore, reliable transmission of the compressed data can be guaranteed by adopting the multipath backup routing among CS nodes. It is shown in the simulation results that, with a 60% packet loss ratio of the link, the CS-PLM algorithm can still ensure the effective reconstruction of the data gathered by the CS algorithm and the relative reconstruction error is lower than 5%. Therefore, it is verified that the proposed algorithm could effectively alleviate the sensitivity to packet losses for the CS based data gathering algorithm on unreliable links.

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“…Once the speculation task starts, the system will have two identical tasks executed in parallel at the same time. If there is a task is completed, then another concurrent task will be terminated by the dispatcher [4][5][6]. The speculation mechanism speeds up the backward Map or Reduce task execution time, thereby improving the system performance.…”

Section: Speculation Execution In the Mapreduce Systemmentioning

confidence: 99%

Job Performance Optimization Method Based on Data Balance in the Wireless Sensor Networks

Zhao

2017

Int. J. Onl. Eng.

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Abstract-In the wireless sensor network, the representative MapReduce computing model based on data center has been widely used in large-scale data processing. In the data transmission phase, the wireless sensor network system uses the hash method to distribute data for each Reduce task based on the number of Reduce tasks. This data partitioning method based on the hash function results in non-uniform distribution of the output data in the data transmission phase and further leads to skewing of the input data in the Reduce task. Data skew will result in load imbalance in the Reduce phase and causes the system performance to degrade. In order to eliminate the data skew problem in the Reduce phase, this paper presents a load balancing method, which consists of two parts: the virtual partitioning method based on the consistent hashing and the heterogeneity-aware loads balancing (HLB) algorithm. The experimental results show that the proposed method can eliminate the data skew in the Reduce phase and distribute the load equitably for each Reduce task. In addition, the method produces less system overhead.

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A Linear Programming with Alliance Strategy Coverage in Wireless Sensor Networks

Abstract: Abstract

Cited by 3 publications

References 17 publications

ENCP: a new Energy-efficient Nonlinear Coverage Control Protocol in mobile sensor networks

ENCP: a new Energy-efficient Nonlinear Coverage Control Protocol in mobile sensor networks

CS‐PLM: Compressive Sensing Data Gathering Algorithm Based on Packet Loss Matching in Sensor Networks

Job Performance Optimization Method Based on Data Balance in the Wireless Sensor Networks

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