Maximum Lifetime Scheduling for Target Coverage and Data Collection in Wireless Sensor Networks

Lu, Zaixin; Li, Wei Wayne; Pan, Miao

doi:10.1109/tvt.2014.2322356

Cited by 75 publications

(43 citation statements)

References 38 publications

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“…Du et al [14] also focused their attention on NL maximization subject to the military barrier coverage constraints 7 , where the sensors form continuous geographic area barriers with the goal of detecting the crossing of an area by the adversaries. Additionally, Lu et al [100] investigated the sleep-mode scheduling problem in order to maximize the NL by only turning on a specific subset of sensors for monitoring the target spots and for exploiting the transmission of the sensed data over multiple hops, all the way to the base station. As another design alternative, Hu et al [101] employed a genetic algorithm for solving the problem of finding the maximum number of disjoint subsets of sensors for maximizing the NL, 6 A specific sensor field is partitioned into smaller sensor subsets, where each subset may be composed of several sensors that are potentially closer to each other.…”

Section: E Coverage Connectivity and Optimal Deploymentmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Yetgin

Cheung

El‐Hajjar

et al. 2017

IEEE Commun. Surv. Tutorials

522

229

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Abstract-Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.

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Section: E Coverage Connectivity and Optimal Deploymentmentioning

confidence: 99%

“…Lu et al [100] investigated the sleep-mode scheduling problem in order to maximize the NL by only turning on a specific subset of sensors for monitoring the target spots.…”

mentioning

confidence: 99%

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Yetgin

Cheung

El‐Hajjar

et al. 2017

IEEE Commun. Surv. Tutorials

522

229

View full text Add to dashboard Cite

show abstract

“…We mainly deal with the first question here while the second one is left for the next section. The calculation of node redundancy degree based on location information employs the geometry knowledge and offers the accurate coverage relationship between nodes [44][45][46]. However, when the location information is not available, it is hard for the nodes to derive the node redundancy degree.…”

Section: Encp Problem Solutionmentioning

confidence: 99%

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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“…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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Maximum Lifetime Scheduling for Target Coverage and Data Collection in Wireless Sensor Networks

Cited by 75 publications

References 38 publications

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

A Survey of Network Lifetime Maximization Techniques in Wireless 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

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