Strengthening barrier-coverage of static sensor network with mobile sensor nodes

Xu, Biaofei; Zhu, Yuqing; Kim, Dong-Hyun; Li, Deying; Jiang, Huaipan; Tokuta, Alade O.

doi:10.1007/s11276-015-0946-8

“…For simplicity, the k-barrier coverage of MSNs based on elastic collision and virtual potential field proposed in this paper is called "VMSN" for short, the kbarrier coverage in stationary WSNs is called "WSN" for short, and k-barrier coverage of general MSNs based on gas kinetic theory proposed in [11] is called "gMSN" for short, and the scheme based on classic virtual potential field in [9] and that based on improved virtual potential field in [28] are abbreviated as "CVF" and "VirFID" respectively. The approach based on Grey forecasting model in [29] is abbreviated as "GM". In this paper, "VMSN" is realized by two ways: theoretical evaluation combined with simulations and complete Monte Carlo simulation, denoted as "VMSN(TE)" and "VMSN(MC)", respectively.…”

Section: Evaluation Methods and Settingsmentioning

confidence: 99%

“…Currently, mobile WSNs have gradually become the concern of barrier coverage, which are investigated in [10], [11] and [22]- [29]. Where [23]- [25] and [11] illustrate that node mobility may improve the coverage performance of WSNs.…”

Section: Related Workmentioning

confidence: 99%

“…B. Wang et al mainly study how to efficiently use the reposition of mobile sensors to achieve k-barrier coverage in directional sensor networks [27]. B. Xu et al investigates the potential of using mobile sensor nodes to strengthen the barrier coverage of WSN by adopting the first-order grey model to determine the vulnerable part of the barrier and relocate mobile nodes to cover the possible loopholes [29]. Particularly, for the bistatic radar (BR) sensor networks, [30] and [31] respectively consider the placement and deployment problem in order to maximize the coverage and minimize the vulnerability of a barrier.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Intrusion Detection Based on <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-Coverage in Mobile Sensor Networks With Empowered Intruders

Huang

¹

,

Gong

²

,

Zhang

³

et al. 2018

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Intrusion detection is one of the important applications of Wireless Sensor Networks (WSNs). Prior research indicated that the barrier coverage method combined with Mobile Sensor Networks (MSNs) can enhance the effectiveness of intrusion detection by mitigating coverage holes commonly appeared in stationary WSNs. However, the trajectories of moving sensors and moving intruders have not been investigated thoroughly, where the impact between two adjacent moving sensors and between a moving sensor and a moving intruder are still underdetermined. In order to address these open problems, in this paper, we firstly discuss the virtual potential field between sensors as well as between sensors and intruders. We then propose to formulate the mobility pattern of sensor node using elastic collision model and that of intruder using point charge model. The point charge model describes an hitherto-unexplored mobility pattern of empowered-intruders, which are capable of acting upon the virtual repulsive forces from sensors in order to hide them away from being detected. With the aid of the two models developed, analytical expressions and simulation results demonstrate that our proposed design achieves a higher k-barrier coverage probability in intrusion detection when compared to that of the conventional designs. It is also worth mentioning that these improvements are achieved with shorter average displacement distance and under the much more challenging MSNs settings. Index Terms-Mobile sensor networks, intrusion detection, k-barrier coverage, empowered intruders I. INTRODUCTION 1.1 Motivation Intrusion detection can be defined as the technique to detect unapproved entrance into specific territory and it has

show abstract

“…The earliest research on the coverage of moving targets started with a study on barrier coverage [7,8]. With the in-depth application of sensor networks, users' coverage monitoring of moving targets proposed new requirements for target location accuracy, node energy consumption efficiency, and real-time monitoring capability.…”

Section: State Of the Artmentioning

confidence: 99%

A Novel Threshold - Controllable Algorithm for Moving Target Coverage

Zhao¹,

Heng-chuan²,

Sun³

et al. 2018

JESTR

View full text Add to dashboard Cite

The uncertainty of a moving target's speed and direction can lead to inaccurate target location and large energy consumption of network nodes when the target is covered and controlled by the sensor node in the wireless sensor network. This study proposed a method of threshold-controllable moving target coverage to accurately predict the target movement location, improve the energy consumption rate of the network, and clarify the main factors affecting the coverage of the moving target. First, a node participation threshold was introduced in the area of the moving target, and a scheduling mechanism of the node state was established. Second, the location prediction model of the moving target and the self-adjusting mechanism of the node data reporting frequency were established in accordance with the historical location information, movement direction, and speed of the moving target. Finally, the effect of node participation threshold on target location accuracy and network energy consumption during monitoring of the moving target was analyzed. Results show that when the participation threshold is set to decrease from 5 to 2, the target location error at different speeds decreases by 56.7%. When the participation threshold is equal to 3, the average energy consumption at different speeds decreases by 55% compared with consumption without a present threshold. Energy consumption decreases by an average of 41% in the model of moving target location prediction. Therefore, the threshold-controllable algorithm for moving target coverage shows excellent performance in terms of location accuracy and network energy utilization efficiency. The study can be utilized for sensor network environments with limited node energy to provide theoretical guidance for the dynamic coverage optimization of moving targets.

show abstract

“…In the past several years, moving target tracking in wireless sensor networks has been closely investigated. research on the coverage of moving targets started with a study on barrier coverage [7,8]. With the in-depth application of sensor networks, users' coverage monitoring of moving targets proposed new requirements for target location accuracy, node energy consumption efficiency, and real-time monitoring capability.…”

Section: State Of the Artmentioning

confidence: 99%

Untitled

2018

JESTR

1

0

View full text Add to dashboard Cite

The uncertainty of a moving target's speed and direction can lead to inaccurate target location and large energy consumption of network nodes when the target is covered and controlled by the sensor node in the wireless sensor network. This study proposed a method of threshold-controllable moving target coverage to accurately predict the target movement location, improve the energy consumption rate of the network, and clarify the main factors affecting the coverage of the moving target. First, a node participation threshold was introduced in the area of the moving target, and a scheduling mechanism of the node state was established. Second, the location prediction model of the moving target and the self-adjusting mechanism of the node data reporting frequency were established in accordance with the historical location information, movement direction, and speed of the moving target. Finally, the effect of node participation threshold on target location accuracy and network energy consumption during monitoring of the moving target was analyzed. Results show that when the participation threshold is set to decrease from 5 to 2, the target location error at different speeds decreases by 56.7%. When the participation threshold is equal to 3, the average energy consumption at different speeds decreases by 55% compared with consumption without a present threshold. Energy consumption decreases by an average of 41% in the model of moving target location prediction. Therefore, the threshold-controllable algorithm for moving target coverage shows excellent performance in terms of location accuracy and network energy utilization efficiency. The study can be utilized for sensor network environments with limited node energy to provide theoretical guidance for the dynamic coverage optimization of moving targets.

show abstract

Strengthening barrier-coverage of static sensor network with mobile sensor nodes

Cited by 46 publications

References 24 publications

Intrusion Detection Based on <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-Coverage in Mobile Sensor Networks With Empowered Intruders

Intrusion Detection Based on <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-Coverage in Mobile Sensor Networks With Empowered Intruders

A Novel Threshold - Controllable Algorithm for Moving Target Coverage

Untitled

Contact Info

Product

Resources

About