Weaving a proper net to catch large objects in wireless sensor networks

Olteanu, Alina; Xiao, Yang; Wu, Kui; Du, Xiaojiang

doi:10.1109/twc.2010.04.081098

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…In this regard, the higher-order-logic of the Gaussian random variable, developed in (Qasim et al, 2016), can be very helpful. Also, the current approach for the network lifetime would be very useful to investigate the formalization of the optimal detection probability in (Olteanu et al, 2010). Figure 1 The k-set randomized scheduling for (n = 8) nodes and (k = 2) subsets.…”

Section: Discussionmentioning

confidence: 99%

“…Few studies address the size and the shape of the intrusion object. For example, the work of (Olteanu et al, 2010) analytically considers the impact of the size and the shape of the intrusion object to analyze the optimal detection probability. More recently, (Wang et al, 2017) has proposed a mathematical framework, called E-HIPA, for an accurate target localization in the spatial domain.…”

Section: Related Workmentioning

confidence: 99%

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Formal probabilistic performance verification of randomly-scheduled wireless sensor networks

Elleuch

Hasan

Tahar

et al. 2018

IJCCBS

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Energy efficiency in Wireless Sensor Networks (WSN) is one of the most critical issue regardless of the target application. While scheduling sensors by partitions to preserve energy is a simple and intuitive approach in this context, it is also important to not compromise on the main performance requirements of the considered application. For mission-critical WSN applications, different Quality of Service (QoS) requirements on network performance have to be met. Besides, various assumptions, may effectively impact the sensing performance capabilities of the network. Nevertheless, most analysis techniques focus on the average performance values, and do not consider neither the targeted QoS requirements, nor the probabilistic feature of the algorithm. Based on the theorem proving approach, we first provide, in this paper, an accurate formal analysis of the network lifetime maximization problem, under QoS constraints, for randomlyscheduled wireless sensor networks. After that, we tackle the higher-order-logic formalization of the intrusion coverage intensity, for a modified version of the randomized scheduling, with more realistic assumptions for the intrusion object, in a two or three dimensional plane.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Formal probabilistic performance verification of randomly-scheduled wireless sensor networks

Elleuch

Hasan

Tahar

et al. 2018

IJCCBS

View full text Add to dashboard Cite

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“…However, the models developed in are based on the assumption that each individual is able to interact with only a few other individuals (generally three or four) in the same vicinity. There are other much related works .…”

Section: Related Workmentioning

confidence: 99%

The effects of wireless communication failures on group behavior of mobile sensors

Zheng

Huang

Wang

et al. 2012

Wireless Communications

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In social groups, complex group behavior often emerges from the local interaction among simple individuals. Throughout this study, we assume that individuals can access information by way of wireless communication. In this case, individuals are able to exchange accurate information with each other as long as wireless communication links between them are allowed. Subsequently, we propose a consensus decision-making model for studying the consistency of group behavior considering both the wireless communication range and the probability of successful communication. Our simulation results show the following conclusions: (i) consistency of the group behavior is absolutely achieved, when the wireless communication range is large enough and the probability of successful communication p D 1; (ii) when the wireless communication range is large enough, the consistency of the group behavior is still achieved as long as p is bigger than some small constant; and (iii) the law in (iii) remains applicable when the number of individuals in the group changes. Therefore, one may infer that consistency of group behavior in mobile sensors is much more related to the extent of distribution of obtainable information than the amount of information, where the extent of distribution of obtainable information means that each individual can obtain information from wider area or from those individuals who are not only in its local area.

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“…For example, the sensors are assumed to be deployed randomly in the battlefield environment in order to detect the force distribution of opponent armies. Over the past few years, the deployment problem has been paid considerable research attention .…”

Section: Introductionmentioning

confidence: 99%

Deterministic deployment based on information coverage in wireless sensor networks

Zheng

Huang

Wang

et al. 2012

Wireless Communications

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In this study, a deterministic deployment problem in wireless sensor networks is examined. On the basis of information coverage, we study equilateral triangle and square deployment strategies, and we provide the maximum distance between sensors in order to reach the required detection probability for any point in the monitoring field. First, we provide a model of the signal attenuation. On the basis of the detected signal from the K sensors, the best linear and unbiased estimation is used to estimate the signal parameter with the corresponding error. For the equilateral triangle deployment, the maximum distance between sensors is computed and provided when the received signal data from two or three sensors is used. Similarly, we have computed and supplied the maximum distance between sensors in the square deployment. Simulations are performed to show the relationship between the number of sensors and the detection probability. The simulation results show that it is not a good choice to improve the detection probability with a larger number of sensors.

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Weaving a proper net to catch large objects in wireless sensor networks

Cited by 9 publications

References 17 publications

Formal probabilistic performance verification of randomly-scheduled wireless sensor networks

Formal probabilistic performance verification of randomly-scheduled wireless sensor networks

The effects of wireless communication failures on group behavior of mobile sensors

Deterministic deployment based on information coverage in wireless sensor networks

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