Localized geometric topologies with bounded node degree for three-dimensional wireless sensor networks

This paper investigates the connectivity properties of three dimensional wireless sensor network in which N nodes are independently and uniformly (i.u.d.) distributed either on the surface of a sphere of radius R or inside the volume of a ball of radius R. Our approach utilizes the geometrical probability results for the conditional probability that a random node falls inside a ball centered at an arbitrary sensor node. We obtain exact expressions for the mean node degree and node isolation probability for the two topologies, which can be easily evaluated analytically or numerically. We also illustrate an upper bound for the probability of connectivity. The analysis is validated by comparison with existing results and Monte Carlo simulations.

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“…The optimal deployment of nodes (i.e. topology control) [10,11] and routing [12] in three dimensions have also been investigated.…”

Section: Introductionmentioning

confidence: 99%

Connectivity of three dimensional wireless sensor networks using geometrical probability

Khalid

Durrani

2013

2013 Australian Communications Theory Workshop (AusCTW)

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“…Wireless sensor networks (WSNs) have wide applications in the military, health, and environmental fields [ 1 , 2 , 3 ]. Because node positions support information for many WSN applications, sensor localization has become a hot research issue.…”

Section: Introductionmentioning

confidence: 99%

Mobile Location in Wireless Sensor Networks Based on Multi Spot Measurements Model

Chen¹,

Huang

et al. 2022

Sensors

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The localization of sensors in wireless sensor networks has recently gained considerable attention. The existing location methods are based on a one-spot measurement model. It is difficult to further improve the positioning accuracy of existing location methods based on single-spot measurements. This paper proposes two location methods based on multi-spot measurements to reduce location errors. Because the multi-spot measurements model has more measurement equations than the single-spot measurements model, the proposed methods provide better performance than the traditional location methods using one-spot measurement in terms of the root mean square error (RMSE) and Cramer–Rao lower bound (CRLB). Both closed-form and iterative algorithms are proposed in this paper. The former performs suboptimally with less computational burden, whereas the latter has the highest positioning accuracy in attaining the CRLB. Moreover, a novel CRLB for the proposed multi-spot measurements model is also derived in this paper. A theoretical proof shows that the traditional CRLB in the case of single-spot measurements performs worse than the proposed CRLB in the case of multi-spot measurements. The simulation results show that the proposed methods have a lower RMSE than the traditional location methods.

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“…Numerical results showed that in terms of convergence, this rule is not optimal. In addition, the node degree is not upper-bounded in the AAT-based topology control rule, making the protocol be less feasible in the application in a FANET [22].…”

Section: Introductionmentioning

confidence: 99%

Leader-following flocking for unmanned aerial vehicle swarm with distributed topology control

Liu

Wang

Zeng

et al. 2020

Sci. China Inf. Sci.

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To address the flocking issues of an unmanned aerial vehicle (UAV) swarm operating at a leaderfollower mode, distributed control protocols comprising both kinetic controller and topology control algorithm must be implemented. For flocking the UAV swarm, a distributed control-input method is required for both maintaining a relatively steady state between neighboring vehicles (including velocity matching and distance maintenance) and avoiding vehicle-to-vehicle collision. Furthermore, the stability of control protocols should be analyzed using the potential energy function. In particular, a distributed β-angle test (BAT) rule in the proposed topology-control issue may allow each UAV to determine its neighboring set by exploiting the locally sensed information, thereby significantly reducing the communication overhead of the entire swarm. In addition, node-degree bound is derived to demonstrate the feasibility of the proposed algorithm, in which the optimal value in terms of convergence is analyzed. The flocking of the flying ad-hoc network (FANET) can be achieved in a self-organizing way without the use of an external control center via the distributed control protocols. Ultimately, the proposed analysis is verified by numerical results.

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Localized geometric topologies with bounded node degree for three-dimensional wireless sensor networks

Cited by 8 publications

References 56 publications

Connectivity of three dimensional wireless sensor networks using geometrical probability

Connectivity of three dimensional wireless sensor networks using geometrical probability

Mobile Location in Wireless Sensor Networks Based on Multi Spot Measurements Model

Leader-following flocking for unmanned aerial vehicle swarm with distributed topology control

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