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Wireless Sensor Networks (WSNs) designed for mission-critical applications suffer from limited sensing capacities, particularly fast energy depletion. Regarding this, mobile sinks can be used to balance the energy consumption in WSNs, but the frequent location updates of the mobile sinks can lead to data collisions and rapid energy consumption for some specific sensors. This paper explores an optimal barrier coverage based sensor deployment for event driven WSNs where a dual-sink model was designed to evaluate the energy performance of not only static sensors, but Static Sink (SS) and Mobile Sinks (MSs) simultaneously, based on parameters such as sensor transmission range r and the velocity of the mobile sink v, etc. Moreover, a MS mobility model was developed to enable SS and MSs to effectively collaborate, while achieving spatiotemporal energy performance efficiency by using the knowledge of the cumulative density function (cdf), Poisson process and M/G/1 queue. The simulation results verified that the improved energy performance of the whole network was demonstrated clearly and our eDSA algorithm is more efficient than the static-sink model, reducing energy consumption approximately in half. Moreover, we demonstrate that our results are robust to realistic sensing models and also validate the correctness of our results through extensive simulations.

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RESUMEN

This paper proposes a high precision Gaussian Mixture Model-based novel Boundary Detection 3D (BD3D) scheme with reasonable implementation cost for 3D cases by selecting a minimum number of Boundary sensor Nodes (BNs) in continuous moving objects. It shows apparent advantages in that two classes of boundary and non-boundary sensor nodes can be efficiently classified using the model selection techniques for finite mixture models; furthermore, the set of sensor readings within each sensor node's spatial neighbors is formulated using a Gaussian Mixture Model; different from DECOMO [1] and COBOM [2], we also formatted a BN Array with an additional own sensor reading to benefit selecting Event BNs (EBNs) and non-EBNs from the observations of BNs. In particular, we propose a Thick Section Model (TSM) to solve the problem of transition between 2D and 3D. It is verified by simulations that the BD3D 2D model outperforms DECOMO and COBOM in terms of average residual energy and the number of BNs selected, while the BD3D 3D model demonstrates sound performance even for sensor networks with low densities especially when the value of the sensor transmission range (r) is larger than the value of Section Thickness (d) in TSM. We have also rigorously proved its correctness for continuous geometric domains and full robustness for sensor networks over 3D terrains.

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In this paper, we present an experimental study on transmission of RF signals over turbulent free-space optics (FSO) channel by using off-the-shelf Radio Frequency - FSO (RF-FSO) antennas. The results demonstrate potential of utilizing FSO links for transmission of RF signals and are used as a guideline in the design, prediction and evaluation of an advanced Dense Wavelength Division Multiplexing (DWDM) RoFSO system we are developing capable of transmitting multiple RF signals. An analytical modeling of the system is also conducted to identify key parameters in evaluating the performance of RF signal transmission using FSO links. The results confirm that the effect of scintillation on RF-FSO system performance can be estimated by using a simple estimation equation and satisfactory result are obtained from comparing the experimental and theoretical derived data under weak to strong turbulence condition.

RESUMEN

The deterioration and deformation of a free-space optical beam wave-front as it propagates through the atmosphere can reduce the link availability and may introduce burst errors thus degrading the performance of the system. We investigate the suitability of utilizing soft-computing (SC) based tools for improving performance of free-space optical (FSO) communications systems. The SC based tools are used for the prediction of key parameters of a FSO communications system. Measured data collected from an experimental FSO communication system is used as training and testing data for a proposed multi-layer neural network predictor (MNNP) used to predict future parameter values. The predicted parameters are essential for reducing transmission errors by improving the antenna's accuracy of tracking data beams. This is particularly essential for periods considered to be of strong atmospheric turbulence. The parameter values predicted using the proposed tool show acceptable conformity with original measurements.