Critical Sensor Density Research Articles

Unraveling Impact of Critical Sensor Density on Occlusion Coverage of Partial Targets for Directional Sensor Networks

Unraveling Impact of Critical Sensor Density on Occlusion Coverage of Partial Targets for Directional Sensor Networks

Network Coverage in Interference Limited Wireless Sensor Networks

The performance of a wireless sensor network is limited by the available bandwidth of the shared wireless channel. For the need to share the available bandwidth sensors contend for concurrent transmissions. The simultaneous transmissions by the neighbouring nodes are the main source of interference at the intended receiver sensor. In this paper, we derive the successful decoding probability of receiver sensor in presence of interference and Rayleigh fading channel. We introduce the concept of circular influence zone, to impose a restriction on the aggregated interference from the distant neighbours at an intended receiver sensor. We provide an insight about the critical sensor density which yields an optimal trade-off between the achievable coverage and receiver decoding probability in presence of co-channel interference.

Experimental investigation of unbound nodes identification for metallic sandwich panels with truss core

Experimental investigation of metallic pyramidal sandwich panels with truss core in the cases of different extent and location of unbound nodes damages are conducted and a baseline-free damage identification method based on flexible matrix, gapped smoothing method and Teager energy operator is used. The influences of sensor density and Teager energy operator on the damage identification are also discussed. Moreover, a 0–1 unbound nodes identification method is also proposed to capture the shape and size of the unbound nodes damage zone. The results show that the proposed baseline-free method could identify the unbound nodes damage of metallic sandwich panels with truss core effectively. Increasing sensor density is beneficial for damage identification, and there is a critical sensor density for the identification of unbound nodes damage. Teager energy operator plays a very important role in suppressing fluctuations and singularities caused by non-damage factors, such as boundary condition, noise and shaker. The identified damage zone obtained by the 0–1 method is in accordance with the actual damage zone.

Sensor Density for Confident Information Coverage in Randomly Deployed Sensor Networks

Coverage is one of the fundamental issues in wireless sensor networks, yet most of the current studies on coverage are based on the simplest disk coverage model. Based on the theory of field reconstruction, we proposed a novel coverage model called confident information coverage in our previous study. In this paper, based on the confident information coverage model, we study the critical sensor density to achieve complete coverage in randomly deployed sensor networks. We first use the average vacancy to measure the degree of coverage, and compute the average vacancy through the computation of the probability that an arbitrary point is not covered by randomly deployed sensors within its correlation range. We then propose a numerical computation method called discrete approximation algorithm to compute this probability, and prove that this probability is actually the limit of the output of the proposed algorithm. Furthermore, we derive the upper and lower bound for the average vacancy as a function of sensor density, which provides a useful insight for the critical sensor density to achieve complete coverage. The simulation results validate our theoretical analysis.

Critical Sensor Density for Partial Coverage under Border Effects in Wireless Sensor Networks

Coverage is an important metric to measure the quality of service of a wireless sensor network monitoring a field of interest (FoI). From an energy perspective, it is often very important to maintain the desired coverage ratio with a minimum number of sensors. The literature on determining the critical sensor density (CSD) for the desired coverage ratio assumes that the FoI is unbounded or toroidal in shape. Although it is not a realistic assumption, it eliminates the border effects in analysis. Since the entire sensing area of the sensors near the boundary may not be useful for the coverage, the CSD estimated without the border effects is lower than the actual value. In this paper, we assume that the sensors are deployed uniformly at random in a convex polygon-shaped FoI and consider the border effects to derive the expected sensing area of a sensor used in the coverage. Next, we estimate the CSD required for the desired coverage ratio. We validate the analysis and demonstrate the impact of border effects on CSD using numerical results. Results show that our approach estimates the CSD better than another one that does not consider the exact geometry of the FoI.

Critical sensor density for partial connectivity in large area wireless sensor networks

In this article, we study the critical sensor density for partial connectivity of a large area sensor network. We assume that sensor deployment follows the Poisson distribution. For a given partial connectivity requirement ρ, 0.5 < ρ < 1, we prove that there exists a critical sensor density λ 0 , around which the probability that at least a fraction ρ of sensors are connected in the network increases sharply from ε to 1-ε within a short interval of sensor density λ. The length of this interval is in the order of O (-log ε/log A ) as A → ∞, where A is the area of the sensor field, and the location of λ 0 is at the sensor density where the aforesaid probability is about 1/2. We prove the preceding theoretical results in the hexagonal model. We also extend our results to the disk model that models transmission range of sensors as disks. Simulations are performed to confirm the analytical results.

On the Path Coverage Properties of Random Sensor Networks

In a sensor network, the points in the operational area that are suitably sensed are a two-dimensional spatial coverage process. For randomly deployed sensor networks, typically, the network coverage of two-dimensional areas is analyzed. However, in many sensor network applications, e.g., tracking of moving objects, the sensing process on paths, rather than in areas, is of interest. With such an application in mind, we analyze the coverage process induced on a one-dimensional path by a sensor network that is modeled as a two-dimensional Boolean model. In the analysis, the sensor locations form a spatial Poisson process of density lambda and the sensing regions are circles of i.i.d. random radii. We first obtain a strong law for the fraction of a path that is k-sensed, i.e., sensed by (ges k) sensors. Asymptotic path-sensing results are obtained under the same limiting regimes as those required for asymptotic coverage by a two-dimensional Boolean model. Interestingly, the asymptotic fraction of the area that is 1-sensed is the same as the fraction of a path that is 1-sensed. For k = 1, we also obtain a central limit theorem that shows that the asymptotics converge at the rate of Theta(lambda1/2) for k = 1. For finite networks, the expectation and variance of the fraction of the path that is k-sensed is obtained. The asymptotics and the finite network results are then used to obtain the critical sensor density to k-sense a fraction alphak of an arbitrary path with very high probability is also obtained. Through simulations, we then analyze the robustness of the model when the sensor deployment is nonhomogeneous and when the are not rectilinear. Other path coverage measures like breach, support, length to first sense, and sensing continuity measures like holes and clumps are also characterized. Finally, we discuss some generalizations of the results like characterization of the coverage process of m-dimensional straight line paths by n-dimensional, n > m, sensor networks