sensing radius
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2021 ◽  
Vol 17 (4) ◽  
pp. 1-48
Author(s):  
Sajal K. Das ◽  
Rafał Kapelko

This article deals with reliable and unreliable mobile sensors having identical sensing radius r , communication radius R , provided that r ≤ R and initially randomly deployed on the plane by dropping them from an aircraft according to general random process. The sensors have to move from their initial random positions to the final destinations to provide greedy path k 1 -coverage simultaneously with k 2 -connectivity. In particular, we are interested in assigning the sensing radius r and communication radius R to minimize the time required and the energy consumption of transportation cost for sensors to provide the desired k 1 -coverage with k 2 -connectivity. We prove that for both of these optimization problems, the optimal solution is to assign the sensing radius equal to r = k 1 || E [S]||/2 and the communication radius R = k 2 || E [S]||/2, where || E [S]|| is the characteristic of general random process according to which the sensors are deployed. When r < k 1 || E [S]||/2 or R < k 2 || E [S]||/ 2, and sensors are reliable, we discover and explain the sharp increase in the time required and the energy consumption in transportation cost to ensure the desired k 1 -coverage with k 2 -connectivity.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7576
Author(s):  
Rongxin Tang ◽  
Yuhao Tao ◽  
Jiahao Li ◽  
Zhiming Hu ◽  
Kai Yuan ◽  
...  

With the rapid progress of hardware and software, a wireless sensor network has been widely used in many applications in various fields. However, most discussions for the WSN node deployment mainly concentrated on the two-dimensional plane. In such a case, some large scale applications, such as information detection in deep space or deep sea, will require a good three dimensional (3D) sensor deployment scenario and also attract most scientists’ interests. Excellent deployment algorithms enable sensors to be quickly deployed in designated areas with the help of unmanned aerial vehicles (UAVs). In this paper, for the first time, we present a three dimensional network deployment algorithm inspired by physical dusty plasma crystallization theory in large-scale WSN applications. Four kinds of performance evaluation methods in 3D space, such as the moving distance, the spatial distribution diversion, system coverage rate, and the system utilization are introduced and have been carefully tested.Furthermore, in order to improve the performance of the final deployment, we integrated the system coverage rate and the system utilization to analyze the parameter effects of the Debye length and the node sensing radius. This criterion attempts to find the optimal sensing radius with a fixed Debye length to maximize the sensing range of the sensor network while reducing the system redundancy. The results suggest that our 3D algorithm can quickly complete an overall 3D network deployment and then dynamically adjust parameters to achieve a better distribution. In practical applications, engineers may choose appropriate parameters based on the sensor’s hardware capabilities to achieve a better 3D sensor network deployment. It may be significantly used in some large-scale 3D WSN applications in the near future.


2021 ◽  
Author(s):  
Jie Feng ◽  
Fangjiong Chen ◽  
Hongbin Cheng

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.


2021 ◽  
Author(s):  
Jie Feng ◽  
Fangjiong Chen ◽  
Hongbin Cheng

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.


Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 867
Author(s):  
Arnab Barua ◽  
Alireza Beygi ◽  
Haralampos Hatzikirou

The way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility), but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic.


2021 ◽  
Author(s):  
Arnab Barua ◽  
Alireza Beygi ◽  
Haralampos Hatzikirou

AbstractThe way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility) but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic.


Author(s):  
Nadia Loy ◽  
Luigi Preziosi

Abstract The aim of this article is to study the stability of a non-local kinetic model proposed by Loy & Preziosi (2020a) in which the cell speed is affected by the cell population density non-locally measured and weighted according to a sensing kernel in the direction of polarization and motion. We perform the analysis in a $d$-dimensional setting. We study the dispersion relation in the one-dimensional case and we show that the stability depends on two dimensionless parameters: the first one represents the stiffness of the system related to the cell turning rate, to the mean speed at equilibrium and to the sensing radius, while the second one relates to the derivative of the mean speed with respect to the density evaluated at the equilibrium. It is proved that for Dirac delta sensing kernels centered at a finite distance, corresponding to sensing limited to a given distance from the cell center, the homogeneous configuration is linearly unstable to short waves. On the other hand, for a uniform sensing kernel, corresponding to uniformly weighting the information collected up to a given distance, the most unstable wavelength is identified and consistently matches the numerical solution of the kinetic equation.


2020 ◽  
Vol 68 (9) ◽  
pp. 6704-6716
Author(s):  
Alessandra La Gioia ◽  
Adam Santorelli ◽  
Martin O'Halloran ◽  
Emily Porter
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