Empirical formulation for estimation of optimum number of randomly deployed nodes in WSN

Rai, Nitika; Daruwala, Rohin

doi:10.1109/wispnet.2016.7566159

Cited by 5 publications

(17 citation statements)

References 9 publications

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“…If every point in the network lies within the sensing area of at least one node, full coverage is achieved. Hence, coverage fraction (for coverage degree, k = 1) can be defined as the probability of detecting an event by at least one node, and is mathematically given by [3][4][5]10, 35]…”

Section: Design Aspects In Wsnmentioning

confidence: 99%

“…where, α and γ are sensing device parameters, R max denotes the maximum sensing radius of the node and R min the distance from the centre of the node at which the uncertainty in detection starts. The parameters if judiciously and carefully selected can effectively represent the physical properties of the nodes [3][4][5]10]. Assume that there are N nodes randomly deployed in a network of area, A, The probability that a node is present at a distance y from the centre of the node is 2πydy/ A, where dy is an infinitesimally small increment in y (Fig.…”

Section: Elfes Sensing Model (Esm)mentioning

confidence: 99%

“…However, most of them assume that nodes are deployed in the given area in sufficiently large numbers. Instead, in the initialisation phase, one must compute the optimal node density required such that the nodes collectively meet the temporal and spatial coverage requirement in the given network area, or alternatively can maximise the network coverage within a given economical constraint depending on the specific requirement of the application [1,2,[4][5][6]. The insufficiency of nodes may result in a defunct network, whereas excessive deployment may result in higher system cost and/or increased greater collisions leading to increase in energy consumption and reduced network lifetime [7].…”

Section: Introductionmentioning

confidence: 99%

“…It can be mathematically represented as the fraction of the total physical area covered by the nodes collectively. Alternatively, it can be defined as the event being detected by at least one of the nodes in the network (for critical and sensitive applications -at least k-nodes) [3][4][5][10][11][12][13]. It is also to be noted that the sensing coverage obtained is also a function of the node deployment strategy.…”

Section: Introductionmentioning

confidence: 99%

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Node density optimisation using composite probabilistic sensing model in wireless sensor networks

Rai¹,

Daruwala²

2019

IET wirel. sens. syst.

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Network coverage is a measure of efficiency that signifies the extent to which the deployed nodes collectively cover the network area. It is a fundamental and critical quality of service (QoS) parameter for designing wireless sensor networks (WSNs). Various sensing models are reported which can be used to predict the coverage fraction for a given number of nodes in a predetermined network area. However, each of these reported models consider a subset of parameters. In this study, a novel formulation and hence a new model, composite probabilistic sensing model (CPSM) is proposed which combines the cumulative effects of all the possible factors, thus resulting in a realistic study. Further, the model is revisited to estimate the optimal density of randomly deployed nodes required to attain the desired network area coverage. An exhaustive parametric study is carried out and the results obtained are used to empirically derive a formula based on regression analysis using least square polynomial curve fitting technique. The formulation can be readily and accurately used to design any practical WSN system.

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Section: Design Aspects In Wsnmentioning

confidence: 99%

Section: Elfes Sensing Model (Esm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Node density optimisation using composite probabilistic sensing model in wireless sensor networks

Rai¹,

Daruwala²

2019

IET wirel. sens. syst.

Self Cite

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“…The core mission of WSN is detecting information of interest, one of the common characteristics is the demand for the node number [22][23][24][25], we can respond correctly only number of the nodes are known accurately, it is also a premise of estimating their directions of arrival, positions, powers, and other parameters, actually we can determine this information according to their signals acquiring by some array sensors. In practical applications, there are variety of disturbances, and complex noise, meanwhile, sampling times is always limited by the hardware and harsh environments, then the node number are often calculated inexactly, leading to the deviation of other estimations.…”

Section: Introductionmentioning

confidence: 99%

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Zhen

Zhang

2020

IEEE Access

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Knowing number of nodes is the precondition of their locating and estimation of other parameters in wireless sensor network, actually it can be evaluated according to the signal received by the sensor array. The popular algorithms are only appropriate for the uncorrelated signals and circumstance of Gaussian white noise, the estimation precision will deteriorate at some harsh environments. Hence a new algorithm for determining coherent node number based on multiple feature extraction in wireless sensor network is presented in this paper. First, perform spatial difference smoothing to the array data. Then the eigenvalues and eigenvectors can be acquired by eigen-decomposition, consequently multiple features which are used for training are constructed. Finally, back propagation neural network and particle swarm optimization are exploited for calculating node number. Simulation results demonstrate that the algorithms has good performances under the background of colored noise and small samples.

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Critical density for coverage and connectivity in non-orientation directional sensor network

Kang

Dong

2020

International Journal of Distributed Sensor Networks

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Both coverage and connectivity are important problems in wireless sensor networks. As more and more non-orientation sensors are continuously added into the region of interest, the size of covered component and connected component increases; at some point, the network can achieve an entire coverage and full connectivity after which the network percolates. In this article, we analyze the critical density in non-orientation directional sensor network in which the orientations of the sensors are random and the sensors are deployed according to the Poisson point process. We propose an approach to compute the critical density in such a network. A collaborating path is proposed with the sum of field-of-view angles of two collaborating sensors being π. Then a correlated model of non-orientation directional sensing sectors for percolation is proposed to solve the coverage and connectivity problems together. The numerical simulations confirm that percolation occurs on the estimated critical densities. It is worth mentioning that the theoretical analysis and simulation results give insights into the design of directional sensor network in practice.

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Empirical formulation for estimation of optimum number of randomly deployed nodes in WSN

Cited by 5 publications

References 9 publications

Node density optimisation using composite probabilistic sensing model in wireless sensor networks

Node density optimisation using composite probabilistic sensing model in wireless sensor networks

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Critical density for coverage and connectivity in non-orientation directional sensor network

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