On a Voronoi aggregative process related to a bivariate Poisson process

Foss, Sergey; Zuyev, Sergei

doi:10.1017/s0001867800027506

Cited by 61 publications

(96 citation statements)

References 2 publications

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“…In this section, we verify the optimal p opt deduced in Section III does minimize the energy costs in the system via simulations. Without loss of generality, we simulated two clustering algorithms, LEACH [11] and HEED [12], on networks of nodes distributed uniformly in square areas. LEACH and HEED are two typical clustering algorithms employed in single-hop routing and multi-hop routing respectively.…”

Section: ) Multi-hop Communicationmentioning

confidence: 99%

Optimal Cluster Number Determination for Clustered Wireless Sensor Networks

Li¹,

Shen²

2008

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

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A method to determine the optimal clusterhead number in a clustered wireless sensor network is proposed to improve the energy efficiency of clustering schemes. The wireless sensor network is modeled as points locating randomly on the plane according to a homogeneous spatial Poisson process. This leads to derive the means of some typical cluster parameters accordingly. After that, the total energy cost consumed in the system is calculated based on a practical radio model. As a result, the optimal probability of becoming a clusterhead for each node is deduced via minimizing the system energy cost. Ultimately, the total energy costs are compared in the scenarios that the networks employ the same clustering algorithm with a range of values for the probability of becoming a clusterhead. Simulation results show that the optimal probability we obtained has indeed minimized energy dissipation in the system and is applicable to other clustering algorithms directly.

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Section: ) Multi-hop Communicationmentioning

confidence: 99%

Optimal Cluster Number Determination for Clustered Wireless Sensor Networks

Li¹,

Shen²

2008

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

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“…Since we use the data collected by the nearest sensor node to estimate the data at points around it, the field can be divided into Voronoi cells [7] with one representative node in each cell. Data at any point in the cell is estimated using the data collected by the representative node in the cell.…”

Section: Distortion Analysismentioning

confidence: 99%

Distortion Analysis for Real-Time Data Collection of Correlated Fields in Randomly Distributed Sensor Networks

Zhang

Wang

Ansari

et al. 2008

2008 IEEE International Conference on Communications

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In this paper we consider the task of real-time reconstruction of data fields sensed by a network of smart sensors assuming that data is temporally and spatially correlated. We present an analysis framework to compute average distortion using realistic transmission schemes and network parameters. The existing work related to the real time data reconstruction problem has assumed regular grid deployments. In this paper we assume that sensor nodes are randomly deployed in the field thus making the problem significantly more realistic and challenging. We handle the randomness by dividing the field into n segments and randomly select k active nodes within each segment. This approach also facilitates the design of an efficient collision-free data transmission scheme to transmit data to the sink. We verify our analytical result by simulating data collection in fields that have different correlation coefficients. The analysis framework presented can be extended to different transmission schemes, node distributions and reconstruction methods.

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“…Since each type 0 node chooses the nearest type 1 node as its cluster head, the data field is actually divided into Voronoi cells [10] with each cell serving as a cluster. Since nodes are randomly deployed in the field following uniform distribution, the number of type 0 and type 1 nodes within a certain area can be viewed as following Poisson process with density λ 0 = n0 πA 2 and λ 1 = n1 πA 2 , respectively.…”

Section: Theoretical Analysismentioning

confidence: 99%

“…Since nodes are randomly deployed in the field following uniform distribution, the number of type 0 and type 1 nodes within a certain area can be viewed as following Poisson process with density λ 0 = n0 πA 2 and λ 1 = n1 πA 2 , respectively. With the help of Voronoi cell analysis in [10], we can determine the expected value of this distance as:…”

Section: Theoretical Analysismentioning

confidence: 99%

Distortion Analysis for Real-Time Reconstruction of Correlated Data Field in Heterogeneous Sensor Networks

Zhang

Wang

Khokhar

2008

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

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This paper deals with the analysis of distortion introduced in the real time reconstruction of a spatio-temporally correlated field by a sink node. The field is measured by a randomly deployed network of heterogeneous sensors and the data is sent to the sink node. For the sake of analysis, we assume that the network consists of multiple clusters consisting of two types of nodes: cluster head and cluster member nodes. Given the initial energy capacities for both types of nodes, we determine the number of nodes needed for each type such that balanced energy consumption is achieved. Given the number of nodes of each type available in the network, we set up a mathematical model and analyze the average distortion in the reconstructed field. We study the relationship of this distortion with varying number of nodes, and determine the minimum number of nodes of each type needed to realize the reconstruction within a given distortion constraint. The work presented in this paper can be extended to consider different clustering methods, intra-cluster transmission schemes, and signal models.

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On a Voronoi aggregative process related to a bivariate Poisson process

Cited by 61 publications

References 2 publications

Optimal Cluster Number Determination for Clustered Wireless Sensor Networks

Optimal Cluster Number Determination for Clustered Wireless Sensor Networks

Distortion Analysis for Real-Time Data Collection of Correlated Fields in Randomly Distributed Sensor Networks

Distortion Analysis for Real-Time Reconstruction of Correlated Data Field in Heterogeneous Sensor Networks

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