An inhomogeneous spatial node distribution and its stochastic properties

Bettstetter, Christian; Gyarmati, M.; Schilcher, Udo

doi:10.1145/1298126.1298195

Cited by 36 publications

(41 citation statements)

References 12 publications

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“…In the space domain, on the other hand, while there are many papers concentrating on the modeling of base station locations using stochastic geometry [2], [15], there are only few works in the literature which take into account the heterogeneous spatial distribution of traffic demand in wireless cellular networks [16]- [22]. To the best of the authors' knowledge, none of the existing works provides a statistically adjustable model representing a variety of possible scenarios for UE distribution.…”

Section: B Related Workmentioning

confidence: 99%

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HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks

Mirahsan

Schoenen

Yanıkömeroğlu

2015

IEEE J. Select. Areas Commun.

116

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Abstract-A recent approach in modeling and analysis of the supply and demand in heterogeneous wireless cellular networks has been the use of two independent Poisson point processes (PPPs) for the locations of base stations (BSs) and user equipments (UEs). This popular approach has two major shortcomings. First, although the PPP model may be a fitting one for the BS locations, it is less adequate for the UE locations mainly due to the fact that the model is not adjustable (tunable) to represent the severity of the heterogeneity (non-uniformity) in the UE locations. Besides, the independence assumption between the two PPPs does not capture the often-observed correlation between the UE and BS locations.This paper presents a novel heterogeneous spatial traffic modeling which allows statistical adjustment. Simple and nonparameterized, yet sufficiently accurate, measures for capturing the traffic characteristics in space are introduced. Only two statistical parameters related to the UE distribution, namely, the coefficient of variation (the normalized second-moment), of an appropriately defined inter-UE distance measure, and correlation coefficient (the normalized cross-moment) between UE and BS locations, are adjusted to control the degree of heterogeneity and the bias towards the BS locations, respectively. This model is used in heterogeneous wireless cellular networks (HetNets) to demonstrate the impact of heterogeneous and BS-correlated traffic on the network performance. This network is called HetHetNet since it has two types of heterogeneity: heterogeneity in the infrastructure (supply), and heterogeneity in the spatial traffic distribution (demand).

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Section: B Related Workmentioning

confidence: 99%

“…In [16], Bettstetter et al, presented an algorithm to create a random inhomogeneous node distribution based on a neighborhood-dependent thinning approach in a homogeneous PPP. The model, however, can not be used for generating BScorrelated UE patterns, as this is beyond the scope of that model.…”

Section: B Related Workmentioning

confidence: 99%

HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks

Mirahsan

Schoenen

Yanıkömeroğlu

2015

IEEE J. Select. Areas Commun.

116

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“…Figure 22 shows the impact of varying camera density when targets are deployed in a clustered manner. To generate clustered distribution of targets, we use the existing algorithm to generate inhomogeneous node distributions [Bettstetter et al 2007]. It can be observed that the coverage achieved by Hierarchical is always greater than that of DFA, and it closely tracks the Optimal, which shows that Hierarchical performs better even for the clustered distribution of targets.…”

Section: Impact Of Camera and Target Placementmentioning

confidence: 99%

Coverage algorithms for visual sensor networks

Munishwar

Abu-Ghazaleh

2013

ACM Trans. Sen. Netw.

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Visual sensor networks are becoming increasingly popular in a number of application domains. A distinguishing characteristic of VSNs is to self-configure to minimize the need for operator control and improve scalability. One of the areas of self-configuration is camera coverage control: how should cameras adjust their field-of-views to cover maximum targets? This is an NP-hard problem. We show that the existing heuristics have a number of weaknesses that influence both their coverage and their overhead. Therefore, we first propose a computationally efficient centralized heuristic that provides near-optimal coverage for small-scale networks. However, it requires significant communication and computation overhead, making it unsuitable for large scale networks. Thus, we develop a distributed algorithm that outperforms the existing distributed algorithm with lower communication overhead, at the cost of coverage accuracy. We show that the proposed heuristics guarantee to cover at least half of the targets covered by the optimal solution. Finally, to gain benefits of both centralized and distributed algorithms, we propose a hierarchical algorithm where cameras are decomposed into neighborhoods that coordinate their coverage using an elected local coordinator. We observe that the hierarchical algorithm provides scalable near-optimal coverage with networking cost significantly less than that of centralized and distributed solutions.

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“…Bettstetter et al [12] place nodes in accordance with the uniform process and then apply thinning to it. Onat and Stojmenovic [13] developed several algorithms that create connected topologies with high probability and allow user to choose the average node degree.…”

Section: Node Placement Modelsmentioning

confidence: 99%

“…In order to provide good fit between the target degree distribution and the degree distribution of the end-result topology, before a node is actually added to the topology in an iteration, several candidate nodes are evaluated (lines [6][7][8][9][10][11][12][13][14]. The number of evaluated candidates per iteration is defined by the algorithm parameter retry.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Generating Realistic Node Mobility and Placement for Wireless Multi-Hop Network Simulation

Milic

Malek

2012

IEICE Trans. Commun.

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SUMMARYThere exists a considerable number of node placement models and algorithms for simulation of wireless multihop networks. However, the topologies created with the existing algorithms do not have properties of real networks. We have developed NPART (Node Placement Algorithm for Realistic Topologies) in order to resolve this fundamental issue in simulation methodology. We compare topologies generated by NPART with open wireless multihop network in Berlin. The NPART generated topologies have almost identical node degree distribution, number of cutedges and vertices as the real network. Unlike them, topologies generated with the common node placement models have their own characteristics which are considerably different both from NPART and from reality. NPART algorithm has been developed into a tool. We propose a method and present a tool for integration of NPART with various realistic node mobility algorithms and tools, such as Citymob [1] and MOVE [2]. This integrated tool allows easy and time-efficient generation of highly complex, realistic simulation scenarios. We use the tool to evaluate effects of integration between existing open community wireless multi-hop networks and vehicular ad-hoc networks (VANETs). The evaluation shows that despite partial coverage and peculiar topological properties of open networks, they offer high levels of performance and network availability to the mobile end users, virtually identical to performance and availability of planned, dedicatedly deployed networks. Our results indicate that the integration of these networks may bring considerable benefits to all parties involved.

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An inhomogeneous spatial node distribution and its stochastic properties

Cited by 36 publications

References 12 publications

HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks

HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks

Coverage algorithms for visual sensor networks

Generating Realistic Node Mobility and Placement for Wireless Multi-Hop Network Simulation

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