For the connectivity of wireless ad hoc network, we have the following results:
1. In 1D, we used the following techniques to obtain the results (from less accurate to exact result).
a) Isolated node: Analyse the probability of the existence of an isolated node.
b) Occupancy theory: subdivide the interval into "cells" of length r (the transmission range). If there exists an empty cell separating two cells that each contains at least one node, then the network is disconnected.
c) Random Interval Graph: The existence of the big spacings. (exact result)
2. In 2D, we do not have the exact result. Instead, the following techniques are used to obtain the asymptotic results.
a) Isolated node: The existence of an isolated node
From 1D results, we can predict that using occupancy theory may improve the results obtained under 2D networks. From observing the occupancy theory methodology, I assume there are two directions that we can look into the connectivity problem.
a) Divide the 2D area into cells and start from here.
b) Look at nodes lying within a certain sector of angle but are not directly connected.
I could not tell any further until I have more concrete ideas on that.
Monday, January 11, 2010
Sunday, January 10, 2010
Notes - The Capacity of Wireless Networks
*This note is written based on [1].
Due to lack of any centralised control, the ad-hoc networks faces different problems at different layers. At the network layer, the main problem is routing. The choice of medium access scheme is also difficult in ad hoc networks, and random access appears to be the current favourite. At the physical layer, an important issue is that of power control. The transmission power should be high enough to reach the intended receiver while causing minimal interference to other nodes.
The direct connectivity between two nodes can be described in two models:
1. The Protocol Model
2. The Physical Model
The authors obtained the capacity based in these two models. In short, the capacity per node decreases as the number of nodes increases. Some implication of the results obtained.
1. The number of uses in a network should be small.
2. Nodes should only communicate with nearby nodes only.
3. A faster rate of decay of signal power with distance allows greater transport and throughput capacity.
4. Group nodes into clusters. Cluster heads are used as relay nodes.
5. Dividing the channels into sub-channels does not change any of the results.
6. To increase throughput, we can add in pure relays into the network (with extra cost).
7. To increase throughput, we can connect base stations by a wired network.
[1] P. Gupta and P. R. Kumar, "The Capacity of Wireless Networks," IEEE Transactions on Information Theory, vol. 46, pp. 388-404, 2000.
Due to lack of any centralised control, the ad-hoc networks faces different problems at different layers. At the network layer, the main problem is routing. The choice of medium access scheme is also difficult in ad hoc networks, and random access appears to be the current favourite. At the physical layer, an important issue is that of power control. The transmission power should be high enough to reach the intended receiver while causing minimal interference to other nodes.
The direct connectivity between two nodes can be described in two models:
1. The Protocol Model
2. The Physical Model
The authors obtained the capacity based in these two models. In short, the capacity per node decreases as the number of nodes increases. Some implication of the results obtained.
1. The number of uses in a network should be small.
2. Nodes should only communicate with nearby nodes only.
3. A faster rate of decay of signal power with distance allows greater transport and throughput capacity.
4. Group nodes into clusters. Cluster heads are used as relay nodes.
5. Dividing the channels into sub-channels does not change any of the results.
6. To increase throughput, we can add in pure relays into the network (with extra cost).
7. To increase throughput, we can connect base stations by a wired network.
[1] P. Gupta and P. R. Kumar, "The Capacity of Wireless Networks," IEEE Transactions on Information Theory, vol. 46, pp. 388-404, 2000.
Thursday, January 7, 2010
Notes - The Connectivity of 2D Ad-Hoc Networks
*This note is written based on [1].
Connectivity is one of the fundamental and widely studied properties of wireless multihop ad-hoc networks. The existing studies applied the theories in the following area:
1. Random Geographic Graph: E.g. for uniformly distributed nodes, the longest nearest-neighbour link and the longest MST (minimum spanning tree) edge have the same value as n goes to infinity. -> connectivity occurs (asymptotically) when the last isolated node disappears.
2. Continuum Percolation: Refer to [2] and [3].
However, as discussed in [1], the theories are only applicable to dense networks. The contribution of the results on dense networks may be limited as network capacity will be compromised. In order to circumvent this problem, the authors added the size of the deployment region as a parameter of the model, and characterise the critical transmission range as the size goes to infinity.
The following two theorems are obtained in [1] for 2D networks:
[1] P. Santi and D. M. Blough, "The Critical Transmitting Range for Connectivity in Sparse Wireless Ad Hoc Networks," IEEE Transactions on Mobile Computing, vol. 2, pp. 25-39, 2003.
[2] P. Gupta and P. R. Kumar, "Critical power for asymptotic connectivity in wireless networks," Stochastic Analysis, Control, Optimization and Applications: A Volume in Honor of W.H. Fleming, W.M. McEneaney, G. Yin, and Q. Zhang (Eds.), pp. 547-566, 1998.
[3] O. Dousse, et al., "Connectivity in ad-hoc and hybrid networks," in IEEE Conference on Computer Communications (INFOCOM), 2002, pp. 1079-1088.
Connectivity is one of the fundamental and widely studied properties of wireless multihop ad-hoc networks. The existing studies applied the theories in the following area:
1. Random Geographic Graph: E.g. for uniformly distributed nodes, the longest nearest-neighbour link and the longest MST (minimum spanning tree) edge have the same value as n goes to infinity. -> connectivity occurs (asymptotically) when the last isolated node disappears.
2. Continuum Percolation: Refer to [2] and [3].
However, as discussed in [1], the theories are only applicable to dense networks. The contribution of the results on dense networks may be limited as network capacity will be compromised. In order to circumvent this problem, the authors added the size of the deployment region as a parameter of the model, and characterise the critical transmission range as the size goes to infinity.
The following two theorems are obtained in [1] for 2D networks:
[1] P. Santi and D. M. Blough, "The Critical Transmitting Range for Connectivity in Sparse Wireless Ad Hoc Networks," IEEE Transactions on Mobile Computing, vol. 2, pp. 25-39, 2003.
[2] P. Gupta and P. R. Kumar, "Critical power for asymptotic connectivity in wireless networks," Stochastic Analysis, Control, Optimization and Applications: A Volume in Honor of W.H. Fleming, W.M. McEneaney, G. Yin, and Q. Zhang (Eds.), pp. 547-566, 1998.
[3] O. Dousse, et al., "Connectivity in ad-hoc and hybrid networks," in IEEE Conference on Computer Communications (INFOCOM), 2002, pp. 1079-1088.
Wednesday, January 6, 2010
Research Conversazione
Research Conversazione- This is an annual event organised by the Faculty of Engineering and IT, University of Sydney. The aim is to showcase the research undertaken by students over the past year. It is also an ideal opportunity for research students to meet the industry representatives and exchange information.
I have prepared posters and presented my research at the Research Conversazione in 2008 and 2009. In 2008, I have won the Energy Australia Award for Advancing Scientific Knowledge. Below are some photos to share. More photos can be found here.
I have prepared posters and presented my research at the Research Conversazione in 2008 and 2009. In 2008, I have won the Energy Australia Award for Advancing Scientific Knowledge. Below are some photos to share. More photos can be found here.
From left to right: Jeffrey, me, Jason
I was explaining my research idea and outcome...
With my poster...
Tuesday, January 5, 2010
Conference Paper - ICC 2010
Seh Chun Ng, Guoqiang Mao and Brian D.O. Anderson, “Properties of 1-D Infrastructure-based Wireless Multi-hop Networks”, to appear in IEEE International Conference on Communications (ICC), 2010.
Abstract:
Many real wireless multi-hop networks are deployed with some infrastructure support, where the results on ad-hoc networks cannot be readily extended to understand the properties of those networks. In this paper, we study those networks in 1-D. Specifically, we consider two types of nodes in the networks: ordinary nodes and powerful nodes, where ordinary nodes are i.i.d and Poissonly distributed in a unit interval and powerful nodes are arbitrarily distributed within the same unit interval. These powerful nodes are inter-connected via some backbone infrastructure. The network is said to be connected, i.e. any two nodes can communicate with each other, if each ordinary node is connected to at least one of the powerful nodes. We call this type of connectivity type-II connectivity. Exact and simplified
asymptotic formulas for type-II connectivity probability and the average hop count between two arbitrary nodes are obtained. Further we prove that equi-distant powerful nodes deployment delivers the optimum performance which maximizes the type-
II connectivity probability. These results are important for the design and deployment of 1-D infrastructure-based networks and provide useful insights into the analysis of higher dimensional networks.
Abstract:
Many real wireless multi-hop networks are deployed with some infrastructure support, where the results on ad-hoc networks cannot be readily extended to understand the properties of those networks. In this paper, we study those networks in 1-D. Specifically, we consider two types of nodes in the networks: ordinary nodes and powerful nodes, where ordinary nodes are i.i.d and Poissonly distributed in a unit interval and powerful nodes are arbitrarily distributed within the same unit interval. These powerful nodes are inter-connected via some backbone infrastructure. The network is said to be connected, i.e. any two nodes can communicate with each other, if each ordinary node is connected to at least one of the powerful nodes. We call this type of connectivity type-II connectivity. Exact and simplified
asymptotic formulas for type-II connectivity probability and the average hop count between two arbitrary nodes are obtained. Further we prove that equi-distant powerful nodes deployment delivers the optimum performance which maximizes the type-
II connectivity probability. These results are important for the design and deployment of 1-D infrastructure-based networks and provide useful insights into the analysis of higher dimensional networks.
Conference Paper - WCNC 2010
Seh Chun Ng, Wuxiong Zhang, Yang Yang and Guoqiang Mao, “Analysis of Access and Connectivity Probabilities in Infrastructure-Based Vehicular Relay Networks”, to appear in IEEE Wireless Communications and Networking Conference (WCNC), 2010.
Abstract:
Coverage is an important problem in wireless networks. Together with the access probability, which measures how well an arbitrary user can access a wireless network, in particular VANET, they are often used as major indicators of the quality of the network. In this paper, we investigate the coverage and access probability of the vehicular networks with roadside infrastructure, i.e. base stations. Specifically, we analyze the relation between these key parameters, i.e. the coverage range of base stations, coverage range of vehicles, vehicle density and distance between adjacent base stations, and how these parameters interact with each other to collectively determine the coverage and the access probability. We use the connectivity probability, the probability that all nodes in the network are connected to at least one base station within a designated number of hops, as a measure of the coverage. We derived close-form formulas for the connectivity probability and the access probability for a 1D vehicular network bounded by two adjacent base stations. The analytical results have been validated by simulations. The results in the paper can be used by network operators to design networks with specific service coverage guarantees.
Abstract:
Coverage is an important problem in wireless networks. Together with the access probability, which measures how well an arbitrary user can access a wireless network, in particular VANET, they are often used as major indicators of the quality of the network. In this paper, we investigate the coverage and access probability of the vehicular networks with roadside infrastructure, i.e. base stations. Specifically, we analyze the relation between these key parameters, i.e. the coverage range of base stations, coverage range of vehicles, vehicle density and distance between adjacent base stations, and how these parameters interact with each other to collectively determine the coverage and the access probability. We use the connectivity probability, the probability that all nodes in the network are connected to at least one base station within a designated number of hops, as a measure of the coverage. We derived close-form formulas for the connectivity probability and the access probability for a 1D vehicular network bounded by two adjacent base stations. The analytical results have been validated by simulations. The results in the paper can be used by network operators to design networks with specific service coverage guarantees.
Photos - Hyams Beach
Hyams beach - whitest sand in the world. 3 hours drive from Sydney.
The sign
The beach
Conference - Budapest, Hungary
I went to Budapest, Hungary to attend WCNC 2009. My first trip ever to Europe.
Buda Castle
The Parliament House
Traditional food
Isn't it colourful?
My portrait
New friends that I have made in WCNC - Michael, Elias, Max.
Conference Paper - WCNC 2009
Seh Chun Ng, Guoqiang Mao and Brian D.O. Anderson, “Energy Savings Achievable in Connection Preserving Energy Saving Algorithms”, in IEEE Wireless Communications and Networking Conference (WCNC), 2009, pp. 1-6.
Abstract:
Energy saving is an important design consideration in wireless sensor networks. In this paper, we analyze the energy savings that can be achieved in a sensor network where each sensor is capable of reducing its transmission power from a maximum power $p_m$, compared with that in a sensor network where each sensor can only transmit at a constant power level $p_m$. To achieve a fair comparison, we assume sensors in both types of sensor networks are connected to the same set of neighbors, i.e. no connection is lost as a result of a sensor reducing its transmission power. We further assume that sensors are distributed in a given area following a Poisson distribution with known node density and the radio propagation is described by a log-normal model. Ignoring boundary effect, we establish analytically the probability for a sensor to achieve an energy saving of at least $h$ dB. We also obtain the expected percentage of energy savings which can be substantial. The research reported in the paper helps to answer questions such as whether the energy savings achieved by using a sensor with a variable-transmission-power (and the consequent extension of its lifetime) justify the additional cost involved in manufacturing it.
Abstract:
Energy saving is an important design consideration in wireless sensor networks. In this paper, we analyze the energy savings that can be achieved in a sensor network where each sensor is capable of reducing its transmission power from a maximum power $p_m$, compared with that in a sensor network where each sensor can only transmit at a constant power level $p_m$. To achieve a fair comparison, we assume sensors in both types of sensor networks are connected to the same set of neighbors, i.e. no connection is lost as a result of a sensor reducing its transmission power. We further assume that sensors are distributed in a given area following a Poisson distribution with known node density and the radio propagation is described by a log-normal model. Ignoring boundary effect, we establish analytically the probability for a sensor to achieve an energy saving of at least $h$ dB. We also obtain the expected percentage of energy savings which can be substantial. The research reported in the paper helps to answer questions such as whether the energy savings achieved by using a sensor with a variable-transmission-power (and the consequent extension of its lifetime) justify the additional cost involved in manufacturing it.
Photos - Taronga Zoo
Wallabies... not look at camera
Emu... not look at camera
Echidna... not look at camera
Koala bear... not look at camera
Me... look at camera
Photos - Blue Mountains
Blue mountains - 2 hours by car or train from Sydney to Katoomba. Note that the name starts with "K" not "C", so the name should be originally from Aboriginal language.
Photos - Zen Oasis
Zen Oasis is a vegetarian restaurant, very near to a historical (and small) town named Berrima, 1.5 hours drive from Sydney.
The vegetarian restaurant...
The lake outside the restaurant
Looks like a well
Photos - Paddington Markets
Paddington markets, open every Saturday from 10am.
In front of Paddington market (church)
Scary?
Photos - Government House
Government house is located near botanic garden, Sydney.
Me @ government house
A garden outside government house
Government house (viewed from the gate)
Photos - University of Sydney
Quadrangle
Any meaning behind?
Quadrangle viewed from victoria park
Graffiti tunnel
PhD life is??
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