Spatial and Temporal Spatial and Temporal Relationship for Stochastic Relationship for Stochastic

NetworksNetworks随机网络的时空观随机网络的时空观

Xinbing Wang

Dept. of Electronic EngineeringShanghai Jiao Tong University

Shanghai, China

2

Related Work—A Partial ViewRelated Work—A Partial View At Year 2000, there are two ground breaking papersAt Year 2000, there are two ground breaking papers A Partial View of Previous Work on CapacityA Partial View of Previous Work on Capacity

Gupta & KumarGupta & Kumar

Grossglauser & TseGrossglauser & Tse

NeelyNeely

Xiangyang LiXiangyang Li

Lei YingLei Ying

Xiaojun LinXiaojun Lin

Guoqiang Mao, Pan Li, Guoqiang Mao, Pan Li, Zheng Wang, Chi Zheng Wang, Chi Zhang, etcZhang, etc

Garetto

3

Typical Related WorkTypical Related Work Scaling Laws (Gupta & Kumar [1]) citation: citation:

  7640/63827640/6382

Random placementRandom placement Protocol modelProtocol model Random source-destination pairingRandom source-destination pairing Result: maximal per node achievable rate scales asResult: maximal per node achievable rate scales as Using multi-hop and geographic routingUsing multi-hop and geographic routing

[1] P. Gupta, P. R. Kumar, “The Capacity of Wireless Network,” IEEE Trans. on Inf. Theory, [1] P. Gupta, P. R. Kumar, “The Capacity of Wireless Network,” IEEE Trans. on Inf. Theory, vol. 46, no. 2, pp. 388-404, Mar. 2000. vol. 46, no. 2, pp. 388-404, Mar. 2000.

multi-hopmulti-hop transmissiontransmission

large scale wireless large scale wireless

networksnetworks

1

n

ground breakingground breaking

workwork

pessimistic pessimistic

resultresult

4

Typical Related WorkTypical Related Work Mobile (Grossglauser & Tse [2]) Mobile (Grossglauser & Tse [2]) citation: citation:

2816/21342816/2134 i.i.d.i.i.d. mobility mobility Two-hop relay algorithmTwo-hop relay algorithm Constant per-node capacityConstant per-node capacity Large delayLarge delay

[2] M. Grossglauser, D. Tse, “Mobility Increases the Capacity of Ad-hoc Wireless Networks,” in Proc. [2] M. Grossglauser, D. Tse, “Mobility Increases the Capacity of Ad-hoc Wireless Networks,” in Proc. IEEE INFOCOM, vol. 3, pp. 1360-1369, Apr. 2001.IEEE INFOCOM, vol. 3, pp. 1360-1369, Apr. 2001.

Two-hop relay scheduling policy Two-hop relay scheduling policy

The first to achieve The first to achieve

constant per-node constant per-node

capacitycapacity

5

Typical Related WorkTypical Related Work Capacity-Delay Tradeoff (Neely [3]) Capacity-Delay Tradeoff (Neely [3]) citation: 257citation: 257 i.i.d.i.i.d. mobility mobility Cell-partitioned networkCell-partitioned network Capacity vs DelayCapacity vs Delay

[3] M. J. Neely and E. Modiano, “Capacity and Delay Tradeoffs for Ad-Hoc Mobile Networks,” IEEE [3] M. J. Neely and E. Modiano, “Capacity and Delay Tradeoffs for Ad-Hoc Mobile Networks,” IEEE Transactions on Information Theory, vol. 51, no. 6, pp. 1917-1937, June 2005.Transactions on Information Theory, vol. 51, no. 6, pp. 1917-1937, June 2005.

A cell-partitioned ad-hoc wireless network with C cells and A cell-partitioned ad-hoc wireless network with C cells and N mobile users.N mobile users.

The first to address The first to address the problem of the problem of capacity-delay capacity-delay tradeoff tradeoff

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Typical Related WorkTypical Related Work Capacity in Heterogeneous Networks (Garretto [4]) Capacity in Heterogeneous Networks (Garretto [4])

citation: 34citation: 34 Heterogeneous mobile networkHeterogeneous mobile network Adjustable network sizeAdjustable network size Uniform and clustered modelUniform and clustered model Optimal per-node capacity in different Optimal per-node capacity in different

heterogeneous levelheterogeneous level

[4] M. Garetto, P. Giaccone, and E. Leonardi, “Capacity scaling in delay tolerant networks with [4] M. Garetto, P. Giaccone, and E. Leonardi, “Capacity scaling in delay tolerant networks with heterogeneous mobile nodes,” in ACM MobiHoc ’07, New York, NY, USA, 2007, pp. 41–50. heterogeneous mobile nodes,” in ACM MobiHoc ’07, New York, NY, USA, 2007, pp. 41–50.

Home point distribution of uniform Home point distribution of uniform

and cluster model and cluster model

Per-node capacity vs network Per-node capacity vs network

size nsize nαα

The first to study The first to study capacity with capacity with heterogeneous mobility heterogeneous mobility

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Typical Related WorkTypical Related Work Multicast (Xiangyang Li [5]) Multicast (Xiangyang Li [5]) citation: 94citation: 94 Static networkStatic network Manhattan routing treeManhattan routing tree One source to One source to kk destinations destinations Capacity related to Capacity related to kk

[5] X. Li, “Multicast Capacity of Large Scale Wireless Ad Hoc Networks”, [5] X. Li, “Multicast Capacity of Large Scale Wireless Ad Hoc Networks”, IEEE/ACM Trans. IEEE/ACM Trans. Networking, Vol.17, No. 3, ppNetworking, Vol.17, No. 3, pp. . 950-961,950-961, Jan. 2008.Jan. 2008.

The first to study The first to study

multicast capacitymulticast capacity

Data copyData copy Manhanttan Routing TreeManhanttan Routing Tree

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Other Related WorkOther Related Work Definition-based CapacityDefinition-based Capacity

[1] P. Gupta, P. R. Kumar, “The Capacity of Wireless Network,” [1] P. Gupta, P. R. Kumar, “The Capacity of Wireless Network,” IEEE Trans. on Inf. Theory, vol. 46, no. 2, pp. 388-404, Mar. IEEE Trans. on Inf. Theory, vol. 46, no. 2, pp. 388-404, Mar. 2000.2000.

[2] P. Gupta, and P. R. Kumar, “Towards an Information Theory [2] P. Gupta, and P. R. Kumar, “Towards an Information Theory of Large Networks: An Achievable Rate Region,” IEEE Trans, of Large Networks: An Achievable Rate Region,” IEEE Trans, Inf. Theory, IT -49, 2003.Inf. Theory, IT -49, 2003.

[3] S. Toumpis, and A. J. Goldsmith, “Capacity Regions for [3] S. Toumpis, and A. J. Goldsmith, “Capacity Regions for Wireless Ad Hoc Networks,” IEEE Trans. Wireless Wireless Ad Hoc Networks,” IEEE Trans. Wireless Communications, V2, No. 4, July 2003.Communications, V2, No. 4, July 2003.

[4] S. Ahmad, A. Jovicic, and P. Viswanath, “On Outer Bounds [4] S. Ahmad, A. Jovicic, and P. Viswanath, “On Outer Bounds to the Capacity Region of Wireless Networks,” IEEE Trans, Inf. to the Capacity Region of Wireless Networks,” IEEE Trans, Inf. Theory, IT -52, No. 6, June 2006.Theory, IT -52, No. 6, June 2006.

Interference-Model-based CapacityInterference-Model-based Capacity [5] S. Li, Y. Liu, and X.-Y Li, “Capacity of large scale wireless [5] S. Li, Y. Liu, and X.-Y Li, “Capacity of large scale wireless

networks under Gaussian channel model,” ACM MobiCom’08, networks under Gaussian channel model,” ACM MobiCom’08, Sep. 14-19,2008.Sep. 14-19,2008.

[6] M. Francescheti, O. Dousse, N. C. Tse and P. Thiran, [6] M. Francescheti, O. Dousse, N. C. Tse and P. Thiran, “Closing the grap in the capacity of wireless network via “Closing the grap in the capacity of wireless network via percolation theory,” IEEE Trans, Inf. Theory, IT-53, pp1009-percolation theory,” IEEE Trans, Inf. Theory, IT-53, pp1009-1018, No. 3, Mar., 2007.1018, No. 3, Mar., 2007.

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Other Related WorkOther Related Work Network-Topology- based Capacity

[7] P. Li, C. Zhang and Y. Fang, “Capacity and Delay of Hybrid Wireless [7] P. Li, C. Zhang and Y. Fang, “Capacity and Delay of Hybrid Wireless Broadband Access Networks,” IEEE Journal on Selected Areas in Broadband Access Networks,” IEEE Journal on Selected Areas in Communications, vol. 27, No. 2, pp117-125, Feb. 2009.Communications, vol. 27, No. 2, pp117-125, Feb. 2009.

[8] B. Liu, P. Thiran and D. Towsley, “Capacity of a wireless ad hoc network [8] B. Liu, P. Thiran and D. Towsley, “Capacity of a wireless ad hoc network with infrastructure,” Proceeding of ACM MobiHoc, Montreal, Quebec, Canada, with infrastructure,” Proceeding of ACM MobiHoc, Montreal, Quebec, Canada, September 2007.September 2007.

[9]M. Garetto, P. Giaccone, and E. Leonardi, “Capacity scaling in delay tolerant [9]M. Garetto, P. Giaccone, and E. Leonardi, “Capacity scaling in delay tolerant networks with heterogeneous mobile nodes,” in ACM MobiHoc ’07, New York, networks with heterogeneous mobile nodes,” in ACM MobiHoc ’07, New York, NY, USA, 2007, pp. 41–50. NY, USA, 2007, pp. 41–50.

[10] U. Niesen, P.Gupta and D.Shah, “On capacity scaling in arbitrary wireless [10] U. Niesen, P.Gupta and D.Shah, “On capacity scaling in arbitrary wireless networks”, IEEE Trans. on Inf. Theory, vol.55.no. 9, Sept, 2009.networks”, IEEE Trans. on Inf. Theory, vol.55.no. 9, Sept, 2009.

Traffic-Pattern-based Capacity [11] A. KeshavarzHaddad, V. Ribeiro and R. Riedi, “Broadcast Capacity in [11] A. KeshavarzHaddad, V. Ribeiro and R. Riedi, “Broadcast Capacity in

Multihop Wireless Networks,” MobiCom’06, September, pp 23–26, 2006, Los Multihop Wireless Networks,” MobiCom’06, September, pp 23–26, 2006, Los Angeles, California, USA.Angeles, California, USA.

[12] S. Shakkottai, X. Liu and R. Srikant, “The Multicast Capacity of Large [12] S. Shakkottai, X. Liu and R. Srikant, “The Multicast Capacity of Large Multihop Wireless Networks,” MobiHoc’07, September 9–14, 2007, Montréal, Multihop Wireless Networks,” MobiHoc’07, September 9–14, 2007, Montréal, Québec, Canada.Québec, Canada.

[13] X. Li, S. Tang, and F. Ophir, “Multicast capacity for large scale wireless ad [13] X. Li, S. Tang, and F. Ophir, “Multicast capacity for large scale wireless ad hoc networks,” Proc. ACM Mobicom 2007.hoc networks,” Proc. ACM Mobicom 2007.

[14] [14] Z. Wang, H. Sadjadpour, J. J. Garcia-Luna-Aceves, “A Unifying Perpective on the Capacity of Wireless Ad Hoc Networks”, IEEE Infocom 2008. 

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Other Related WorkOther Related Work Capacity ImprovementCapacity Improvement

[15] M. J. Neely and E. Modiano, “Capacity and Delay Tradeoffs [15] M. J. Neely and E. Modiano, “Capacity and Delay Tradeoffs for Ad-Hoc Mobile Networks,” IEEE Transactions on for Ad-Hoc Mobile Networks,” IEEE Transactions on Information Theory, vol. 51, no. 6, pp. 1917-1937, June 2005.Information Theory, vol. 51, no. 6, pp. 1917-1937, June 2005.

[16] S. Yi, Y. Pei and S. Kalyanaraman, “On the Capacity [16] S. Yi, Y. Pei and S. Kalyanaraman, “On the Capacity Improvement of Ad Hoc Wireless Networks Using Directional Improvement of Ad Hoc Wireless Networks Using Directional Antennas,” MobiHoc’03, Annapolis, Maryland, USA, June, 2003.Antennas,” MobiHoc’03, Annapolis, Maryland, USA, June, 2003.

[17] C. Zhang, Y. Fang, X. Zhu, “Throughput-Delay Tradeoffs in [17] C. Zhang, Y. Fang, X. Zhu, “Throughput-Delay Tradeoffs in Large-Scale MANETs with Network Coding,” INFOCOM Large-Scale MANETs with Network Coding,” INFOCOM 2009,pp. 199-207.2009,pp. 199-207.

[18] A. Ozgur, O. Leveque and David N. C. Tse, “Hierarchical [18] A. Ozgur, O. Leveque and David N. C. Tse, “Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks,” IEEE Transactions on Information Theory, vol. 53, Networks,” IEEE Transactions on Information Theory, vol. 53, no. 10, Oct., 2007.no. 10, Oct., 2007.

[19] H. R. Sadjadpour, Z. Wang, and J.J. Garcia-Luna-Aceves, [19] H. R. Sadjadpour, Z. Wang, and J.J. Garcia-Luna-Aceves, “The capacity of Wireless Ad Hoc Networks with Multi-Packet “The capacity of Wireless Ad Hoc Networks with Multi-Packet Reception,” IEEE Transactions on Communications, Vol. 58, No. Reception,” IEEE Transactions on Communications, Vol. 58, No. 2, pp. 600-610, February, 2010.2, pp. 600-610, February, 2010.

[20] P. Kyasanur and N. H. Vaidya, “Capacity of Multichannel [20] P. Kyasanur and N. H. Vaidya, “Capacity of Multichannel Wireless Networks Under the Protocol Model,” IEEE/ACM Wireless Networks Under the Protocol Model,” IEEE/ACM Trans. on Networking, vol.17, no. 2, April 2009.Trans. on Networking, vol.17, no. 2, April 2009.

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Our Initial ResultsOur Initial ResultsCapacity Delay Tradeoff (ToN, TPDS, TMC, ToN, TPDS, TMC,

MobiHoc, INFOCOM, ICC, etcMobiHoc, INFOCOM, ICC, etc) Multicast, Converge-castMulticast, Converge-cast Delay and Capacity Tradeoff Delay and Capacity Tradeoff Capacity in Cognitive Radio NetworksCapacity in Cognitive Radio Networks

Coverage (ICDCS 2011ICDCS 2011)

Connectivity (Mobicom 2009Mobicom 2009)

Topology (INFOCOM 2012INFOCOM 2012)

Computation (INFOCOM 2012INFOCOM 2012)

Delay and Capacity TradeoffDelay and Capacity Tradeoff Analysis for MotionCast Analysis for MotionCast

In this paper, we study capacity and delay tradeoffs for MotionCast. We utilize redundant packets transmissions to realize the tradeoff, and present the performance of the 2-hop relay algorithm without and with redundancy respectively.

We find that the capacity of the 2-hop relay algorithm without redundancy is better than that of static networks when And our tradeoff is better than that of directly extending the tradeoff for unicast to multicast.

[1] Xinbing Wang, W. Huang, S. Wang, J. Zhang, C. Hu, "Delay and Capacity Tradeoff Analysis [1] Xinbing Wang, W. Huang, S. Wang, J. Zhang, C. Hu, "Delay and Capacity Tradeoff Analysis for MotionCast," in for MotionCast," in IEEE/ACM Transactions on NetworkingIEEE/ACM Transactions on Networking, 2011, 2011. . 12

Relay-destinations transmission. (a) Each packet delivered to a relay. (b) Each relay can make a packet into k similar cppies.

[2] Xinbing Wang, Y. Bei, Q. Peng, L. Fu, "Speed Improves Delay-Capacity Tradeoff in [2] Xinbing Wang, Y. Bei, Q. Peng, L. Fu, "Speed Improves Delay-Capacity Tradeoff in MotionCast," to appear in MotionCast," to appear in IEEE Transactions on Parallel and Distributed SystemsIEEE Transactions on Parallel and Distributed Systems , 2011, 2011. . 13

Speed Improves Delay-Capacity Tradeoff Speed Improves Delay-Capacity Tradeoff in MotionCastin MotionCast

In this paper, we study the relationship between mobility speed R and delay-capacity tradeoff ratio with multicast traffic pattern.

We show that there is a special turning point when mobility speed varies from zero to the scale of network.

In both LSRM and GSRM, as the number of destinations of each multicast session increases, the impact of mobility is more significant.

Local-based Speed-Restricted Model

(LSRM)

Global-based Speed-Restricted Model

(GSRM)

[3] W. Huang, Xinbing Wang, "Throughput and Delay Scaling of General Cognitive [3] W. Huang, Xinbing Wang, "Throughput and Delay Scaling of General Cognitive Networks," in Networks," in IEEE INFOCOM IEEE INFOCOM 20112011, Shanghai, China., Shanghai, China. 14

Throughput and Delay Scaling of General Throughput and Delay Scaling of General Cognitive NetworksCognitive Networks

All previous works consider specific primary networks with predefined communication schemes, and then design secondary protocols accordingly.

What is the performance of a general cognitive networks with arbitrary primary users?

In this paper, we show that the cognitive networks can perform as well as standalone networks under some general conditions.

Primary network operates at a SINR level slightly larger than reception threshold

Primary network employs round-robin TDMA like scheduling schemes or its traffic flows

The TX ranges of primary and secondary networks satisfy a condition

[4] Q. Peng, Xinbing Wang, H. Tang, "Heterogeneity Increases Multicast Capacity in [4] Q. Peng, Xinbing Wang, H. Tang, "Heterogeneity Increases Multicast Capacity in Clustered Network," in Clustered Network," in IEEE INFOCOM IEEE INFOCOM 20112011, Shanghai, China. , Shanghai, China.

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Heterogeneity Increases Multicast Capacity Heterogeneity Increases Multicast Capacity in Clustered Networkin Clustered Network

1616

Our cooperative MIMO scheme in static networks breaks the bottleneck and can achieve an aggregate throughput of order 1.

Our cooperative MIMO scheme in MANETs can achieve a per-node throughput of Θ(1) while the delay is reduced to Θ(k) where k is the number of sources.

Our results well cover other traffic patterns and act as a generalization.

Converge-Cast with MIMOConverge-Cast with MIMO

[5] L. Fu, Y. Qin, Xinbing Wang, X. Liu, "Converge-Cast with MIMO," in [5] L. Fu, Y. Qin, Xinbing Wang, X. Liu, "Converge-Cast with MIMO," in IEEE IEEE INFOCOM INFOCOM 20112011, Shanghai, China. , Shanghai, China.

Step 1:

Preparing for cooperation with recursion

Step 2:

Multi-hop MIMO Transmission

Step 3:

Cooperative Reception

[6] Y. Wang, X. Chu, Xinbing Wang, Y. Cheng, "Optimal Multicast Capacity and Delay Tradeoffs [6] Y. Wang, X. Chu, Xinbing Wang, Y. Cheng, "Optimal Multicast Capacity and Delay Tradeoffs in MANETs: A Global Perspective," in in MANETs: A Global Perspective," in IEEE INFOCOM IEEE INFOCOM 20112011, Shanghai, China. , Shanghai, China.

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Optimal Multicast Capacity and Delay Tradeoffs Optimal Multicast Capacity and Delay Tradeoffs in MANETs: A Global Perspectivein MANETs: A Global Perspective

In our work, we give a global perspective of multicast capacity and delay analysis in Mobile Ad-hoc Networks (MANETs). Specifically, we consider four node mobility models: 1. two-dimensional i.i.d. mobility; 2. two-dimensional hybrid random walk; 3. one-dimensional i.i.d. mobility; 4. one-dimensional hybrid random walk. Two mobility time-scales are included: 1. Fast mobility; 2. Slow mobility.

We generalize the optimal delay-throughput tradeoffs in unicast traffic pattern and generalize the multicast capacity result under delay constraint when considering the two-dimensional i.i.d. fast mobility model.

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Fundamental Lower Bound for Node Buffer Size Fundamental Lower Bound for Node Buffer Size in Intermittently Connected Wireless Networksin Intermittently Connected Wireless Networks

We study the lower bounds for node buffer in intermittently connected network. In supercritical case, the achievable lower bound does not increase as the network size

grows. In subcritical case, the achievable lower bound is , where n is the number of

nodes in the network.

In both cases, lower bounds for buffer occupation scales linearly to the length of time

slot.

[7] Y. Xu, Xinbing Wang, "Fundamental Lower Bound for Node Buffer Size in Intermittently [7] Y. Xu, Xinbing Wang, "Fundamental Lower Bound for Node Buffer Size in Intermittently Connected Wireless Networks," in Connected Wireless Networks," in IEEE INFOCOM 2011IEEE INFOCOM 2011, , Shanghai, China. Shanghai, China.

Supercritical Case: active giant exists in each time slot

Subcritical Case: no active giant in each time slot

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Mobility Increases the Connectivity of K-hop Mobility Increases the Connectivity of K-hop Clustered Wireless NetworksClustered Wireless Networks

We study the critical transmission range for conncecivity, k-hop clustered networks, random walk mobility with non-trivial velocity and i.i.d. mobility model.

Our results show that Mobility does improve connectivity in k-hop clustered networks, and it also significantly decreases the energy consumption and the power-delay trade-off.

[8] [8] Q. Wang, X. Wang, X. Lin, "Mobility Increases the Connectivity of K-hop Clustered Wireless Networks," in Proc. of  ACM Mobicom 2009ACM Mobicom 2009, Beijing, September 2009.

A summary of all the results achieved in this work.

[9] X. Wang, Xinbing Wang, J. Zhao, "Impact of Mobility and Heterogeneity on [9] X. Wang, Xinbing Wang, J. Zhao, "Impact of Mobility and Heterogeneity on Coverage and Energy Consumption in Wireless Sensor Networks,“ in Coverage and Energy Consumption in Wireless Sensor Networks,“ in IEEE ICDCS IEEE ICDCS 20112011, Minneapolis, Minnesota, USA, 2011., Minneapolis, Minnesota, USA, 2011.

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Impact of Mobility and Heterogeneity on Coverage and Impact of Mobility and Heterogeneity on Coverage and Energy Consumption in Wireless Sensor NetworksEnergy Consumption in Wireless Sensor Networks

In this paper, we have studied coverage in mobile and heterogeneous wireless sensor networks. Specifically, we have investigated asymptotic coverage under uniform deployment model with i.i.d. and 1-dimensional random walk mobility model, respectively.

Mobility is found to decrease sensing energy consumption. On the other hand, we demonstrate that heterogeneity increases energy consumption under 1-dimensional random walk mobility model but imposes no impact under the i.i.d. model. The k-coverage under Poisson deployment scheme with 2-dimensional random walk mobility model has been discussed, which also identifies the coverage improvement brought by mobility.

Coverage Under 1-Dimensional Random Walk Mobility ModelCoverage Under I.I.D Model

21

In-network computation:PreliminariesIn-network computation:Preliminaries

Network Computation OverviewNetwork Computation Overview

RecoveryRecovery

X

Random projections

y Transform basisTransform basis

CoefficientCoefficientRandom matrixRandom matrix

(1)

(2)

22

Problem FormulationProblem Formulation

Computation FormulationComputation Formulation

Measurements of Measurements of nn sensor nodes sensor nodes Random Projections:Random Projections: Compressive Sensing:Compressive Sensing: The target function can be represented as a The target function can be represented as a

1[ , , ]Tnx xx 1[ , , ]Tmy yy

: nF x y

1

n

i ij jj

F x

: a random Gaussian or Bernoulli matrix.

y x

Multiround Random Linear Function: Multiround Random Linear Function:

Tree-based computation protocol with CSTree-based computation protocol with CS

23

ProtocolProtocol

Intra-cell Protocol

Inter-cell Protocol• Each cell head generates random coefficients receives the values from

its child cell heads, computes the value of and transmits the result to the parent cell head.

• Computation is repeated for m rounds using different random coefficients.

, , 1 ,1

jnk k

i j i j i j jk

y y d

,ki j

24

通信 : 以传递真实信息为目的，对源信息比特进行的调制解调、编解码、重传等操作。过程中不涉及对源信息的改变，网络传递的是信息流（ information flow）

计算 : 以处理信息为目的，对信息进行函数化操作。过程中涉及的是信息函数（ information function）

计算通信：以传递信息函数为目的，通过信息传递和信息处理的深度耦合，同时运用计算和通信资源在网络传递函数流（ function flow）

计算

通信计算通信

计算通信

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Our Future Work—A New Our Future Work—A New PerspectivePerspective The network structure in all the previous work is fixed and given

in prior, ignoring both the spatial and temporal dynamic evolution: Ad-hoc wireless networkAd-hoc wireless network Uniformly and independently distributed usersUniformly and independently distributed users Independent mobility pattern between usersIndependent mobility pattern between users Network with both ad-hoc users and infrastructure supportNetwork with both ad-hoc users and infrastructure support The fixed network size and areaThe fixed network size and area

A new type of hybrid network is emerging with the development of Internet: The combination of both wireless and wire lined devices.The combination of both wireless and wire lined devices. New protocol design requirement on such new hybrid networks.New protocol design requirement on such new hybrid networks. The feature of heterogeneity and variability of such networks.The feature of heterogeneity and variability of such networks.

26

Our Future Work—A New Our Future Work—A New PerspectivePerspective

The previous work lacks the good The previous work lacks the good understanding of the nature of understanding of the nature of wireless network:wireless network: The assumption of fixed The assumption of fixed

network model.network model. Not applicable to other types Not applicable to other types

of networks.of networks. No deep reflection on inner No deep reflection on inner

relationship between capacity relationship between capacity and delay.and delay.

Study a new spatial and temporal Study a new spatial and temporal based network model can help us based network model can help us better understand the nature of better understand the nature of network topology and his temporal network topology and his temporal and spatial relationship as well as a and spatial relationship as well as a more generalized model.more generalized model.

Why study such a new Why study such a new network?network?

27

Our Future Work—A New Our Future Work—A New PerspectivePerspective

Our Goal of the StudyOur Goal of the StudyCapacity in static hybrid network (the Capacity in static hybrid network (the spatial spatial relationship of information flow)relationship of information flow)

Capacity (the Capacity (the spatial spatial relationship of information relationship of information flow), delay (the flow), delay (the temporal temporal relationship of relationship of information flow) and tradeoff in mobile hybrid information flow) and tradeoff in mobile hybrid network (the network (the spatial and temporal spatial and temporal relationship of relationship of information flow)information flow)

Coverage of hybrid network (the Coverage of hybrid network (the spatial end-to-spatial end-to-end end relationship of network)relationship of network)

Connectivity of hybrid network (the Connectivity of hybrid network (the spatial and spatial and temporal end-to-end temporal end-to-end relationship of network)relationship of network)

Explore: Spatial, Temporal, Frequency, and Explore: Spatial, Temporal, Frequency, and Content’s correlationsContent’s correlations

The spatial impact node A The spatial impact node A has on its adjacent nodes has on its adjacent nodes and the temporal impact it and the temporal impact it has during the dynamic has during the dynamic changing of its position.changing of its position.

28

Our Future Work—A New Our Future Work—A New PerspectivePerspective

Challenges and open questionsChallenges and open questionsHow to extract such new hybrid networks into How to extract such new hybrid networks into mathematical modelsmathematical models

Modeling of temporal and spatial relationship Modeling of temporal and spatial relationship between nodes and information flowsbetween nodes and information flows

Modeling of such new hybrid systemModeling of such new hybrid system

Mathematical analysis of such networks may be very Mathematical analysis of such networks may be very complicated.complicated.

Difficult to analyze the inter-dependency of nodes’ Difficult to analyze the inter-dependency of nodes’ behavior and information transferbehavior and information transfer

Thank you !Thank you !