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研究生:吳尚鴻
研究生(外文):Shan-Hung Wu
論文名稱:瞬變網路環境中的省電式資料管理
論文名稱(外文):Energy-Efficient Data Management in Transient Networks
指導教授:陳銘憲陳銘憲引用關係
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:121
中文關鍵詞:行動資料管理詢問處理電源管理媒體控制管理
外文關鍵詞:Mobile Data ManagementQuery ProcessingPower SavingMedium Access ControlQuorum System
相關次數:
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資料管理系統在瞬變網路環境中(例如行動隨意網路、感知網路、以及車輛網路)往往扮演著重要的角色讓使用者可以藉由詢問來分析或理解現實生活所發生的現象。傳統資料庫中的詢問處理系統可能並不適用於瞬變網路環境,因為此網路的結構高度動態,並且每個節點的能力受限於電力、頻寬、以及運算能力等的限制。本論文探討如何在瞬變網路中有效地達成資料管理。我們從不同的觀點來切入詢問處理的議題,包括詢問處理的演算法、演算法之下的通訊協定、以及前兩者之間的交互影響關係。

K-Nearest Neighbors (KNN)詢問是一個資料管理中廣泛被討論的議題。在瞬變網路中(尤其是行動隨意網路),如何節省節點的電力消耗是一個重要課題;然而,目前的KNN詢問處理演算法需要不同的索引結構,這些索引的建立以及維護往往需要耗費許多電力,並且導致過長的處理時間,因此這些KNN詢問處理演算法仍不易被實施於瞬變網路中。在第二章,我們提出了一個新的路程式詢問處理演算法:DIKNN。此演算法藉由結合詢問發散以及回覆收集,避免了建立以及維護索引所需要耗費的電力,因此相較於之前的演算法更適用於瞬變網路環境中。

在第三章,我們探討如何在媒體控制管理層節省電力。雖然Quorum-based Power Saving (QPS)通訊協定已經被廣泛的提出並用於隨意網路以增加節點的可用時間;然而,這些協定強制規定每一個節點必須都要用同一個長度的循環規則來作息。由於特定長度的循環規則會同時影響省電的程度以及資料接收的延遲時間,不同的節點往往希望能夠選擇最適合自己長度的循環規則。在現行的QPS通訊協定中循環規則的長度是固定或受限於某些特殊的值(例如質數),這項限制大大影響了QPS通訊協定的彈性。我們增廣了傳統的Quorum System提出了Hyper Quorum System (HQS)的概念,使得每個節點都可以選擇任意長度的循環規則來作息以達到最適合的省電程度。

基於第二章以及第三章的結果,在第四章我們探討應用層中的詢問處理演算法以及媒體控制管理層中的QPS通訊協定如何交互影響。我們考慮了網路層且在行動隨意網路中相當普及的叢及通訊協定,提出了Asymmetric Cyclic Quorum (ACQ) system。ACQ考慮了媒體控制管理層以上層級的需求與特性,更進一步地節省每個節點的電力。在第五章,我們沿伸ACQ的概念,為車輛網路提出了DSRC-AA省電通訊協定。此通訊協定適用於高度動態並且對資料傳輸延遲容忍度極小的網路環境。

我們實作的模擬實驗顯示:DIKNN,ACQ/DSRC-AA,以及HQS分別可以在應用層、網路層、以及媒體控制管理層達到可觀的省電效果,並且提供可容許的延遲與正確性。
Data management system in transient networks, such as Mobile Ad hoc NETworks (MANETs), sensor networks, and vehicular networks, is essential to allow users to analyze/reason a physical phenomenon by issuing queries. Traditional query processing techniques used by the database systems may not be adequate for transient networks as in these networks, the topology of nodes (or stations) is highly dynamic; and the capability of each node is limited by energy, bandwidth, and computing power. In this dissertation, we study how to achieve energy efficient data management in transient networks. Our study covers different aspects of query processing, including the query processing algorithms, the underlying network protocols to execute the algorithms, and their interworking.
The problem finding K-Nearest Neighbors (KNN) is one of the major topic in data management. In transient networks (especially mobile sensor networks), energy conservation should be done along with query processing. Current KNN algorithms require certain kind of indexing support. This index could be either a centralized spatial index or an in-network data structure that is distributed over the sensor nodes. Creation and maintenance of these index structures, to reflect the network dynamics due to sensor node mobility, may result in long query response time and low battery efficiency, thus limiting their practical use. In Chapter 2, we propose a novel algorithm called Density-aware Itinerary KNN query processing (DIKNN) that is more suitable for transient networks. DIKNN avoids the cost of index maintenance by combining the query dissemination and response collection in an itinerary.
In Chapter 3, we look down to MAC layer to study the energy conservation issue in transient networks. Although Quorum-based Power Saving (QPS) protocols have been proposed for ad hoc networks (e.g., IEEE 802.11 ad hoc mode) to increase energy efficiency and prolong the operational time of mobile stations, these protocols assign to each station a cycle pattern that specifies when the station should wake up (to transmit/receive data) and sleep (to save battery power). In all existing QPS protocols, the cycle length is either identical for all stations or is restricted to certain numbers (e.g. squares or primes). These restrictions on cycle length limit the practical use of QPS protocols in transient networks as each individual station may want to select a cycle length that is best suited for its own need (in terms of remaining battery power, tolerable packet delay, and drop ratio). We propose the notion of Hyper Quorum System (HQS)---a generalization of QPS that allows for arbitrary cycle lengths, and therefore tailorable energy conservation effect on each station.
Based on the results in Chapters 2 and 3, in Chapter 4 we investigate how query processing algorithms at Application layer and energy conservation protocols at MAC layer can interwork with each other. We take in to account the clustering techniques at Network layer, which is common in Mobile Ad Hoc Networks (MANETs) to ensure the scalability and efficiency of various communication protocols, and propose an Asymmetric Cyclic Quorum (ACQ) system that is able to give further energy conservation by letting the MAC acknowledge the requirements from upper layers. In Chapter 5, we further extend the concept of ACQ to the vehicular networks and propose DSRC-AA that is suitable for highly dynamic networks requiring very short packet transmission delay.
We conduct extensive simulations over our studies. Simulation results show that DIKNN, ACQ/DSRC-AA, and HQS can achieve significant improvement in energy conservation at Application, Network, and MAC layers respectively in handling queries while preserving user-tolerable latency and query result accuracy.
1 Introduction 1
1.1 Motivation and Overview of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Algorithm Perspective: Itinerary-basedQueryProcessing . . . . . . . . . . . . 2
1.1.2 ProtocolPerspective: AdaptivePowerManagement . . . . . . . . . . . . . . . 3
1.1.3 Cross-Layer Design: Topology- & User Requirement-Aware Adaptation . . . . 4
1.1.4 Application to Vehicular Networks . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Algorithm Perspective: Itinerary-based Query Processing 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 DesignofDIKNN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.1 Definitions and Network Model . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.2 ExecutionPhases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.3 Itinerary-Based Query Dissemination . . . . . . . . . . . . . . . . . . . . . . 14
2.4 KNN Boundary Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4.1 RoutingPhase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4.2 Linear KNNB Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4.3 Interaction with Environments . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5 Optimal Concurrent Dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.6.1 Settings and Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . 31
2.6.2 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.6.3 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.6.4 Impact of Query Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.6.5 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.6.6 Impact of Network Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.6.7 Impact of Node Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3 Protocol Perspective: Adaptive Power Management 40
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.1 IEEE Power Saving Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.2 Quorum-based Power Saving Protocols . . . . . . . . . . . . . . . . . . . . . 44
3.3 The Hyper Quorum System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.3.1 Definitions and Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.3.2 Constructing Schemes for HQS . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.3.3 The HQS-based Power Management . . . . . . . . . . . . . . . . . . . . . . . 54
3.4 The Optimal Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.5.1 Theoretical Analysis: the QuorumRatio . . . . . . . . . . . . . . . . . . . . . 64
3.5.2 Link Discovery Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.5.3 Energy Conservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.5.4 Delay and Delay Drop Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4 Cross-Layer Design: Topology- & User Requirement-Aware Adaptation 69
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.2.1 Clustering in Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.2.2 Quorum-based Power-Saving Protocols . . . . . . . . . . . . . . . . . . . . . 72
4.3 Asymmetric Cyclic Quorum System . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.3.1 Definitions and Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.3.2 Constructing Scheme for ACQ . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.4.1 QuorumRatio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.4.2 Energy Conservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.4.3 AverageDelay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.4.4 Neighbor Discovery Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5 Application to Vehicular Networks 85
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.2.1 Architecture of Vehicular Networks . . . . . . . . . . . . . . . . . . . . . . . 88
5.2.2 IEEE 802.11 PS mode and AQPS Protocols . . . . . . . . . . . . . . . . . . . 90
5.3 Theoretical Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.3.1 Asymmetric Quorum System . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.3.2 The Majority-based ConstructingScheme . . . . . . . . . . . . . . . . . . . . 97
5.4 The DSRC-AA Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.4.1 ProtocolDesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.4.2 Performance Guarantee in AsynchronousVehicularNetworks . . . . . . . . . 103
5.4.3 Adaptive DSRC-AA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.5 PerformanceEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.5.1 DutyCycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.5.2 Neighbor Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.5.3 EffectofMobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.5.4 DelayDropRatio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.5.5 Effect of TrafficLoad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
6 Conclusions 114
7 Bibliography 116
[1] The network simulator. http://www.isi.edu/nsnam/ns.
[2] Caribou population distribution in gros morne national park greater ecosystem.
http://www.pc.gc.ca/apprendre-learn/prof/sub/eco/itm5/fi-lr6/caribou_E.asp, 2003.
[3] I. S. 802.11-1997. IEEE Standard for Wireless LAN Medium Access Control (MAC) and Physical
Layer (PHY) Specifications, 1997.
[4] I. S. 802.15.4-2003. Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications
for Low Rate Wireless Personal Area Networks, 2003.
[5] A. Amis and R. Prakash. Load-balancing clusters in wireless ad hoc networks. In Proc. of ASSET,
pages 25–32, 2000.
[6] M. Anand, E. Nightingale, and J. Flinn. Self-tuning wireless network power management. Wireless
Networks, 11(4):451–469, 2005.
[7] D. Audino, F. Baronti, R. Roncella, and R. Saletti. Wireless audio communication network for invehicle
access of infotainment services in motorcycles. In Proc. of Personal, Indoor and Mobile
Radio Communications Symp., pages 1–5, 2006.
[8] P. Basu, N. Khan, and T. Little. A mobility based metric for clustering in mobile ad hoc networks.
In Proc. of ICDCSW, pages 413–418, 2001.
[9] M. Bawa, A. Gionis, H.G.-Molina, and R.Motwani. The price of validity in dynamic networks.
In Proc. of SIGMOD, 2004.
[10] P. Bernstein, V. Hadzilacos, and N. Goodman. Concurrency Control and Recovery in Database
Systems. Addison-Wesley, 1987.
[11] T. BGandhi and M. Trivedi. Pedestrian protection systems: Issues, survey, and challenges. IEEE
Transactions on Intelligent Transportation Systems, 8(3):413–430, 2007.
[12] G. Bianchi. Performance analysis of the ieee 802.11 distributed coordination function. IEEE
JSAC, 18(3), 2000.
[13] S. Biswas, R. Tatchikou, and F. Dion. Vehicle-to-vehicle wireless communication protocols for
enhancing highway traffic safety. IEEE Communications Magazine, 44(1):74–82, 2006.
[14] J. Blum, A. Eskandarian, and L. Hoffman. Mobility management in ivc networks. In Proc. of
Intelligent Vehicles Symp., pages 150–155, 2003.
[15] J. Blum, A. Eskandarian, and L. Hoffman. Challenges of intervehicle ad hoc networks. IEEE
Transactions on Intelligent Transportation Systems, 5(4):347–351, 2004.
[16] C. Chao, J. Sheu, and I. Chou. An adaptive quorum-based energy conserving protocol for ieee
802.11 ad hoc networks. IEEE Transactions on Mobile Computing, 5(5):560–570, 2006.
[17] B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris. Span: An energy-efficient coordination
algorithm for topology maintenance in ad hoc wireless networks. Wireless Networks, 8(5):481–
494, 2002.
[18] W. Chen and S. Cai. Ad hoc peer-to-peer network architecture for vehicle safety communications.
IEEE Communications Magazine, 43(4):100–107, 2005.
[19] W. Chen, N. Jain, and S. Singh. Anmp: Ad hoc network management protocol. IEEE Journal on
Selected Areas in Communications, 17(8):1506–1531, 1999.
[20] R. Cheng, B. Kao, S. Prabhakar, A. Kwan, and Y. Tu. Adaptive stream filters for entity-based
queries with non-value tolerance. In Proc. of VLDB, 2005.
[21] Z. Chou. A randomized power management protocol with dynamic listen interval for wireless
ad hoc networks. In Proc. of Vehicular Technology Conference (VTC-Spring), pages 1251–1255,
2006.
[22] Z. Chou. Optimal adaptive power management protocols for asynchronous wireless ad hoc networks.
In Proc. of WCNC, pages 61–65, 2007.
[23] D. Collins. Carrier Grade Voice Over IP. McGraw-Hill, 2003.
[24] E. Commission. Prevent. http://www.prevent-ip.org, 2007.
[25] I. . L. S. Committee. Wireless LAN Medium Access Control and Physical Layer Specifications,
1999.
[26] M. Demirbas and H. Ferhatosmanoglu. Peer-to-peer spatial queries in sensor networks. In Proc.
of ICP2PC, 2003.
[27] A. S. E2213-03. Standard specification for telecommunications and information exchange between
roadside and vehicle systemsa˛X5 GHz band dedicated short range communications (DSRC)
medium access control (MAC) and physical layer (PHY) specifications, 2003.
[28] L. Feeney and M. Nilsson. Investigating the energy consumption of a wireless network interface
in an ad hoc networking environment. In Proc. of INFOCOM, pages 1548–1557, 2001.
[29] H. Ferhatosmanoglu, E. Tuncel, D. Agrawal, and A. Abbadi. Approximate nearest neighbor
searching in multimedia databases. In Proc. of ICDE, 2001.
[30] D. Ganesan, S. Ratnasamy, H. Wang, and D. Estrin. Coping with irregular spatio-temporal sampling
in sensor networks. ACM SIGCOMM Computer Communication Review, 34(1), 2004.
[31] I. . W. Group. Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)
Specifications, 2006.
[32] C. Gui and P. Mohapatra. Virtual patrol: A new power conservation design for surveillance using
sensor networks. In Proc. of IPSN, 2005.
[33] P. Gupta and P. Kumar. The capacity of wireless networks. IEEE Transactions on Information
Theory, 46(2):388–404, 2000.
[34] A. Guttman. R-trees: A dynamic index structure for spatial searching. In Proc. of SIGMOD,
1984.
[35] A. A. H.D. Chon, D. Agrawal. Range and knn query processing for moving objects in grid model.
Mobile Networks and Applications, 8(4), 2003.
[36] G. Hjaltason and H. Samet. Distance browsing in spatial databases. ACM TODS, 24(2), 1999.
[37] T. Hou and T. Tsai. An access-based clustering protocol for multihop wireless ad hoc networks.
IEEE Journal on Selected Areas in Communications, 19(7):1201–1210, 2001.
[38] I. Howitt and J. Gutierrez. Ieee 802.15.4 low rate - wireless personal area network coexistence
issues. Wireless Communications and Networking, 3(16-20), 2003.
[39] http://www.rammount.com/products/motorcycles.htm. RAM Motorcycle Mounts for Smartphone.
[40] L. Huang and T. Lai. On the scalability of ieee 802.11 ad hoc networks. In Proc. of MobiHoc,
pages 173–182, 2002.
[41] C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: A scalable and robust communication
paradigm for sensor networks. In Proc. of MOBICOM, 2000.
[42] A. Iwata,C.Chiang, G. Pei, M. Gerla, and T.Chen. Scalable routing strategies for ad hoc wireless
networks. IEEE Journal on Selected Areas in Communications, 17(8):1369–1379, 1999.
[43] J. Jiang, Y. Tseng, C. Hsu, and T. Lai. Quorum-based asynchronous power-saving protocols for
ieee 802.11 ad hoc networks. Mobile Networks and Applications, 10(1-2):169–181, 2005.
[44] P. Juang, H. Oki, Y.Wang, M. Martonosi, L. Peh, and D. Rubenstein. Energy-efficient computing
for wildlife tracking: Design tradeoffs and early experiences with zebranet. In Proc. of ASPLOSX,
2002.
[45] E. Jung and N. Vaidya. An energy efficient mac protocol for wireless lans. In Proc. of INFOCOM,
pages 1756–1764, 2002.
[46] E. Jung and N. Vaidya. An energy efficient mac protocol for wireless lans. In Proc. of INFOCOM,
2002.
[47] J. Kahn, R. Katz, and K. Pister. Next century challenges: Mobile networking for smart dust. In
Proc. of MOBICOM, 1999.
[48] B. Karp and H. Kung. Gpsr: Greedy perimeter stateless routing for wireless networks. In Proc.
of MOBICOM, 2000.
[49] Y. Kim, R. Govindan, B. Karp, and S. Shenker. On the pitfalls of geographic face routing. In
Proc. of DIALM, 2005.
[50] U. Kozat, G. Kondylis, B. Ryu, and M. Marina. Virtual dynamic backbone for mobile ad hoc
networks. In Proc. of ICC, pages 250–255, 2001.
[51] R. Krashinsky and H. Balakrishnan. Minimizing energy for wireless web access with bounded
slowdown. Wireless Networks, 11(1-2):135–148, 2005.
[52] R. Kravets and P. Krishnan. Application-driven power management for mobile communication.
Wireless Networks, 6(4):263–277, 2000.
[53] F. Kuhn, R. Wattenhofer, Y. Zhang, and A. Zollinger. Geometric ad-hoc routing: Of theory and
practice. In Proc. of PODC, 2003.
[54] F. Kuhn, R. Wattenhofer, and A. Zollinger. Worst-case optimal and average-case efficient geometric
ad-hoc routing. In Proc. of MobiHoc, 2003.
[55] W. Lee and B. Zheng. Dsi: A fully distributed spatial index for location-based wireless broadcast
services. In Proc. of ICDCS, 2005.
[56] G. Leen and D. Heffernan. Expanding automotive electronic systems. Computer, 35(1):88–93,
2002.
[57] C. Lin, G. Chiu, and C. Cho. A new quorum-based scheme for managing replicated data in
distributed systems. IEEE Transactions on Computers, 51(12):1442–1447, 2002.
[58] C. Lin andM. Gerla. Adaptive clustering for mobile wireless networks. IEEE Journal on Selected
Areas in Communications, 15(7):1265–1275, 1997.
[59] B. Liu, W. Lee, and D. Lee. Distributed caching of multi-dimensional data in mobile environments.
In Proc. of MDM, 2005.
[60] D. Logothetis, V. Mainkar, and K. Trivedi. Transient analysis of non-markovian queues via
markov regenerative processes. Probability and Statistics: AJ Medhi Festschrift, 1996.
[61] W. Luk and T. Wong. Two new quorum based algorithms for distributed mutual exclusion. In
Proc. of ICDCS, pages 100–106, 1997.
[62] A. Markopoulou, F. Tobagi, and M. Karam. Assessing the quality of voice communications over
internet backbones. IEEE/ACM Transactions on Networking, 11(5):747–760, 2003.
[63] A.McDonald and T. Znati. Design and performance of a distributed dynamic clustering algorithm
for ad-hoc networks. In Proc. of Annual Simulation Symp., pages 27–35, 2001.
[64] K. Medepalli and F. Tobagi. Towards performance modeling of ieee 802.11 based wireless networks:
A unified framework and its applications. In Proc. of INFOCOM, pages 1–12, 2006.
[65] M. Mokbel, X. Xiong, and W. Aref. Sina: Scalable incremental processing of continuous queries
in spatio-temporal databases. In Proc. of SIGMOD, 2004.
[66] D. Niculescu and B. Nath. Trajectory based forwarding and its applications. In Proc. of MOBICOM,
2003.
[67] F. of American Scientists. Remote battlefield sensor system (rembass). http://www.fas.org, 2000.
[68] U. D. of TraNsportation. Intelligent transportation system joint program office home.
http://www.its.dot.gov, 2006.
[69] U. D. of Transportation. Vehicle infrastructure integration (vii). http://www.its.dot.gov/vii, 2007.
[70] S. Patil, S. Das, and A. Nasipuri. Serial data fusion using space-filling curves in wireless sensor
networks. In Proc. of SECON, 2004.
[71] C. Perkins and E. Royer. Ad-hoc on-demand distance vector routing. In Proc. of WMCSA, 1999.
[72] R. Rajaraman. Topology control and routing in ad hoc networks: A survey. ACM SIGACT News,
33(2):66–73, 2002.
[73] H. Reumerman, M. Roggero, and M. Ruffini. The application-based clustering concept and requirements
for intervehicle networks. IEEE Communications Magazine, 43(4):108–113, 2005.
[74] N. Roussopoulos, S. Kelley, and F. Vincent. Nearest neighbor queries. In Proc. of SIGMOD,
1995.
[75] R. Santos, R. Edwards, and A. Edwards. Cluster-based location routing algorithm for inter-vehicle
communication. In Proc. of Vehicular Technology Conf., pages 914–918, 2004.
[76] J. Sheu, C. Chao, and C. Sun. A clock synchronization algorithm for multi-hop wireless ad hoc
networks. In Proc. of ICDCS, pages 574–581, 2004.
[77] P. Sinha, R. Sivakumar, and V. Bharghavan. Enhancing ad hoc routing with dynamic virtual
infrastructures. In Proc. of INFOCOM, pages 1763–1772, 2001.
[78] Z. Song and N. Roussopoulos. K-nearest neighbor search for moving query point. In Proc. of
SSTD, 2001.
[79] Y. Tseng, C. Hsu, and T. Hsieh. Power-saving protocols for ieee 802.11-based multi-hop ad hoc
networks. In Proc. of INFOCOM, pages 200–209, 2002.
[80] J. Winter and W. Lee. Kpt: A dynamic knn query processing algorithm for location-aware sensor
networks. In Proc. of DMSN, 2004.
[81] J.Winter, Y. Xu, andW. Lee. Energy efficient processing of k nearest neighbor queries in locationaware
sensor networks. In Proc. of MobiQuitous, 2005.
[82] J. Wu, F. Dai, and M. G. amd I. Stojmenovic. On calculating power-aware connected dominating
sets for efficient routing in ad hoc wireless networks. IEEE/KICS Journal of Communications and
Networks, 4(1):59–70, 2002.
[83] Y. Xu, W. Lee, J. Xu, and G. Mitchell. Processing window queries in wireless sensor networks.
In Proc. of ICDE, 2006.
[84] X. Yang, J. Liu, N. Viadya, and F. Zhao. A vehicle-to-vehicle communication protocol for cooperative
collision warning. In Proc. of MobiQuitous, pages 114–123, 2004.
[85] W. Ye, J. Heidemann, and D. Estrin. Medium access control with coordinated adaptive sleeping
for wireless sensor networks. IEEE/ACM Transactions on Networking, 12(3):493–506, 2004.
[86] O. Younis and S. Fahmy. Heed: A hybrid, energy-efficient, distributed clustering approach for ad
hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4):366–379, 2004.
[87] R. Zheng. Design, analysis, and empirical evaluation of power management in multihop wireless
networks. Technical Report UIUCDCS-R-2003-2381, 2003.
[88] R. Zheng, J. Hou, and L. Sha. Optimal block design for asynchronous wake-up schedules and its
applications in multihop wireless networks. IEEE Transactions on Mobile Computing, 5(9):1228–
1241, 2006.
[89] R. Zheng, J. Hou, and L. Sha. Performance analysis of power management policies in wireless
networks. IEEE Transactions on Wireless Communications, 5(6):1351–1361, 2006.
[90] R. Zheng and R. Kravets. On-demand power management for ad hoc networks. In Proc. of
INFOCOM, pages 481–491, 2003.
[91] J. Zhu and S. Roy. Mac for dedicated short range communications in intelligent transport system.
IEEE Communications Magazine, 41(12):60–67, 2003.
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