Paper List

Zigbee

1. Li-Hsing Yen and Wei-Ting Tsai, "The Room Shortage Problem of Tree-Based ZigBee/IEEE802.15.4 Wireless Networks," Computer Communications, October 2009.
   
   
2. Meng-Shiuan Pan, Chia-Hung Tsai, and Yu-Chee Tseng, "The Orphan Problem in ZigBee Wireless Networks," IEEE Transactions on Mobile Computing, Mar 2009.
   
   
3. Lain-Jinn Hwang, Shiann-Tsong Sheu, Yun-Yen Shih, and Yen-Chieh Cheng, "Grouping Strategy for Solving Hidden Node Problem in IEEE 802.15.4 LR-WPAN," Proceedings of the First International Conference on Wireless Internet, pp. 26-32, July 2005.
   
   
4. Anis Koubâa, André Cunha, Mário Alves, and Eduardo Tovar, "TDBS: a time division beacon scheduling mechanism for ZigBee cluster-tree wireless sensor networks," Real-Time Systems, vol. 40, pp. 321-354, October 2008.
   
   
5. Lun-Wu Yeh, Meng-Shiuan Pan, Yu-Chee Tseng, "Two-Way Beacon Scheduling in ZigBee Tree-BasedWireless Sensor Networks," IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, pp. 130-137, June 2008.
   
   
6. M.-S. Pan, H.-W. Fang, Y.-C. Liu, and Y.-C. Tseng, "Address Assignment and Routing Schemes for ZigBee-Based Long-Thin Wireless Sensor Networks," IEEE Vehicular Technology Conference, pp. 173-177, 2008.
   
   
7. Jin-Woo Kim, Jihoon Kim, and Doo-Seop Eom, "Multi-Dimensional Channel Management Scheme to Avoid Beacon Collision in LR-WPAN," IEEE Transactions on Consumer Electronics, vol. 54, pp. 396-404, 2008.
   
   
8. Bing Han and Gwendal Simon, "Optimizing Multi-hop Queries in ZigBee Based Multi-sink Sensor Networks," Lecture Notes In Computer Science, Vol. 5408, pp. 294-305, 2009.
   

Wireless Sensor Network

1. Bing-Hong Liu, Wei-Chieh Ke, Chin-Hsien Tsai, and Ming-Jer Tsai, "Constructing a Message-Pruning Tree with Minimum Cost for Tracking Moving Objects in Wireless Sensor Networks is NP-Complete and an Enhanced Data Aggregation Structure," IEEE Transactions on Computers, vol. 57, pp. 849-863, June 2008.
   
   
2. Mário Macedo, António Grilo, and Mário Nunes, "Distributed Latency-Energy Minimization and interference avoidance in TDMA Wireless Sensor Networks," Computer Networks: The International Journal of Computer and Telecommunications Networking, vol. 53, pp. 569-582, April 2009.
   
   
3. Bing Han, J. Leblet, and G. Simon, "Query range problem in wireless sensor networks," IEEE Communications Letters, vol. 13, pp. 55-57, January 2009.

IEEE 802.11 WLANs

1. Huazhi Gong, Kitae Nahm, and JongWon Kim, "Distributed Fair Access Point Selection for Multi-Rate IEEE 802.11 WLANs," 5th IEEE Consumer Communications and Networking Conference, pp. 528-532, January 2008.
   
   
2. Huazhi Gong and JongWon Kim, "Dynamic Load Balancing through Association Control of Mobile Users in WiFi Networks," IEEE Transactions on Consumer Electronics, Vol. 54, No. 2, MAY 2008.
   
   
3. [MBS08] Kimaya Mittala, Elizabeth M. Beldingb, and Subhash Suri, "A game-theoretic analysis of wireless access point selection by mobile users," January 2008.

Game Theory

1. [CMC08] M. Cesana, I. Malanchini, and A. Capone, "Modelling Network Selection and Resource Allocation in Wireless Access Networks with Non-Cooperative Games," 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, pp. 404-409, October 2008.
   
   
2. M. Cesana, N. Gatti, and I. Malanchini, "Game Theoretic Analysis of Wireless Access Network Selection: Models, Inefficiency Bounds, and Algorithms," International Conference On Performance Evaluation Methodologies And Tools & Workshops, 2008.
   
   
3. [JPW08] Libin Jiang, S. Parekh, and J. Walrand, "Base Station Association Game in Multi-cell Wireless Networks," IEEE Wireless Communications and Networking Conference, pp. 1616-1621, March 2008.
   
   
4. S. Shakkottai, E. Altman, and A. Kumar, "Multihoming of Users to Access Points in WLANs: A Population Game Perspective," Multihoming of Users to Access Points in WLANs: A Population Game Perspective, pp. 1207-1215, October 2007.
   
   
5. [Ros73] R. W. Rosenthal, "A Class of Games Possessing Pure-Strategy Nash Equilibria," International Journal of Game Theory, vol. 2, no. 1, pp. 65-67, December 1973.
   
   
6. [Mil96] I. Milchtaich, "Congestion Games with Player-Specific Payoff Functions," Games and Economic Behavior, vol. 13, pp. 111-124, March 1996.
   
   
7. D. Niyato, and E. Hossain, “Dynamics of Network Selection in Heterogeneous Wireless Networks: An Evolutionary Game Approach,” IEEE Trans. Vehicular Technology, vol.58, no.4, pp.2008-2017, May 2009.
   
   
8. Elias Koutsoupias and Christos Papadimitriou, "Worst-case equilibria," Computer Science Review, vol. 3, Issue 2, pp. 65-69, May 2009.
   
   
9. [MS96] D. Monderer and L. S. Shapley, "Potential Games," Games and Economic Behavior 14, pp. 124–143, 1996.

Coverage Game or Problem

1. [ZM09]M. Zhu and S. Mart´ınez, "Distributed coverage games for mobile visual sensors (I): Reaching the set of Nash equilibria," Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, pp. 169-174, December 2009.
   
   
2. [CTL+05]M. Cardei, M. T. Thai, Y. Li, and W. Wu, "Energy-Efficient Target Coverage in Wireless Sensor Networks," IEEE Infocom, 2005, pp.1976–1984.
   
   
3. [LG08]Y. Li and S. Gao, "Designing k-Coverage Schedules in Wireless Sensor Networks," Journal of Combinatorial Optimization, vol. 15, pp. 127–146, 2008.
   
   
4. [AST08]X. Ai, V. Srinivasan and C. Tham, "Optimality and Complexity of Pure Nash Equilibria in the Coverage Game," IEEE Journal on Selected Areas in Communications, vol. 26, no. 7, pp. 1170 - 1182, September 2008.
   
   
5. [CD05]M. Cardei and D. Du, "ImprovingWireless Sensor Network Lifetime through Power Aware Organization," Wireless Networks, vol. 11, no. 3, pp. 333-340, May 2005.
   
   
6. [CP09]M. Chaudhary and A. K. Pujari, "Q-Coverage Problem in Wireless Sensor Networks," Proceedings of the 10th International Conference on Distributed Computing and Networking, Vol. 5408, pp. 325 - 330 , 2009.

[Mil96] Congestion Games with Player-Specific Payoff Functions

這篇論文明確說明了[Ros73]所提出的是congestion game，以及congestion game of symmetric case (即是[Ros73]所提出的)中potential function的存在。它還提出了Nonsymmetric congestion game (就是論文題目所述的game)，說明了這個game和finite improvement property、Nash equilibrium的關係。另外還提出了Weighted congestion game。

[Ros73] A Class of Games Possessing Pure-Strategy Nash Equilibria

這篇論文定義了一種類型的game (其實就是congestion game，但論文中並沒有提到這個名詞)，並證明了這種game會存在pure-strategy Nash equilibrium，在證明過程中有使用到potential function，但是在這時potential game還未被定義，往後很多與game有關的文章都有引用這篇。

[MS96] Potential Games

這篇論文明確地定義了何謂potential game，以及說明了potential game的特性，potential game可分為ordinal potential game、w-potential game和exact potential game三種。它還說明了potential game與equilibrium、finite improvement path、close path、congestion game的關係。每個congestion game都可以找到它的potential function，所以每個congestion game都屬於potential game。

[ZM09] Distributed coverage games for mobile visual sensors (I): Reaching the set of Nash equilibria

這篇論文以game theory來model一個mobile visual sensor的coverage問題。假設有一塊區域，區域以正方形格子狀切割，sensor只能分布在格子中心點，sensor的可偵測區域是一個類似扇形的區域，sensor可以改變它的位置、旋轉角度和焦距來調整它的可偵測區域。要如何增加整體sensor的可偵測區域就是這篇論文所探討的問題，他們提出了restricted game來model此問題，並提出了inhomogeneous synchronous learning (ISL) algorithm來解這個game，最後證明了ISL algorithm可以收斂達到Nash equilibrium。

[JPW08] Base Station Association Game in Multi-cell Wireless Networks

主旨：
　　在無線網路中有複數個Base stations (BS's)可供選擇是相當常見的，如果只根據某些因素來選擇BS (e.g. data rate, signal strength)，可能會導致相當差的system performance。在這篇論文中，提出了一種分散式使用者自私的BS選擇方法，並且嘗試取得較好的系統效能。

問題：
　　假設使用者的數量是非常多而且連續的，要找出如何讓使用者選擇BS，並且使system performance (一個Total utility function，作者自行定義) 達到最高。

解法：
　　作者把鄰近physical location的users視為同一個class，相同class的users將擁有相同的data rate vector，並且提出了兩種BS的scheduling policies，分別是Equal-time allocation by the BS和Equal-throughput allocation by the BS，不同的scheduling policies將導致完全不同的結果，作者證明了若在Equal-time allocation by the BS的情況下，將可以達到optimal system performance。

[CMC08] Modelling Network Selection and Resource Allocation in Wireless Access Networks with Non-Cooperative Games

主旨：
　　傳統802.11 WLANs的AP選擇機制是利用AP的RSSI值來決定(在此篇稱為選擇AN問題)，有throughput unfairness, poor overall performance, and performance anomaly...等問題，本篇以一個non-cooperative games來modle此問題。

問題：
　　傳統802.11 WLANs的AP選擇機制是user選擇一個它所收到AP的RSSI值最大的AP去連接，如此一來，user就會很容易集中連接到某幾台AP，使得AP的Load不均，AP無法負荷，user的throughput也會因此下降。

解法：
　　使用雙層的non-cooperative games來解此問題，先以Interference Based Network Selection Game (INSG) 跑出結果，把INSG中的Nash Equilibrium代入Network Selection and Resource Allocation Game (NSRAG)，再跑出最終結果。