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研究生:柯君翰
研究生(外文):Chun-Han Ko
論文名稱:物聯網資訊採集與群組多播的機制設計
論文名稱(外文):Mechanism Design for IoT Information Gathering and Multicast Distribution
指導教授:魏宏宇魏宏宇引用關係
指導教授(外文):Hung-Yu Wei
口試日期:2017-06-29
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:電信工程學研究所
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:2017
畢業學年度:105
語文別:英文
論文頁數:126
中文關鍵詞:物聯網能量採集無線電資源分配機制設計賽局理論
外文關鍵詞:The IoTenergy harvestingradio resource allocationmechanism designgame theory
相關次數:
  • 被引用被引用:1
  • 點閱點閱:253
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  • 收藏至我的研究室書目清單書目收藏:0
物聯網是智能設備、車輛、機器還有其他智能物體等互相聯通而形成的網路。實現複雜物聯網的關鍵因素則在於許多新型通信協定和技術的整合,它們將使物聯網設備能夠進行協作、溝通並作出決策。物聯網設備的互相聯通作用可以簡單的資料流說明:諸如無線感應器的物聯網設備從環境中收集資料,並將資料傳輸到資料伺服器進行信息處理。爾後伺服器會將處理獲得的信息傳輸分送到諸如用戶設備的物聯網設備以實現更好的使用者效用。由於物聯網的快速發展,無處不在的信息收集、處理和分送,均使無線電資源的需求不斷上升,這也使原本就有限的無線電資源更為稀少。此外,無線傳輸通道會隨著時間波動。而能量採集技術雖能實現自我維持運作的物聯網設備,但採集的能量通常是間歇並隨時間變化的。因此,考量到無線電資源的稀少性、無線通道的時間波動和能量的時間變化,如何有效地在空間和時間上分配運用無線電資源將是物聯網中極為重要的一項議題。此外,我們也考慮了物聯網設備的自私特性與物聯網環境的不完整信息。不完整信息意味著網路環境中如無線通道狀況與設備的能量儲存量等信息通常僅為設備本身所知。為了最佳化網路效能,網路設計者將需要設備回饋這些不完整信息。由於整體網路最佳化通常會犧牲部分設備的個別效能,因此自私的設備可能會通過虛假的信息回饋來操縱網路最佳化結果,以使自己的效能表現變得更好。此時整體網路效能表現將可能不是網路設計者所希望達到的最佳結果。有鑑於此,我們在物聯網中制定了無線電資源分配問題,並以機制設計的概念提出了創新的物聯網資源分配機制。透過理論研究,我們證明設計的資源配置機制將可使物聯網設備回饋真實的信息,並從而實現最佳的均衡資源分配。均衡資源分配實現了數個效率和公平的度量,包括最大系統吞吐量/環境資料保真度,柏拉圖效率,最大公平性,與比例公平性。而透過提出的定價方案,我們證明均由設備付錢給機制。換言之,我們設計的機制將不需要付錢給設備才能確保真實的反饋(預算平衡)。此外,我們也證明所有設備將加入設計的機制以獲得比沒有加入時更高的效用(個別理性)。
The Internet of Things (IoT) is the inter-networking of smart devices, vehicles, machines, and other items. The key factor for enabling this sophisticated paradigm is the integration of novel communication protocols and technologies, which allows IoT devices to cooperate, communicate, and make decisions. The interconnection of IoT devices can be explained in simple data flows: Devices such as wireless sensors gather data from the environment and transmit the data to data servers for information processing. The processed information is then distributed to devices such as user equipments to achieve greater user utility. Due to the rapid development of the IoT, ubiquitous information gathering, processing, and distribution have escalated the demand of radio resources, which makes radio resources even scarcer. In addition, wireless channels are time-fluctuating. Energy harvesting technology enables self-sustainable IoT devices but harvested energy is usually intermittent and time-varying. Therefore, taking into account radio resource scarcity, channel fluctuations, and energy variations, efficient radio resource allocation in space and time is particularly important in the IoT. Moreover, we also consider selfishness of IoT devices and incomplete information of the network environment. Incomplete information means that the network environment, such as channel conditions and energy levels, is only known to devices themselves. To optimize the network performance, feedback of the incomplete information from devices is required. Note that overall network optimization usually sacrifices individual performance of some devices. Selfish devices can manipulate the network optimization result through untruthful feedback if doing so increase their own performance. The network performance may not be optimal. In this regard, we formulate radio resource allocation problems in the IoT and adopt a mechanism design approach to propose novel IoT resource allocation mechanisms. Our theoretic findings show that the proposed resource allocation mechanisms can induce truthful information feedback from devices so as to achieve optimal equilibrium resource allocation. The equilibrium resource allocation achieves several efficiency and fairness metrics, including maximum system throughput/data fidelity, Pareto efficiency, max-min fairness, proportional fairness. With the proposed pricing schemes, the payment is always made from the devices to the mechanisms. In other words, the mechanisms do not need to pay to ensure truthful feedback (budget balance). Moreover, all devices will join the proposed mechanisms to gain higher utility than without joining (individual rationality).
Contents
Doctoral Dissertation Certification by Oral Defense Committee i
Acknowledgements ii
Chinese Abstract iii
Abstract v
1 Introduction 1
1.1 Selfish Devices and Incomplete Information 2
1.2 Problem Overviews and Contributions 3
1.2.1 Strategy-Proof Resource Allocation Mechanism for Multi-Flow Wireless Multicast (Chapter 2) 3
1.2.2 Wireless Multicast over Time-Varying Channels for Energy-Harvesting Devices (Chapter 3) 4
1.2.3 On Maximizing Data Fidelity in Energy-Harvesting IoT: A Mechanism Design Approach (Chapter 4) 5
1.3 Mechanism Design 6
1.3.1 Basic Knowledge 6
1.3.2 Equilibrium Concepts 6
1.3.3 Infinite Time Horizon Mechanisms 7
2 Strategy-Proof Resource Allocation Mechanism for Multi-Flow Wireless Multicast 9
2.1 Introduction 9
2.2 Related Work 12
2.3 Multicast System Model 14
2.3.1 Multicast Flows 14
2.3.2 Lossy Channels and Generalized Erasure Coding 15
2.3.3 UEs’ Utility 16
2.4 Resource Allocation Problem and Mechanism Design 17
2.4.1 Pricing and Access Control 19
2.4.2 UE Resource Demands and Group Resource Demands 20
2.4.3 Group Weights 22
2.4.4 Weighted Water-Filling Resource Allocation 23
2.5 Resource Allocation Game 26
2.6 Strategy-Proofness – Truth-Revealing Equilibrium 28
2.7 Desirable Properties of the Equilibrium Resource Allocation 34
2.8 Numerical Simulation Results 38
2.8.1 Numerical Analysis 38
2.8.2 3GPP MBMS Simulation 41
2.9 Summary 42
3 Wireless Multicast over Time-Varying Channels for Energy-Harvesting Devices 44
3.1 Introduction 44
3.2 Related Works 46
3.3 System Model 48
3.3.1 Types of Multicast Flows 49
3.3.2 Energy Arrivals and Markov Energy States 50
3.3.3 Fading Channels and Markov Channel States 51
3.3.4 Joint States and Information Assumptions 52
3.3.5 One-Shot and Long-Term Throughput 52
3.4 Resource Allocation Problem and Mechanism Design 54
3.4.1 Feedback Strategies of the Devices 55
3.4.2 Flow Resource Allocation Policy 56
3.4.3 Bayesian Update of the State Information 57
3.4.4 Device Pricing Policy 59
3.4.5 Ex-Post Utility of the Devices 60
3.5 Resource Allocation Game 60
3.6 Truth-Revealing Equilibrium and Desirable Properties 62
3.7 Value Iteration for the Resource Allocation Policy 66
3.8 Further Discussions 68
3.8.1 Feedback per n Time Slots 68
3.8.2 Markov Data Sources 69
3.9 Numerical Results 69
3.10 Summary 73
4 On Maximizing Data Fidelity in Energy-Harvesting IoT: A Mechanism Design Approach 75
4.1 Introduction 75
4.2 Related Works 77
4.3 System Model 80
4.3.1 Data Statistics and Entropy 81
4.3.2 Energy States and Activation Intervals 82
4.3.3 Information Assumption on the Data Statistics, Energy States, and Activation Intervals 84
4.3.4 Data Fidelity 84
4.4 Node Activation Mechanism 85
4.4.1 Feedback Strategies of the Servers 87
4.4.2 Node Activation Policy and Value Iteration 87
4.4.3 Update of the Energy State Information 89
4.4.4 Server Pricing Policy 90
4.4.5 Utility of the Servers 91
4.5 Node Activation Game and Solution Concept 92
4.6 Truth-Revealing Equilibrium and Desirable Properties 93
4.7 Further Discussions 98
4.7.1 Synchronous Feedback per n Time Slots 99
4.7.2 Asynchronous Group Feedback 100
4.7.3 Stationary, Markov Energy Arrivals and Time-Varying Channels 101
4.8 Numerical Results 103
4.8.1 Parameter Setting 103
4.8.2 Verification of Incentive Compatibility, Budget Balance, and Individual Rationality 103
4.8.3 Impacts of Parameter Change 105
4.8.4 Performance Comparison of Synchronous and Asynchronous Feedback Designs 106
4.9 Summary 107
5 Conclusions 108
A Proofs for Lemmas 2.1, 2.3, and 2.4 110
A.1 Lemma 2.1 110
A.2 Lemma 2.3 113
A.3 Lemma 2.4 115
Bibliography 118
[1] L. Atzori, A. Iera, and G. Morabito. The internet of things: A survey. Computer networks, 54(15):2787–2805, 2010.
[2] A. Whitmore, A. Agarwal, and L. Da Xu. The internet of things-a survey of topics and trends. Information Systems Frontiers, 17(2):261–274, 2015.
[3] S. Priya and D. J. Inman. Energy Harvesting Technologies, volume 21. Springer, 2009.
[4] U. Varshney. Multicast over wireless networks. Communications of the ACM, 45(12):31–37, 2002.
[5] 3GPP TS 26.346 V12.0.0 3rd Generation Partnership Project. Technical specification group services and system aspects: Multimedia broadcast/multicast service (mbms) protocols and codecs. December 2013.
[6] IETF RFC 6330. Raptorq forward error correction scheme for object delivery. 2011.
[7] 3GPP TR 26.848 V1.0.0 3rd Generation Partnership Project. Technical specification group services and system aspects: Multimedia broadcast/multicast service (mbms) enhanced mbms operation. March 2014.
[8] 3GPP TR 26.849 3rd Generation Partnership Project. Technical specification group services and system aspects: Mbms improvements mbms operation on demand. March 2014.
[9] Y. Liang, C. Chou, and H. Wei. Multi-group wireless multicast broadcast services using adaptive modulation and coding: Modeling and analysis. In 2010 IEEE 71st Vehicular Technology Conference (VTC 2010-Spring), pages 1–5, May 2010.
[10] F.-Y. Tsuo, J.-P. Huang, C.-H. Ko, and H.-Y. Wei. Incentive compatible configuration for wireless multicast: A game theoretic approach. IEEE Transactions on Vehicular Technology, 60(7):3520–3525, Sept 2011.
[11] C.-Y. Wang, Y. Chen, H.-Y. Wei, and K. J. R. Liu. Scalable video multicasting: A stochastic game approach with optimal pricing. IEEE Transactions on Wireless Communications, 14(5):2353–2367, May 2015.
[12] W. Tu. Efficient resource utilization for multi-flow wireless multicasting transmissions. IEEE Journal on Selected Areas in Communications, 30(7):1246 –1258, August 2012.
[13] K. Jain, J. Padhye, V. N. Padmanabhan, and L. Qiu. Impact of interference on multihop wireless network performance. ACM/Springer Wireless Networks 11:471487, 2005.
[14] M. Kodialam and T. Nandagopal. Characterizing achievable rates in multi-hop wireless networks: The joint routing and scheduling problem. In ACM MobiCom, 2003.
[15] M. Kodialam and T. Nandagopal. Characterizing the capacity region in multi-radio multi-channel wireless mesh networks. In ACM MobiCom, 2005.
[16] P. Wan. Multiflows in multihop wireless networks. In ACM Mobihoc, 2009.
[17] P. Wan, Y. Cheng, Z. Wang, and F. Yao. Multiflows in multi-channel multi-radio multihop wireless networks. In Proceedings IEEE INFOCOM, pages 846–854, 2011.
[18] V.S.A. Kumar, M.V. Marathe, S. Parthasarathy, and A. Srinivasan. Algorithmic aspects of capacity in wireless networks. In SIGMETRICS Perform. Eval. Rev. 33(1):133V144, 2005.
[19] A. E. Ozdaglar and D. P. Bertsekas. Optimal solution of integer multicommodity flow problems with application in optical networks. In Symposium on Global Optimization, 2003.
[20] R. Cruz and A. Santhanam. Optimal routing, link scheduling and power control in multi-hop wireless networks. In IEEE Infocom, 2003.
[21] G. B. Middleton, B. Aazhang, and J. Lilleberg. A flexible framework for polynomial time resource allocation in multiflow wireless networks. In the 47th Allerton Conference on Communication, Control and Computing, September 2009.
[22] G. B. Middleton, B. Aazhang, and J. Lilleberg. Efficient resource allocation and interference management for streaming multiflow wireless networks. In IEEE ICC, May 2010.
[23] M. Baghaie, D. S. Hochbaum, and B. Krishnamachari. On hardness of multiflow transmission in delay constrained cooperative wireless networks. In the 2011 IEEE Globecom, Houston, Texas, USA, 5-9 December 2011.
[24] W. Tu, C. Sreenan, C. Chou, A. Misra, and S. Jha. Resource-aware video multicasting via access gateways in wireless mesh networks. IEEE Transactions on Mobile Computing, 11:881–895, June 2012.
[25] A. Gopinathan, Z. Li, and B. Li. On achieving group strategyproof information dissemination in wireless networks. In International Conference on Game Theory for Networks, 2009. GameNets’ 09., pages 232–240. IEEE, 2009.
[26] A. Gopinathan, Z. Li, and B. Li. Group strategyproof multicast in wireless networks. IEEE Transactions on Parallel and Distributed Systems, 22(5):708–715, 2011.
[27] S. Jakubczak and D. Katabi. A cross-layer design for scalable mobile video. In Proceedings of the 17th annual international conference on Mobile computing and networking (MobiCom), pages 289–300. ACM, 2011.
[28] S. Jakubczak and D. Katabi. Softcast: one-size-fits-all wireless video. ACM SIGCOMM Computer Communication Review, 41(4):449–450, 2011.
[29] R. Baeza-Yates and B. Ribeiro-Neto. Modern information Retrieval, volume 463. ACM press New York, 1999.
[30] J. Blomer, M. Kalfane, R. Karp, M. Karpinski, M. Luby, and D. Zuckerman. An xor-based erasure-resilient coding scheme, 1995.
[31] W. K. Lin, D. M. Chiu, and Y. B. Lee. Erasure code replication revisited. In Proceedings of the Fourth International Conference on Peer-to-Peer Computing, 2004., pages 90–97, 2004.
[32] C.-H. Ko and H.-Y. Wei. On-demand resource-sharing mechanism design in two-tier ofdma femtocell networks. IEEE Transactions on Vehicular Technology, 60(3):1059–1071, March 2011.
[33] J. F. Nash. Equilibrium points in n-person games. In Proceedings of the National Academy of Sciences, volume 36, pages 48–49, 1950.
[34] D. C. Parkes. Combinatorial auctions. In Iterative Combinatorial Auctions, chapter 2. Cambridge, MA: MIT Press, 2001.
[35] C. Courcoubetis and R. Weber. Pricing Communication Networks: Economics, Technology and Modelling, chapter 10. New York: Wiley, 2003.
[36] B. Radunovic and J.-Y. Le Boudec. A unified framework for max-min and min-max fairness with applications. ACM/IEEE Transactions on Networking, 15(5):1073–1083, 2007.
[37] A. Detti, G. Bianchi, W. Kellerer, et al. SVEF: an open-source experimental evaluation framework. In In Proc. of IEEE MediaWIN 2009, Sousse, Tunisia, 2009.
[38] Joint scalable video model software.
[39] Foreman yuv video. ftp://ftp.tnt.uni-hannover.de/pub/svc/testsequences/.
[40] 3GPP. TR 36.814, evolved universal terrestrial radio access (e-utra); further advancements for e-utra physical layer aspects, Mar. 2010.
[41] S. Schwarz, J.C. Ikuno,M. Simko,M. Taranetz, Q.Wang, andM. Rupp. Pushing the limits of LTE: A survey on research enhancing the standard. IEEE Access, 1:51–62, 2013.
[42] M. Condoluci, G. Araniti, T. Mahmoodi, and M. Dohler. Enabling the iot machine age with 5g: Machine-type multicast services for innovative real-time applications. IEEE Access, 4:5555–5569, 2016.
[43] C.-H. Ko, C.-C. Chou, H.-Y. Meng, and H.-Y. Wei. Strategy-proof resource allocation mechanism for multi-flow wireless multicast. IEEE Transactions on Wireless Communications, 14(6):3143–3156, 2015.
[44] O. Ozel, K. Tutuncuoglu, J. Yang, S. Ulukus, and A. Yener. Transmission with energy harvesting nodes in fading wireless channels: Optimal policies. IEEE Journal on Selected Areas in Communications, 29(8):1732–1743, 2011.
[45] C. Keong Ho and R. Zhang. Optimal energy allocation for wireless communications with energy harvesting constraints. IEEE Transactions on Signal Processing, 60(9):4808–4818, 2012.
[46] H. Ju and R. Zhang. Throughput maximization in wireless powered communication networks. IEEE Transactions on Wireless Communications, 13(1):418–428, 2014.
[47] J. Yang, O. Ozel, and S. Ulukus. Broadcasting with an energy harvesting rechargeable transmitter. IEEE Transactions on Wireless Communications, 11(2):571–583, 2012.
[48] J. Xu and R. Zhang. Throughput optimal policies for energy harvesting wireless transmitters with non-ideal circuit pwer. IEEE Journal on Selected Areas in Communications, 32(2):322–332, 2014.
[49] S. Guo and O. WW Yang. Energy-aware multicasting in wireless ad hoc networks: A survey and discussion. Computer Communications, 30(9):2129–2148, 2007.
[50] S.-P. Chuah, Z. Chen, and Y.-P. Tan. Energy-efficient resource allocation and scheduling for multicast of scalable video over wireless networks. IEEE Transactions on Multimedia, 14(4):1324–1336, 2012.
[51] L. Al-Kanj and Z. Dawy. Energy-aware resource allocation in ofdma wireless multicasting networks. In 2012 19th International Conference on Telecommunications (ICT), pages 1–5. IEEE, 2012.
[52] C.-C. Kuan, G.-Y. Lin, H.-Y. Wei, and R. Vannithamby. Reliable multicast and broadcast mechanisms for energy-harvesting devices. IEEE Transactions on Vehicular Technology, 63(4):1813–1826, 2014.
[53] C. K. Ho, P. D. Khoa, and P. C. Ming. Markovian models for harvested energy in wireless communications. In 2010 IEEE International Conference on Communication Systems (ICCS), pages 311–315. IEEE, 2010.
[54] H. S. Wang and N. Moayeri. Finite-state markov channel-a useful model for radio communication channels. IEEE Transactions on Vehicular Technology, 44(1):163–171, 1995.
[55] Q. Zhang and S. A. Kassam. Finite-state markov model for rayleigh fading channels. IEEE Transactions on Communications, 47(11):1688–1692, 1999.
[56] M. A. Alsheikh, D. T. Hoang, D. Niyato, H.-P. Tan, and S. Lin. Markov decision processes with applications in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 17(3):1239–1267, 2015.
[57] T. R. Palfrey and S. Srivastava. On bayesian implementable allocations. The Review of Economic Studies, 54(2):193–208, 1987.
[58] W. Vickrey. Counterspeculation, auctions, and competitive sealed tenders. The Journal of Finance, 16(1):8–37, 1961.
[59] E. H. Clarke. Multipart pricing of public goods. Public Choice, 11(1):17–33, 1971.
[60] T. Groves. Incentives in teams. Econometrica: Journal of the Econometric Society, pages 617–631, 1973.
[61] D. Gillette. Stochastic games with zero stop probabilities. Contributions to the Theory of Games, 3:179–187, 1957.
[62] D. Fudenberg and D. Levine. Subgame-perfect equilibria of finite-and infinitehorizon games. Journal of Economic Theory, 31(2):251–268, 1983.
[63] D. Abreu. On the theory of infinitely repeated games with discounting. Econometrica: Journal of the Econometric Society, pages 383–396, 1988.
[64] E. Hendon, H. J. Jacobsen, and B. Sloth. The one-shot-deviation principle for sequential rationality. Games and Economic Behavior, 12(2):274–282, 1996.
[65] J. Pineau, G. Gordon, S. Thrun, et al. Point-based value iteration: An anytime algorithm for pomdps. In IJCAI, volume 3, pages 1025–1032, 2003.
[66] M. L. Littman, T. L. Dean, and L. P. Kaelbling. On the complexity of solving markov decision problems. In Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, pages 394–402. Morgan Kaufmann Publishers Inc., 1995.
[67] J.Wu, C. Yuen,M.Wang, and J. Chen. Content-aware concurrent multipath transfer for high-definition video streaming over heterogeneous wireless networks. IEEE Transactions on Parallel and Distributed Systems, 27(3):710–723, 2016.
[68] L.Mainetti, L. Patrono, and A. Vilei. Evolution of wireless sensor networks towards the internet of things: A survey. In 2011 19th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pages 1–6. IEEE, 2011.
[69] W. KG Seah, Z. A. Eu, and H.-P. Tan. Wireless sensor networks powered by ambient energy harvesting (wsn-heap)-survey and challenges. In 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology, 2009. Wireless VITAE 2009., pages 1–5. Ieee, 2009.
[70] F. K. Shaikh and S. Zeadally. Energy harvesting in wireless sensor networks: A comprehensive review. Renewable and Sustainable Energy Reviews, 55:1041–1054, 2016.
[71] V. Sharma, U. Mukherji, V. Joseph, and S. Gupta. Optimal energy management policies for energy harvesting sensor nodes. IEEE Transactions on Wireless Communications, 9(4):1326–1336, 2010.
[72] N. Michelusi, K. Stamatiou, and M. Zorzi. Transmission policies for energy harvesting sensors with time-correlated energy supply. IEEE Transactions on Communications, 61(7):2988–3001, 2013.
[73] M. Vuran, ¨O. Akan, and I. Akyildiz. Spatio-temporal correlation: Theory and applications for wireless sensor networks. Computer Networks, 45(3):245–259, 2004.
[74] C. Liu, K.Wu, and J. Pei. An energy-efficient data collection framework for wireless sensor networks by exploiting spatio-temporal correlation. IEEE Transactions on Parallel and Distributed Systems, 18(7), 2007.
[75] S. Yoon and C. Shahabi. The clustered aggregation (cag) technique leveraging spatial and temporal correlations in wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 3(1):3, 2007.
[76] C. Luo, F. Wu, J. Sun, and C. W. Chen. Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, pages 145–156. ACM, 2009.
[77] N. Roy, V. Rajamani, and C. Julien. Supporting multi-fidelity-aware concurrent applications in dynamic sensor networks. In 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pages 43–49. IEEE, 2010.
[78] J. Zheng, Y. Cai, X. Shen, Z. Zheng, and W. Yang. Green energy optimization in energy harvesting wireless sensor networks. IEEE Communications Magazine, 53(11):150–157, 2015.
[79] D. Zhang, Z. Chen,M. K. Awad, N. Zhang, H. Zhou, and X. S. Shen. Utility-optimal resource management and allocation algorithm for energy harvesting cognitive radio sensor networks. IEEE Journal on Selected Areas in Communications, 34(12):3552–3565, 2016.
[80] M.-J. Shih, G.-Y. Lin, and H.-Y. Wei. Two paradigms in cellular internet-of-things access for energy-harvesting machine-to-machine devices: push-based versus pull-based. IET Wireless Sensor Systems, 6(4):121–129, 2016.
[81] H.-H Lin, M.-J. Shih, H.-Y. Wei, and R. Vannithamby. Deepsleep: Ieee 802.11 enhancement for energy-harvesting machine-to-machine communications. Wireless Networks, 21(2):357–370, 2015.
[82] N. Cressie. Statistics for Spatial Data. John Wiley & Sons, 2015.
[83] A. Deshpande, C. Guestrin, S. R. Madden, J. M. Hellerstein, and W. Hong. Model-driven data acquisition in sensor networks. In Proceedings of the Thirtieth International Conference on Very Large Data Bases-Volume 30, pages 588–599. VLDB Endowment, 2004.
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