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Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

Published: 19 October 2023 Publication History

Abstract

We aim to preserve a large amount of data generated inside base station-less sensor networks (BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP: data preservation problem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit.
In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN.

References

[1]
G. Aathur, P. Desnoyers, D. Ganesan, and P. Shenoy. 2009. Ultra-low power data storage for sensor networks. ACM Trans. Sensor Netw. 5, 4 (2009), 33:1–33:34.
[2]
A. Abrardo, L. Balucanti, and A. Mecocci. 2013. A game theory distributed approach for energy optimization in WSNs. ACM Trans. Sensor Netw. 9, 4, Article 44 (July2013).
[3]
A. Agah, S. K. Das, and K. Basu. 2004. A game theory based approach for security in wireless sensor networks. In IEEE IPCCC’04. 259–263.
[4]
R. K. Ahuja, T. L. Magnanti, and J. B. Orlin. 1993. Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, Inc.
[5]
B. Alhakami, B. Tang, J. Han, and M. Beheshti. 2019. DAO-R: Integrating data aggregation and offloading in sensor networks via data replication. Int. J. Sensor Netw. 29, 2 (2019), 134–146.
[6]
T. AlSkaif, M. G. Zapata, and B. Bellalta. 2015. Game theory for energy efficiency in wireless sensor networks: Latest trends. J. Netw. Comput. 54 (2015), 33–61.
[7]
L. Anderegg and S. Eidenbenz. 2003. Ad hoc-VCG: A truthful and cost-efficient routing protocol for mobile ad hoc networks with selfish agents. In MobiCom’03. 245–259.
[8]
A. Asheralieva and D. Niyato. 2019. Game theory and Lyapunov optimization for cloud-based content delivery networks with device-to-device and UAV-enabled caching. IEEE Trans. Vehic. Technol. 68, 10 (2019), 10094–10110.
[9]
A. Attiah, M. Amjad, M. Chatterjee, and C. Zou. 2018. An evolutionary routing game for energy balance in wireless sensor networks. Comput. Netw. 138 (2018), 31–43.
[10]
S. Basagni, L. Boloni, P. Gjanci, C. Petrioli, C. A. Phillips, and D. Turgut. 2014. Maximizing the value of sensed information in underwater wireless sensor networks via an autonomous underwater vehicle. In INFOCOM’14.
[11]
D. E. Charilas and A. D. Panagopoulos. 2010. A survey on game theory applications in wireless networks. Comput. Netw. 54, 18 (Dec.2010), 3421–3430.
[12]
F. Chen and J. Kao. 2013. Game-based broadcast over reliable and unreliable wireless links in wireless multihop networks. IEEE Trans. Mob. Comput. 12, 8 (2013), 1613–1624.
[13]
J. Chen, Q. Yu, P. Cheng, Y. Sun, Y. Fan, and X. Shen. 2011. Game theoretical approach for channel allocation in wireless sensor and actuator networks. IEEE Trans. Automat. Contr. 56, 10 (2011), 2332–2344.
[14]
X. Chen. 2015. Decentralized computation offloading game for mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. 26, 4 (2015), 974–983.
[15]
Y. Chen and B. Tang. 2016. Data preservation in base station-less sensor networks: A game theoretic approach. In EAI GameNets’16.
[16]
Z. Chen, Y. Qiu, J. Liu, and L. Xu. 2011. Incentive mechanism for selfish nodes in wireless sensor networks based on evolutionary game. Comput. Math. Applic. 62, 9 (2011), 3378–3388.
[17]
E. H. Clarke. 1971. Multipart pricing of public goods. Pub. Choice 11, 1 (1971), 17–33.
[18]
T. Corman, C. Leiserson, R. Rivest, and C. Stei. 2009. Introduction to Algorithms. MIT Press.
[19]
N. Crary, B. Tang, and S. Taase. 2015. Data preservation in data-intensive sensor networks with spatial correlation. In MobiData’15.
[20]
D. DiPalantino and R. Johari. 2009. Traffic engineering vs. content distribution: A game theoretic perspective. In IEEE INFOCOM’09. 540–548.
[21]
M. Doudou, J. M. Barcelo-Ordinas, D. Djenouri, J. Garcia-Vidal, A. Bouabdallah, and N. Badache. 2016. Game theory framework for MAC parameter optimization in energy-delay constrained sensor networks. ACM Trans. Sen. Netw. 12, 2 (May2016).
[22]
L. Duan, T. Kubo, K. Sugiyama, J. Huang, T. Hasegawa, and J. Walrand. 2012. Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing. In IEEE INFOCOM’12.
[23]
S. Edwards, T. Murray, T. O’Farrell, I. C. Rutt, P. Loskot, I. Martin, N. Selmes, R. Aspey, T. James, S. L. Bevan, and T. Baugé. 2013. A high-resolution sensor network for monitoring glacier dynamics. IEEE Sensors J. 14, 11 (2013), 3926–3931.
[24]
S. Eidenbenz, G. Resta, and P. Santi. 2005. COMMIT: A sender-centric truthful and energy-efficient routing protocol for ad hoc networks with selfish nodes. In IEEE IPDPS’05.
[25]
M. Elhoseny, K. Shankar, and M. Abdel-Basset. 2020. Artificial Intelligence Techniques in IoT Sensor Networks. Chapman and Hall/CRC.
[26]
J. Feigenbaum, C. Papadimitriou, R. Sami, and S. Shenker. 2005. A BGP-based mechanism for lowest-cost routing. Distrib. Comput. 18, 1 (2005), 61–72.
[27]
J. Feigenbaum, C. Papadimitriou, and S. Shenker. 2001. Sharing the cost of multicast transmissions. J. Comput. Syst. Sci. (Special Issue Internet Algor.) 63 (2001), 21–41.
[28]
M. Felegyhazi, J. P. Hubaux, and L. Buttyan. 2006. Nash equilibria of packet forwarding strategies in wireless ad hoc networks. IEEE Trans. Mob. Comput. 5, 5 (2006), 463–476.
[29]
D. Fudenberg and J. Tirole. 1991. Game Theory. MIT Press.
[30]
R. Ghaffarivardavagh, S. S. Afzal, O. Rodriguez, and F. Adib. 2020. Ultra-wideband underwater backscatter via piezoelectric metamaterials. In ACM SIGCOMM’20.
[31]
S. M. Ghoreyshi, A. Shahrabi, and T. Boutaleb. 2018. An efficient AUV-aided data collection in underwater sensor networks. In IEEE AINA’18.
[32]
D. Glaroudis, A. Iossifides, and P. Chatzimisios. 2020. Survey, comparison and research challenges of IoT application protocols for smart farming. Comput. Netw. 168 (2020).
[33]
A. V. Goldberg. 1997. An efficient implementation of a scaling minimum-cost flow algorithm. J. Algor. 22 (1997), 1–29.
[34]
A. V. Goldberg, E. Tardos, and R. E. Tarjan. 1990. Network flow algorithmss. Paths, Flows VLSI-Des. 9 (1990), 101–164.
[35]
P. Golle, K. Leyton-Brown, and I. Mironov. 2001. Incentives in peer-to-peer file sharing. In ACM EC’01.
[36]
X. Gong and N. Shroff. 2018. Incentivizing truthful data quality for quality-aware mobile data crowdsourcing. In MobiHoc’18. 161–170.
[37]
X. Gong and N. Shroff. 2019. Truthful mobile crowdsensing for strategic users with private data quality. IEEE/ACM Trans. Netw. 27, 5 (2019), 1959–1972.
[38]
J. Green and J. Laffont. 1979. Incentives in public decision making. Stud. Pub. Econ. 1 (1979), 65–78.
[39]
T. Groves. 1973. Incentives in teams. Econometrica 41, 4 (1973), 617–631.
[40]
W. Heinzelman, A. Chandrakasan, and H. Balakrishnan. 2000. Energy-efficient communication protocol for wireless microsensor networks. In HICSS.
[41]
X. Hou, Z. Sumpter, L. Burson, X. Xue, and B. Tang. 2012. Maximizing data preservation in intermittently connected sensor networks. In MASS.
[42]
S. Hsu, Y. Yu, and B. Tang. 2020. \(DRE^2\): Achieving data resilience in wireless sensor networks: A quadratic programming approach. In IEEE MASS.
[43]
S. Hu, G. Li, and G. Huang. 2021. Dynamic spatial-correlation-aware topology control of wireless sensor networks using game theory. IEEE Sensors J. 21, 5 (2021), 7093–7102.
[44]
H. Huang, A. V. Savkin, M. Ding, and C. Huang. 2019. Mobile robots in wireless sensor networks: A survey on tasks. Comput. Netw. 148 (2019), 1–19.
[45]
J. Jang and F. Adib. 2019. Underwater backscatter networking. In ACM SIGCOMM.
[46]
J. Jaramillo and R. Srikant. 2010. A game theory based reputation mechanism to incentivize cooperation in wireless Ad Hoc networks. Ad Hoc Netw. 8, 4 (June2010), 416–429.
[47]
H. Jin, L. Su, and K. Nahrstedt. 2017. Theseus: Incentivizing truth discovery in mobile crowd sensing systems. In ACM MobiHoc.
[48]
R. Kannan and S. Iyengar. 2004. Game-theoretic models for reliable path-length and energy-constrained routing with data aggregation in wireless sensor networks. IEEE J. Select. Areas Commun. 22 (2004), 1141–1150.
[49]
W. Z. Khan, Y. Xiang, M. Y. Aalsalem, and Q. Arshad. 2013. Mobile phone sensing systems: A survey. IEEE Commun. Surv. Tutor. 15, 1 (2013), 402–427.
[50]
I. Koutsopoulos. 2013. Optimal incentive-driven design of participatory sensing sys-tems. In IEEE INFOCOM’13.
[51]
E. Koutsoupias and C. Papadimitriou. 2016. Worst-case equilibria. Comput. Sci. Rev. 3, 2 (2016), 65–69.
[52]
Z. Li and H. Shen. 2012. Game-theoretic analysis of cooperation incentive strategies in mobile ad hoc networks. IEEE Trans. Mob. Comput. 11, 8 (2012), 1287–1303.
[53]
W. Liang, X. Ren, X. Jia, and X. Xu. 2013. Monitoring quality maximization through fair rate allocation in harvesting sensor networks. IEEE Trans. Parallel Distrib. Syst. 24, 9 (2013), 1827–1840.
[54]
H. Lim, G. Ghinita, E. Bertino, and M. Kantarcioglu. 2012. A game-theoretic approach for high-assurance of data trustworthiness in sensor networks. In IEEE ICDE’12.
[55]
K. Liu, J. Deng, P. K. Varshney, and K. Balakrishnan. 2007. An acknowledgment-based approach for the detection of routing misbehavior in MANETs. IEEE Trans. Mob. Comput. 6, 5 (May2007), 536–550.
[56]
L. Liu, R. Wang, D. Guo, and X. Fan. 2016. Message dissemination for throughput optimization in storage-limited opportunistic underwater sensor networks. In SECON.
[57]
J. Ly, Y. Chen, and B. Tang. 2023. Data-VCG: A data preservation game for base station-less sensor networks with performance guarantee. In TENSOR’23.
[58]
S. Marti, T. J. Giuli, K. Lai, and M. Baker. 2000. Mitigating routing misbehavior in mobile ad hoc networks. In ACM MobiCom’00.
[59]
M. Maskery and V. Krishnamurthy. 2007. Decentralized adaptation in sensor networks: Analysis and application of regret-based algorithms. In IEEE CDC’07.
[60]
D. Mosse and G. Gadola. 2012. Controlling wind harvesting with wireless sensor networks. In IGCC.
[61]
N. Nisan. 1999. Algorithms for selfish agents: Mechanism design for distributed computation. In STACS’99..
[62]
N. Nisan and A. Ronen. 1999. Algorithmic mechanism design. In STOC’99). 129–140.
[63]
N. Nisan and A. Ronen. 2007. Algorithmic mechanism design. Games Econ. Behav. 35 (2007), 166–196.
[64]
D. Niyato, E. Hossain, M. M. Rashid, and V. K. Bhargava. 2007. Wireless sensor networks with energy harvesting technologies: A game-theoretic approach to optimal energy management. IEEE Wirel. Commun. 14, 4 (2007), 90–96.
[65]
N. Pathak, A. Mukherjee, and S. Misra. 2020. Reconfigure and reuse: Interoperable wearables for healthcare IoT. In IEEE INFOCOM’20.
[66]
F. Pavlidou and G. Koltsidas. 2008. Game theory for routing modeling in communication networks—A survey. J. Commun. Netw. 10, 3 (Sep.2008), 268–286.
[67]
D. Peng, F. Wu, and G. Chen. 2018. Data quality guided incentive mechanism design for crowdsensing. IEEE Trans. Mob. Comput. 17, 2 (2018), 307–319.
[68]
D. E. Phillips, M. Moazzami, G. Xing, and J. M. Lees. 2017. A sensor network for real-time volcano tomography: System design and deployment. In IEEE ICCCN’17.
[69]
X. Qin, X. Wang, L. Wang, Y. Lin, and Wang X.2019. An efficient probabilistic routing scheme based on game theory in opportunistic networks. Comput. Netw. 149 (2019), 144–153.
[70]
M. Rahmati and D. Pompili. 2019. UWSVC: Scalable video coding transmission for in-network underwater imagery analysis. In IEEE MASS’19.
[71]
B. Rashid and M. H. Rehmani. 2016. Applications of wireless sensor networks for urban areas: A survey. J. Netw. Comput. Applic. 60 (2016), 192–219.
[72]
J. Rivera, Y. Chen, and B. Tang. 2023. On the performance of Nash equilibria of data preservation in base station-less sensor networks. In IEEE MASS 2023.
[73]
S. Sengupta, M. Chatterjee, and K. Kwiat. 2010. A game theoretic framework for power control in wireless sensor networks. IEEE Trans. Comput. 59, 2 (2010), 231–242.
[74]
I. F. Senturk, K. Akkaya, and S. Yilmaz. 2014. Relay placement for restoring connectivity in partitioned wireless sensor networks under limited information. Ad Hoc Netw. 13 (2014), 487–503.
[75]
H.-Y. Shi, W.-L. Wang, N.-M. Kwok, and S.-Y. Chen. 2012. Game theory for wireless sensor networks: A survey. Sensors (Basel, Switz.) 12 (122012), 9055–97.
[76]
J. Shneidman and D. C. Parkes. 2003. Rationality and self-interest in peer to peer networks. In IPTPS’03.
[77]
Y. Shoham. 2008. Computer science and game theory. Commun. ACM 51, 8 (Aug.2008), 74–79.
[78]
A. A. Syed, W. Ye, and J. Heidemann. 2008. T-Lohi: A new class of MAC protocols for underwater acoustic sensor networks. In IEEE INFOCOM’08. Retrieved from: http://www.isi.edu/ilense/snuse/index.html.
[79]
R. Tan, G. Xin, J. Chen, W.-Z. Song, and R. Huang. 2013. Fusion-based volcanic earthquake detection and timing in wireless sensor networks. ACM Trans. Sensor Netw. 9 (2013).
[80]
B. Tang. 2018. \(DAO^2\): Overcoming overall storage overflow in intermittently connected sensor networks. In IEEE INFOCOM’18, 1–9.
[81]
B. Tang, N. Jaggi, and M. Takahashi. 2014. Achieving data K-availability in intermittently connected sensor networks. In ICCCN’14.
[82]
B. Tang, N. Jaggi, H. Wu, and R. Kurkal. 2013. Energy-efficient data redistribution in sensor networks. ACM Trans. Sensor Netw. 9, 2 (Apr.2013), 11:1–11:28.
[83]
B. Tang, H. Ngo, Y. Ma, and B. Alhakami. 2023. \(DAO^2\): Overcoming overall storage overflow in intermittently connected sensor networks. Accepted at IEEE/ACM Transactions on Networking (2023), 1–16. DOI:
[84]
W. Vickrey. 1961. Counterspeculation, auctions and competitive sealed tenders. J. Finance 16, 1 (1961), 8–37.
[85]
A. C. Voulkidis, M. P. Anastasopoulos, and P. G. Cottis. 2013. Energy efficiency in wireless sensor networks: A game-theoretic approach based on coalition formation. ACM Trans. Sensor Netw. 9, 4 (July2013).
[86]
W. Wang and X.-Yang Li. 2006. Low-cost routing in selfish and rational wireless ad hoc networks. IEEE Trans. Mob. Comput. 5, 5 (2006), 596–607.
[87]
D. Wang, J. Ren, Z. Wang, Y. Wang, and Y. Zhang. 2023. PrivAim: A dual-privacy preserving and quality-aware incentive mechanism for federated learning. IEEE Trans. Comput. 72, 7 (2023), 1913–1927.
[88]
Q. Wang and W. Yang. 2007. Energy consumption model for power management in wireless sensor networks. In SECON’07.
[89]
W. Wang, X. Y. Li, and Y. Wang. 2004. Truthful multicast routing in selfish wireless networks. In MobiCom. 245–259.
[90]
Z. Wang, T. Alpcan, J. S. Evans, and S. Dey. 2019. Truthful mechanism design for wireless powered network with channel gain reporting. IEEE Trans. Commun. 67, 11 (2019), 7966–7979.
[91]
A. Whitmore, A. Agarwal, and L. Da Xu. 2015. The internet of things—A survey of topics and trends. Inf. Syst. Front. 17 (2015), 261–274.
[92]
A. Wichmann, T. Korkmaz, and A. S. Tosun. 2018. Robot control strategies for task allocation with connectivity constraints in wireless sensor and robot networks. IEEE Trans. Mob. Comput. 17, 6 (2018), 1429–1441.
[93]
F. Wu, T. Chen, S. Zhong, L. Li, and Y. Yang. 2008. Incentive-compatible opportunistic routing for wireless networks. In ACM MobiCom’08. 303–314.
[94]
F. Wu, K. Gong, T. Zhang, G. Chen, and C. Qiao. 2015. COMO: A game-theoretic approach for joint multirate opportunistic routing and forwarding in non-cooperative wireless networks. IEEE Trans. Wirel. Commun. 14, 2 (2015), 948–959.
[95]
L. D. Xu, W. He, and S. Li. 2014. Internet of things in industries: A survey. IEEE Trans. Industr. Inform. 10, 4 (2014), 2233–2243.
[96]
X. Xue, X. Hou, B. Tang, and R. Bagai. 2013. Data preservation in intermittently connected sensor networks with data priorities. In SECON’13.
[97]
D. Yang, G. Xue, X. Fang, and J. Tang. 2016. Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones. IEEE/ACM Trans. Netw. 24, 3 (2016), 1732–1744.
[98]
H. Yedidsion, A. Banik, P. Carmi, M. J. Katz, and M. Segal. 2017. Efficient data retrieval in faulty sensor networks using a mobile mule. In WiOpt’17.
[99]
H. Yetgin, K. Cheung, M. El-Hajjar, and L. A. Hanzo. 2017. Survey of network lifetime maximization techniques in wireless sensor networks. IEEE Commun. Surv. Tutor. 19 (2017), 828–854.
[100]
Q. Yu, J. Chen, Y. Fan, X. Shen, and Y. Sun. 2010. Multi-channel assignment in wireless sensor networks: A game theoretic approach. In IEEE INFOCOM’10. 1–9.
[101]
C. Zhang, X. Zhu, Y. Song, and Y. Fang. 2010. A formal study of trust-based routing in wireless ad hoc networks. In IEEE INFOCOM’10.
[102]
X. Zhang, Z. Yang, W. Sun, Y. Liu, S. Tang, K. Xing, and X. Mao. 2016. Incentives for mobile crowd sensing: A survey. IEEE Commun. Surv. Tutor. 18, 1 (2016), 54–67.
[103]
H. Zheng and J. Wu. 2015. Data collection and event detection in the deep sea with delay minimization. In SECON’15.
[104]
Z. Zheng, Y. Peng, F. Wu, S. Tang, and G. Chen. 2017. Trading data in the crowd: Profit-driven data acquisition for mobile crowdsensing. IEEE J. Select. Areas Commun. 35, 2 (2017), 486–501.
[105]
S. Zhong, J. Chen, and Y. R. Yang. 2003. Sprite: A simple, cheat-proof, credit-based system for mobile ad-hoc networks. In IEEE INFOCOM’03.

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  • (2023)Data-VCG: A Data Preservation Game for Base Station-less Sensor Networks with Performance Guarantee2023 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking57963.2023.10186405(1-6)Online publication date: 12-Jun-2023

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    cover image ACM Transactions on Sensor Networks
    ACM Transactions on Sensor Networks  Volume 20, Issue 1
    January 2024
    717 pages
    EISSN:1550-4867
    DOI:10.1145/3618078
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    Published: 19 October 2023
    Online AM: 27 June 2023
    Accepted: 13 June 2023
    Revised: 08 May 2023
    Received: 17 June 2022
    Published in TOSN Volume 20, Issue 1

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    Author Tags

    1. Base station-less sensor networks
    2. data preservation
    3. energy-efficiency
    4. network flow (minimum cost flow and maximum flow)
    5. integer linear program
    6. game theory
    7. algorithmic mechanism design

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    • (2023)Data-VCG: A Data Preservation Game for Base Station-less Sensor Networks with Performance Guarantee2023 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking57963.2023.10186405(1-6)Online publication date: 12-Jun-2023

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