Optimal Pricing Design for Coordinated and Uncoordinated IoT Networks
Pages 7121 - 7137
Abstract
An Internet of Things (IoT) system can include several different types of service providers, who sell IoT service, network service, and computation service to customers, either jointly or separately. A deep understanding of complicated coupling among these providers in terms of pricing and service decisions is critical to the success of IoT networks. This paper studies the impact of the provider interaction structures on the overall IoT system with heterogeneous customers. Specifically, we first study a generic IoT scenario with three interaction structures: coordinated, vertically-uncoordinated, and horizontally-uncoordinated structures. Despite the challenging non-convex optimization problems involved in modeling and analyzing these structures, we successfully obtain the closed-form optimal pricing strategies of providers in each interaction structure. We further extend the analysis to a specific IoT scenario with local computation capability (e.g., Internet of Vehicles (IoV)). We prove that the coordinated structure is better than two uncoordinated structures for both providers and customers, as it avoids selfish price markup behaviors in uncoordinated structures. Between the two uncoordinated structures, when customers’ demand variance is large and utility-cost ratio is medium, vertically-uncoordinated structure is better than horizontal one for both providers and customers, due to the complementary providers’ competition in horizontally-uncoordinated structure. Counter-intuitively, we identify that providers’ optimal prices do not change with their costs at the critical point of customers’ full participation in the vertically-uncoordinated structure.
References
[1]
N. Ding, L. Gao, J. Huang, X. Li, and X. Chen, “Optimal pricing under vertical and horizontal interaction structures for IoT networks,” in Proc. IEEE Int. Conf. Comput. Commun., 2022, pp. 870–879.
[2]
S. K. Goudos, P. I. Dallas, S. Chatziefthymiou, and S. Kyriazakos, “A survey of IoT key enabling and future technologies: 5G, mobile IoT, sematic web and applications,” Wireless Pers. Commun., vol. 97, no. 2, pp. 1645–1675, 2017.
[3]
R. Ahmed, A. K. Malviya, M. J. Kaur, and V. P. Mishra, “Comprehensive survey of key technologies enabling 5G-IoT,” in Proc. 2nd Int. Conf. Adv. Comput. Softw. Eng., 2019, pp. 1–5.
[4]
N. C. Luong, D. T. Hoang, P. Wang, D. Niyato, D. I. Kim, and Z. Han, “Data collection and wireless communication in Internet of Things (IoT) using economic analysis and pricing models: A survey,” IEEE Commun. Surv. Tut., vol. 18, no. 4, pp. 2546–2590, Oct.–Dec. 2016.
[5]
L. Xu, R. Collier, and G. M. O’Hare, “A survey of clustering techniques in WSNs and consideration of the challenges of applying such to 5G IoT scenarios,” IEEE Internet Things J., vol. 4, no. 5, pp. 1229–1249, Oct. 2017.
[6]
L. Chettri and R. Bera, “A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems,” IEEE Internet Things J., vol. 7, no. 1, pp. 16–32, Jan. 2020.
[7]
D. Niyato, X. Lu, P. Wang, D. I. Kim, and Z. Han, “Economics of Internet of Things: An information market approach,” IEEE Wireless Commun., vol. 23, no. 4, pp. 136–145, Aug. 2016.
[8]
“2019 trends in Internet of Things,” 2019. [Online]. Available: https://www.comptia.org/content/research/iot-industry-trends-analysis
[9]
“Internet of things insights and opportunities,” 2016. [Online]. Available: https://www.comptia.org/content/research/internet-of-things-insights-and-opportunities
[10]
“AWS IoT,” 2022. [Online]. Available: https://aws.amazon.com/iot/
[11]
“BYD Auto,” 2022. [Online]. Available: https://bydeurope.com/pdp-auto
[12]
“China Mobile,” 2022. [Online]. Available: https://www.chinamobileltd.com/en/global/home.php
[13]
“Data storage system makes BYD auto an energy leader,” 2019. [Online]. Available: https://e.huawei.com/fr/publications/global/ict_insights/201810190908/manufacturing/201901191539
[14]
“Cosmo smartwatch,” 2022. [Online]. Available: https://cosmotogether.com/
[15]
“How much data does your car collect? Here's a reminder,” 2022. [Online]. Available: https://www.lexology.com/library/detail.aspx?g=68dda91b-0ef5–498f-9996-5fbd9f4fdee3
[16]
I. Lee, “Pricing models for the Internet of Things (IoT): Game perspectives,” Internet Things, vol. 15, 2021, Art. no.
[17]
W. Zhang, X. Li, L. Zhao, X. Yang, T. Liu, and W. Yang, “Service pricing and selection for IoT applications offloading in the multi-mobile edge computing systems,” IEEE Access, vol. 8, pp. 153 862–153 871, 2020.
[18]
H. Dai, H. Zhang, W. Wu, and B. Wang, “A game-theoretic learning approach to QoE-driven resource allocation scheme in 5G-enabled IoT,” EURASIP J. Wireless Commun. Netw., vol. 2019, no. 1, pp. 1–10, 2019.
[19]
B. Qian, H. Zhou, T. Ma, K. Yu, Q. Yu, and X. Shen, “Multi-operator spectrum sharing for massive IoT coexisting in 5 G/B5G wireless networks,” IEEE J. Sel. Areas Commun., vol. 39, no. 3, pp. 881–895, 2020.
[20]
E. H. H. Kure, P. Engelstad, S. Maharjan, S. Gjessing, and Y. Zhang, “Distributed uplink offloading for IoT in 5G heterogeneous networks under private information constraints,” IEEE Internet Things J., vol. 6, no. 4, pp. 6151–6164, Aug. 2019.
[21]
Y. Zhao, W. Bao, D. Wang, K. Xu, and L. Lv, “Connection-based pricing for IoT devices: Motivation, comparison, and potential problems,” in Proc. IEEE Conf. Comput. Commun. Workshops, 2020, pp. 1302–1303.
[22]
X. Zhang, P. Huang, L. Guo, and M. Sha, “Incentivizing relay participation for securing IoT communication,” in Proc. IEEE Conf. Comput. Commun., 2019, pp. 1504–1512.
[23]
N. Raveendran, H. Zhang, L. Song, L.-C. Wang, C. S. Hong, and Z. Han, “Pricing and resource allocation optimization for IoT fog computing and NFV: An EPEC and matching based perspective,” IEEE Trans. Mobile Comput., vol. 21, no. 4, pp. 1349–1361, Apr. 2022.
[24]
X. Xu, X. Liu, Z. Xu, C. Wang, S. Wan, and X. Yang, “Joint optimization of resource utilization and load balance with privacy preservation for edge services in 5 G networks,” Mobile Netw. Appl., vol. 25, no. 2, pp. 713–724, 2019.
[25]
M. Liu and Y. Liu, “Price-based distributed offloading for mobile-edge computing with computation capacity constraints,” IEEE Wireless Commun. Lett., vol. 7, no. 3, pp. 420–423, Jun. 2018.
[26]
M. A. Alsheikh, D. T. Hoang, D. Niyato, D. Leong, P. Wang, and Z. Han, “Optimal pricing of Internet of Things: A machine learning approach,” IEEE J. Sel. Areas Commun., vol. 38, no. 4, pp. 669–684, Apr. 2020.
[27]
L. Mei, W. Li, and K. Nie, “Pricing decision analysis for information services of the Internet of Things based on stackelberg game,” in Proc. Int. Conf. Logistics Inform. Serv. Sci., 2013, pp. 1097–1104.
[28]
J.-S. Lee and B. Hoh, “Sell your experiences: A market mechanism based incentive for participatory sensing,” in Proc. IEEE Int. Conf. Pervasive Comput. Commun., 2010, pp. 60–68.
[29]
W. Mao, Z. Zheng, and F. Wu, “Pricing for revenue maximization in IoT data markets: An information design perspective,” in IEEE Conf. Comput. Commun., 2019, pp. 1837–1845.
[30]
A. Ghosh and S. Sarkar, “Pricing for profit in Internet of Things,” IEEE Trans. Netw. Sci. Eng., vol. 6, no. 2, pp. 130–144, 2nd Quarter 2019.
[31]
”What devices like apple, Google smartwatches are beginning to display about our health,” [Online]. Available: https://www.cnbc.com/2021/12/12/what-apple-google-smartwatches-are-learning-about-our-health.html
[32]
I. Hedi, I. Speh, and A. Sarabok, “IoT network protocols comparison for the purpose of IoT constrained networks,” in Proc. Int. Conv. Inf. Commun. Technol. Electron. Microelectronics2017, pp. 501–505.
[33]
R. K. Kodali and A. Anjum, “IoT based home automation using node-red,” in Proc. Int. Conf. Green Comput. Internet Things, 2018, pp. 386–390.
[34]
C. Xu, G. Zheng, and X. Zhao, “Energy-minimization task offloading and resource allocation for mobile edge computing in NOMA heterogeneous networks,” IEEE Trans. Veh. Technol., vol. 69, no. 12, pp. 16 001–16 016, Dec. 2020.
[35]
M. Cui, S. Zhong, B. Li, X. Chen, and K. Huang, “Offloading autonomous driving services via edge computing,” IEEE Internet Things J., vol. 7, no. 10, pp. 10 535–10 547, Oct. 2020.
[36]
R. Ding, Z. Yang, Y. Wei, H. Jin, and X. Wang, “Multi-agent reinforcement learning for urban crowd sensing with for-hire vehicles,” in Proc. IEEE Conf. Comput. Commun., 2021, pp. 1–10.
[37]
Z. Wang, L. Gao, T. Wang, and J. Luo, “Monetizing edge service in mobile internet ecosystem,” IEEE Trans. Mobile Comput., vol. 21, no. 5, pp. 1751–1765, May 2020.
[38]
R. La and V. Anantharam, “Network pricing using game theoretic approach,” in Proc. 38th IEEE Conf. Decis. Control, 2002, pp. 4008–4013.
[39]
A. Hayrapetyan, É. Tardos, and T. Wexler, “A network pricing game for selfish traffic,” Distrib. Comput., vol. 19, no. 4, pp. 255–266, Jan. 2007.
[40]
X. Wang, X. Chen, Z. Li, and Y. Chen, “Access delay analysis and optimization of NB-IoT based on stochastic network calculus,” in Proc. Int. Conf. Smart Internet Things, 2018, pp. 23–28.
[41]
V. A. Barros, J. C. Estrella, L. B. Prates, and S. M. Bruschi, “An IoT-DaaS approach for the interoperability of heterogeneous sensor data sources,” in Proc. Int. Conf. Model. Anal. Simul. Wireless Mobile Syst., 2018, pp. 275–279.
[42]
W. Liu, K. Nakauchi, and Y. Shoji, “A location-aided flooding mechanism in community-based IoT networks,” in Proc. Annu. Comput. Commun. Workshop Conf., 2017, pp. 1–6.
[43]
K. Binmore, A. Rubinstein, and A. Wolinsky, “The nash bargaining solution in economic modelling,” RAND J. Econ., vol. 17, pp. 176–188, 1986.
[44]
C.-T. Zhang and L.-P. Liu, “Research on coordination mechanism in three-level green supply chain under non-cooperative game,” Appl. Math. Modelling, vol. 37, no. 5, pp. 3369–3379, 2013.
[45]
Y. Wang, “Joint pricing-production decisions in supply chains of complementary products with uncertain demand,” Operations Res., vol. 54, no. 6, pp. 1110–1127, 2006.
[46]
K. Sato and K. Nakashima, “Optimal pricing problem for a pay-per-use system based on the Internet of Things with intertemporal demand,” Int. J. Prod. Econ., vol. 221, 2020, Art. no.
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Published: 23 September 2022
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