A research on mobile collective monitoring with regard to heterogeneous task allocation and deep reinforcement learning based on Internet of Things based on Stackelberg game theory.

Vahedi, Zohreh; Seyyed  Mahdavi Chabok, Seyyed Javad; Veisi, Gelareh

doi:10.22075/ijnaa.2024.33578.5012

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	A research on mobile collective monitoring with regard to heterogeneous task allocation and deep reinforcement learning based on Internet of Things based on Stackelberg game theory.
International Journal of Nonlinear Analysis and Applications
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 04 آذر 1404 اصل مقاله (1.31 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.33578.5012
نویسندگان
Zohreh Vahedi¹؛ Seyyed Javad Seyyed Mahdavi Chabok^* ²؛ Gelareh Veisi¹
¹Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
²Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
تاریخ دریافت: 29 بهمن 1402، تاریخ پذیرش: 02 اردیبهشت 1403
چکیده
Today, with the rapid growth of Internet-based service delivery services, the realization of numerous applications, including mobile mass surveillance, has become possible. In mobile crowd sensing, equipment located at the edges of the network can be used to provide computing services, storage and execution of functions that have time priorities. Despite the many studies that have been done in the past on the application of the mobile crowd sensing approach, the management of handling heterogeneous requests by considering the quality of service has not been comprehensively investigated yet. Therefore, the main goal of this paper is to provide an approach to allocate heterogeneous tasks in the form of implementing mobile crowd sensing in such a way that both the period for the completion of the activity is reduced and the quality of coverage and service level are observed at an optimal level. Since the participating groups in such an approach have conflicts of interest, therefore, the Stackelberg inverse game theory has been used as a tool to manage the level of user participation and consider the benefit of all players. One of the features of this game model is the possibility of implementing it without having complete information about all players. In order to reach the equilibrium point of the game, the optimal strategy of the applicants is determined by using the deep reinforcement learning algorithm, because this method can be useful in finding the appropriate proposed strategy by using the history of interactions. One of the important challenges when applying learning algorithms is the lack of stability during the execution of the learning process. In this regard, an approximate policy has been used to approximate the values of the reward function, which prevents divergence during the implementation of the learning process. Another important challenge is knowing the density of user participation in mobile mass monitoring programs. The higher the number of monitoring nodes in an area, the better the coverage quality can be. For this purpose, the fuzzy system has been used, which can estimate the level of participation density by having the time range of users' presence in the study area and the level of geographic density. In this paper, three characteristics of activity completion time frame, service quality and coverage level have been evaluated. According to the obtained results, the use of such an approach increases the coverage level by more than 17\% compared to the average of common methods.
کلیدواژه‌ها
mobile collective monitoring؛ heterogeneous task allocation؛ Deep Reinforcement Learning؛ Internet of Things؛ Reverse Stackelberg Game

مراجع
[1] A. Al Buhussain, E. Robson, and A. Boukerche, Performance analysis of bio-inspired scheduling algorithms for cloud environments, IEEE Int. Parallel Distrib. Process. Symp. Workshops, IEEE, 2016, pp. 776–785. [2] T. Ali, U. Draz, S. Yasin, J. Noureen, A. Shaf, and M. Ali, An efficient participant’s selection algorithm for crowdsensing, Int. J. Adv. Comput. Sci. Appl. 9 (2018), 399–404. [3] C.M. Angelopoulos, O. Evangelatos, S. Nikoletseas, T.P. Raptis, J. DP Rolim, and K. Veroutis, A user-enabled testbed architecture with mobile crowdsensing support for smart, green buildings, IEEE Int. Conf. Commun., IEEE, 2015, pp. 573–578. [4] K. Arulkumaran, M.P. Deisenroth, M. Brundage, and A.A. Bharath, Deep reinforcement learning: A brief survey, IEEE Signal Process. Mag. 34 (2017), no. 6, 26–38. [5] D. Bahdanau, Neural machine translation by jointly learning to align and translate, arXiv preprint arXiv:1409.0473, (2014). [6] F. Bao, R. Chen, and J. Guo, Scalable, adaptive and survivable trust management for community of interest based internet of things systems, IEEE 11th Int. Symp. Autonom. Decent. Syst., IEEE, 2013, pp. 1–7. [7] E. Barrett, E. Howley, and J. Duggan, Applying reinforcement learning towards automating resource allocation and application scalability in the cloud, Concurr. Comput.: Practice Exper. 25 (2013), no. 12, 1656–1674. [8] T. Basar, On the relative leadership property of Stackelberg strategies, J. Optim. Theory Appl. 11 (1973), no. 6, 655–661. [9] T. Basar and G.J. Olsder, Dynamic Noncooperative Game Theory, Society for Industrial and Applied Mathematics, 1998. [10] S. Basu, M. Karuppiah, K. Selvakumar, K.-C. Li, S. Hafizul Islam, M. Mehedi Hassan, and M.Z.A. Bhuiyan, An intelligent/cognitive model of task scheduling for IoT applications in cloud computing environment, Future Gen. Comput. Syst. 88 (2018), 254–261. [11] E. Bohn, E.M. Coates, S. Moe, and T.A. Johansen, Deep reinforcement learning attitude control of fixed-wing UAVs using proximal policy optimization, Int. Conf. Unmanned Aircraft Systems, IEEE, 2019, pp. 523–533. [12] S. Bradai, S. Khemakhem, and M. Jmaiel, Real-time and energy aware opportunistic mobile crowdsensing framework based on people’s connectivity habits, Comput. Networks 142 (2018), 179–193. [13] S. Bresciani, S. Ur Rehman, G.M. Alam, K. Ashfaq, and M. Usman, Environmental MCS package, perceived environmental uncertainty and green performance: In green dynamic capabilities and investment in environmental management perspectives, Rev. Int. Bus. Strat. 33 (2023), no. 1, 105–126. [14] H. Cai, Y. Zhu, Z. Feng, H. Zhu, J. Yu, and J. Cao, Truthful incentive mechanisms for mobile crowd sensing with dynamic smartphones, Comput. Networks 141 (2018), 1–16. [15] D.H. Cansever and T. Basar, On stochastic incentive control problems with partial dynamic information, Syst. Control Lett. 6 (1985), no. 1, 69–75. [16] G.F.H. Canepa, A context-aware application offloading scheme for a mobile peer-to-peer environment, Ph.D. Thesis, Korea Advanced Institute of Science and Technology (KAIST), 2013. [17] A. Capponi, C. Fiandrino, B. Kantarci, L. Foschini, D. Kliazovich, and P. Bouvry, A survey on mobile crowdsensing systems: Challenges, solutions, and opportunities, IEEE Commun. Surv. Tutor. 21 (2019), no. 3, 2419–2465. [18] Y. Chen, B. Li, and Q. Zhang, Incentivizing crowdsourcing systems with network effects, IEEE INFOCOM 2016-The 35th Annual IEEE Int. Conf. Comput. Commun., IEEE 35 (2016), 1–9. [19] J. Chen, H. Ma, D. Zhao, and D.S. Wei, Participant density-independent location privacy protection for data aggregation in mobile crowd-sensing, Wireless Person. Commun. 98 (2018), no. 1, 699–723. [20] M. Chen, T. Wang, K. Ota, M. Dong, M. Zhao, and A. Liu, Intelligent resource allocation management for vehicles network: An A3C learning approach, Comput. Commun. 151 (2020), 485–494. [21] J. Chen and A. Zhang, Hetmaml: Task-heterogeneous model-agnostic meta-learning for few-shot learning across modalities, Proc. 30th ACM Int. Conf. Inf. Knowledge Manag. 30 (2021), 191–200. [22] M.H. Cheung, F. Hou, and J. Huang, Delay-sensitive mobile crowdsensing: Algorithm design and economics, IEEE Trans. Mobile Comput. 17 (2018), no. 12, 2761–2774. [23] J. Cruz, Leader-follower strategies for multilevel systems, IEEE Trans. Autom. Control 23 (2003), no. 2, 244–255. [24] W. Dai, H. Lu, J. Xiao, and Z. Zheng, Task allocation without communication based on incomplete information game theory for multi-robot systems, J. Intell. Robotic Syst. 94 (2019), no. 3, 841–856. [25] T. Das, P. Mohan, V.N. Padmanabhan, R. Ramjee, and A. Sharma, PRISM: platform for remote sensing using smartphones, Proc. 8th Int. Conf. Mobile Syst. Appl. Serv. 8 (2010), 63–76. [26] R. Deng, R. Lu, C. Lai, T.H. Luan and H. Liang, Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption, IEEE Internet Things J. 3 (2016), no. 6, 1171–1181. [27] X. Ding, R. Lv, X. Pang, J. Hu, Z. Wang, X. Yang, and X. Li, Privacy-preserving task allocation for edge computing-based mobile crowdsensing, Comput. Electr. Eng. 97 (2022), 107528. [28] J.J. Durillo, H. Mohammadi Fard, and R. Prodan, MOHEFT: A multi-objective list-based method for workflow scheduling, IEEE Int. Conf. Cloud Comput. Technol. Sci. Proceedings, IEEE, 2012, pp.185–192. [29] H. Ehtamo and R.P. Hamalainen, Incentive strategies and equilibria for dynamic games with delayed information, J. Optim. Theory Appl. 63 (1989), no. 3, 355–369. [30] J. Espadas, A. Molina, G. Jimenez, M. Molina, R. Ramirez, and D. Concha, A tenant-based resource allocation model for scaling Software-as-a-Service applications over cloud computing infrastructures, Future Gen. Comput. Syst. 29 (2013), no. 1, 273–286. [31] C. Fang, H. Xu, Y. Bai, T. Zhang, Y. Yang, and Z. Hu, Deep reinforcement learning-based joint task offloading in cloud-edge-end cooperation environments, 2nd Int. Conf. Front. Electron. Inf. Comput. Technol., IEEE, 2 (2022), 524–530. [32] J. Feng, T. Li, Y. Zhai, S. Lv, and F. Zhao, Ensuring honest data collection against collusive CSDF attack with binary-minimaxs clustering analysis in mobile crowd sensing, IEEE Access 7 (2019), 124491–124501. [33] S. Ghasemi Falavarjani, M.A. Nematbakhsh, and B. Shahgholi Ghahfarokhi, A multi-criteria resource allocation mechanism for mobile clouds, Int. Symp. Comput. Networks Distrib. Syst., Cham: Springer International Publishing, 2013, pp.145–154. [34] B. Gomathi, K. Krishnasamy, and B.S. Balaji, Epsilon-fuzzy dominance sort-based composite discrete artificial bee colony optimisation for multi-objective cloud task scheduling problem, Int. J. Bus. Intell. Data Min. 13 (2018), no. 1-3, 247–266. [35] L. Graesser and W.L. Keng, Foundations of Deep Reinforcement Learning: Theory and Practice in Python, Addison-Wesley Professional, 2019. [36] N. Groot, G. Zaccour, and B. De Schutter, Hierarchical game theory for system-optimal control: Applications of reverse Stackelberg games in regulating marketing channels and traffic routing, IEEE Control Syst Mag. 37 (2017), no. 2, 129–152. [37] D. Grzonka, A. Jakobik, J. Kolodziej, and S. Pllana, Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security, Future Gen. Comput. Syst. 86 (2018), 1106–1117. [38] B. Guo, H. Chen, Z. Yu, X. Xie, S. Huangfu, and D. Zhang, FlierMeet: a mobile crowdsensing system for cross-space public information reposing, tagging, and sharing, IEEE Trans. Mobile Comput. 14 (2014), no. 10, 2020–2033. [39] B. Guo, Y. Liu, W. Wu, Z. Yu, and Q. Han, ActiveCroud: A framework for optimized multitask allocation in mobile crowdsensing systems, IEEE Trans. Human-Machine Syst. 47 (2016), no. 3, 392–403. [40] A. Hammoud, A. Mourad, H. Otrok, O. Abdel Wahab, and H. Harmanani, Cloud federation formation using genetic and evolutionary game theoretical models, Future Gen. Comput. Syst. 104 (2020), 92–104. [41] Y. Han, Y. Zhu, and J. Yu, A distributed utility-maximizing algorithm for data collection in mobile crowd sensing, IEEE Glob. Commun. Conf., 2014, pp. 277–282. [42] S. He, D.-H. Shin, J. Zhang, and J. Chen, Toward optimal allocation of location dependent tasks in crowdsensing, IEEE INFOCOM Conf. Comput. Commun., IEEE, 2014, pp. 745–753. [43] Y.C. Ho, On incentive problems, Syst. Control Lett. 3 (1983), no. 2, 63–68. [44] Y.-C. Ho, P. Luh, and R. Muralidharan, Information structure, Stackelberg games, and incentive controllability, IEEE Trans. Autom. Control 26 (1981), no. 2, 454–460. [45] Y.-C. Ho, P.B. Luh, and G.J. Olsder, A control-theoretic view on incentives, Automatica 18 (1982), no. 2, 167–179. [46] S. Hu and G. Li, Dynamic request scheduling optimization in mobile edge computing for IoT applications, IEEE Internet Things J. 7 (2019), no. 2, 1426–1437. [47] L. Huang, X. Feng, C. Zhang, L. Qian, and Y. Wu, Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing, Digital Commun. Networks 5 (2019), no. 1, 10–17. [48] M. Hussin, N.A.W. Abdul Hamid, and K.A. Kasmiran, Improving reliability in resource management through adaptive reinforcement learning for distributed systems, J. Parallel Distrib. Comput. 75 (2015), 93–100. [49] M. Hussin, Y.C. Lee, and A.Y. Zomaya, Efficient energy management using adaptive reinforcement learning-based scheduling in large-scale distributed systems, Int. Conf. Parallel Process., IEEE, 2011, pp. 385–393. [50] B. Jang, M. Kim, G. Haerrimana, and J.W. Kim, Q-learning algorithms: A comprehensive classification and applications, IEEE Access 7 (2019), 133653–133667. [51] G. Javadzadeh and A.M. Rahmani, Fog computing applications in smart cities: A systematic survey, Wireless Networks 26 (2020), no. 2, 1433–1457. [52] S.-K. Kim and C.K. Ahn, Auto-tuner-based controller for quadcopter attitude tracking applications, IEEE Trans. Circ. Syst. II: Express Briefs 66 (2019), no. 12, 2012–2016. [53] M.D. Kristensen, Scavenger: Transparent development of efficient cyber foraging applications, IEEE Int. Conf. Pervasive Comput. Commun., IEEE, 2010, pp. 217–226. [54] C. Lau, Neural Networks: Theoretical Foundations and Analysis, IEEE Press, 1991. [55] J.Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki and T. Graepel, Multi-agent reinforcement learning in sequential social dilemmas, arXiv preprint arXiv:1702.03037, (2017). [56] P. Li and Y. Wang, An active learning reliability analysis method using adaptive Bayesian compressive sensing and Monte Carlo simulation (ABCS-MCS), Reliab. Engin. Syst. Safety 221 (2022), 108377. [57] Y. Liu, B. Guo, Y. Wang, W. Wu, Z. Yu, and D. Zhang, TaskMe: Multi-task allocation in mobile crowd sensing, Proc. ACM Int. Joint Conf. Pervasive Ubiq. Comput., 2016, pp. 403–414. [58] R. Liu and J. Zou, The effects of memory replay in reinforcement learning, 56th Ann. Allerton Conf. Commun. Control Comput. (Allerton), IEEE, 56 (2018), 478–485. [59] A. Lu and J.-H. Zhu, Worker recruitment with cost and time constraints in mobile crowd sensing, Future Gen. Comput. Syst. 112 (2020), 819–831. [60] P. Luh, Y. Ho, and R. Muralidharan, Load adaptive pricing: An emerging tool for electric utilities, IEEE Trans. Autom. Control 27 (2003), no. 2, 320–329. [61] F. Luo, Y. Yuan, W. Ding, and H. Lu, An improved particle swarm optimization algorithm based on adaptive weight for task scheduling in cloud computing, Proc. 2nd Int. Conf. Comput. Sci. Appl. Engine. 2 (2018), 1–5. [62] S. Maharjan, Y. Zhang, and S. Gjessing, Optimal incentive design for cloud-enabled multimedia crowdsourcing, IEEE Trans. Multimedia 18 (2016), no. 12, 2470–2481. [63] M. Marjanovic, A. Antonic, and I.P. Zarko, Edge computing architecture for mobile crowdsensing, IEEE Access 6 (2018), 10662–10674. [64] G. Martin-Herran and G. Zaccour, Credible linear-incentive equilibrium strategies in linear-quadratic differential games, Advances in dynamic games and their applications: analytical and numerical developments, Boston: Birkhauser Boston, 2009, pp. 1–31. [65] M. Mehdi, G. Muhlmeier, K. Agrawal, R. Pryss, M. Reichert, and F.J. Hauck, Referenced mobile crowdsensing architecture: A healthcare use case, Proc. Comput. Sci. 134 (2018), 445–451. [66] MILLIONAGENTS, Expert in data collection and processing since 2012, http://www.millionagents.com/. Accessed 10 Nov 2019. [67] A. Mtibaa, A. Fahim, K.A. Harras, and M.H. Ammar, Towards resource sharing in mobile device clouds: Power balancing across mobile devices, ACM SIGCOMM Comput. Commun. Rev. 43 (2013), no. 4, 51–56. [68] A. Nair, P. Srinivasan, S. Blackwell, C. Alcicek, R. Fearon, A. De Maria, V. Panneershelvam, M. Suleyman, C. Beattie, S. Petersen, S. Legg, V. Mnih, K. Kavukcuoglu, and D. Silver, Massively parallel methods for deep reinforcement learning, arXiv preprint arXiv:1507.04296, (2015). [69] J.F. Nash Jr, Equilibrium points in n-person games, Proc. Nat. Acad. Sci. 36 (1950), no. 1, 48–49. [70] L. Ni, J. Zhang, C. Jiang, C. Yan, and K. Yu, Resource allocation strategy in fog computing based on priced timed petri nets, IEEE Internet Things J. 4 (2017), no. 5, 1216–1228. [71] N. Nisan, T. Roughgarden, E. Tardos, and V.V. Vazirani, Algorithmic Game Theory, Cambridge University Press, 2007. [72] M. Pachter, Linear-quadratic reversed Stackelberg differential games with incentives, IEEE Trans. Autom. Control 29 (2003), no. 7, 644–647. [73] Z. Peng, X. Gui, J. An, T. Wu, and R. Gui, Multi-task oriented data diffusion and transmission paradigm in crowdsensing based on city public traffic, Comput. Networks 156 (2019), 41–51. [74] S. Phoha, N. Jacobson, D. Friedlander, and R. Brooks, Sensor network based localization and target tracking through hybridization in the operational domains of beamforming and dynamic space-time clustering, In GLOBECOM’03. IEEE Glob. Telecommun. Conf., (IEEE Cat.), 5 (2003), no. 03CH37489, 2952–2956. [75] PiedPiper, Live crypto prices and market cap charts, https://slideplayer.com/slide/13011299/, 2025. [76] S. Pitts, Rethinking the discount factor in reinforcement learning: A decision theoretic approach, Proc. AAAI Conf. Artific. Intell. 33 (2019), no. 1, 7949–7956. [77] M. Pouryazdan, C. Flandrino, B. Kantarci, T. Soyata, D. Kliazovich, and P. Bouvry, Intelligent gaming for mobile crowd-sensing participants to acquire trustworthy big data in the internet of things, IEEE Access 5 (2017), 22209–22223. [78] M.-R. Ra, B. Liu, T.F. La Porta, and R. Govindan, Medusa: A programming framework for crowd-sensing applications, Proc. 10th Int. Conf. Mobile Syst. Appl. Serv. 10 (2012), 337–350. [79] G. Rjoub, J. Bentahar, O. Abdel Wahab, and A.S. Bataineh, Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems, Concurr. Comput.: Practice Exper. 33 (2021), no. 23, p. e5919. [80] T. Roughgarden, Stackelberg scheduling strategies, Proc. 33th Ann. ACM Symp. Theory Comput., 2001, pp. 104–113. [81] A. Schrijver, Theory of Linear and Integer Programming, John Wiley & Sons, 1998. [82] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, Proximal policy optimization algorithms, arXiv preprint arXiv:1707.06347, 2017 (2017). [83] E.M. Shakshuki, N. Kang, and T.R. Sheltami, EAACK—a secure intrusion-detection system for MANETs, IEEE Trans. Ind. Electron. 60 (2012), no. 3, 1089–1098. [84] N. Shan, X. Cui, and Z. Gao, "DRL+ FL": An intelligent resource allocation model based on deep reinforcement learning for mobile edge computing, Comput. Commun. 160 (2020), 14–24. [85] O. Shehory and S. Kraus, Methods for task allocation via agent coalition formation, Artific. Intell. 101 (1998), no. 1-2, 165–200. [86] H. Shen and T. Basar, Incentive-based pricing for network games with complete and incomplete information, Advances in dynamic game theory: numerical methods, algorithms, and applications to ecology and economics, Boston, MA: Birkhauser Boston, 2007, pp. 431–458. [87] Z. Shi, H. Huang, Y.-E. Sun, X. Wu, F. Li, and M. Tian, An efficient task assignment mechanism for crowdsensing systems, Int. Conf. Cloud Comput. Secur., Cham: Springer International Publishing, 2016, pp. 14–24. [88] Y. Shoham and K. Leyton-Brown, Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press, 2008. [89] M. Simaan and J.B. Cruz Jr, On the Stackelberg strategy in nonzero-sum games, J. Optim. Theory Appl. 11 (1973), no. 5, 533–555. [90] M. Simaan and J.B. Cruz Jr, A Stackelberg solution for games with many players, IEEE Trans. Autom. Control 18 (2003), no. 3, 322–324. [91] P. Singh and N.K. Walia, A review: Cloud computing using various task scheduling algorithms, Int. J. Comput. Appl. 142 (2016), no. 7, 30–32. [92] C.C. Sobin, V. Raychoudhury, and S. Saha, An energy-efficient and buffer-aware routing protocol for opportunistic smart traffic management, Proc. 18th Int. Conf. Distrib. Comput. Network, 2017, pp. 1–8. [93] H.V. Stackelberg, Markiform und gleichgewicht, J. Springer, 1934. [94] H.V. Stackelberg, The Theory of Market Economy, Translated by: A.T. Peacock, William Hodge, London, 1952. [95] S. Sundar and B. Liang, Offloading dependent tasks with communication delay and deadline constraint, IEEE INFOCOM Conf. Comput. Commun., IEEE, 2018, pp. 37–45. [96] R.S. Sutton and A.G. Barto, Reinforcement learning: An introduction, Cambridge: MIT Press, 1998. [97] M. Taghavi, J. Bentahar, and H. Otrok, Two-stage game theoretical framework for IaaS market share dynamics, Future Gen. Comput. Syst. 102 (2020), 173–189. [98] S. Tayeb, S. Latifi, and Y. Kim, A survey on IoT communication and computation frameworks: An industrial perspective, IEEE 7th Ann. Comput. Commun. Workshop Conf., IEEE, 2017, pp. 1–6. [99] M. Tirmazi, A. Barker, N. Deng, M.E. Haque, Z.G. Qin, S. Hand, M. Harchol-Balter, and J. Wilkes, Borg: the next generation, Proc. Fifteenth Eur. Conf. Comput. Syst., 2020, pp. 1–14. [100] M. Tomasoni, A. Capponi, C. Fiandrino, D. Kliazovich, F. Granelli, and P. Bouvry, Profiling energy efficiency of mobile crowdsensing data collection frameworks for smart city applications, 6th IEEE Int. Conf. Mobile Cloud Comput. Serv. Engin. (MobileCloud), IEEE, 6 (2018), 1–8. [101] H. Topcuoglu, S. Hariri, and M.-Y. Wu, Performance-effective and low-complexity task scheduling for heterogeneous computing, IEEE Trans. Parallel Distrib. Syst. 13 (2002), no. 3, 260–274. [102] T. Vallee, T.C. Deissenberg, and Q. Basar, Optimal open loop cheating in dynamic reversed LQ Stackelberg games, Ann. Oper. Res. 8 (1999), no. 0, 217–232. [103] H. Van Seijen, M. Fatemi, and A. Tavakoli, Using a logarithmic mapping to enable lower discount factors in reinforcement learning, Adv. Neural Inf. Process. Syst. 32 (2019). [104] L. Vig and J.A. Adams, Multi-robot coalition formation, IEEE Trans. Robotics 22 (2006), no. 4, 637–649. [105] W.Y.C. Wang, A. Rashid, and H.-M. Chuang, Toward the trend of cloud computing, J. Electr. Commerce Res. 12 (2011), no. 4, 238. [106] T. Wang, K. Ismail, and K.S. Abas Azmi, The rise of MCS and EMA in the sustainable field: a systematic literature analysis, Sustainability 14 (2022), no. 24, 16532. [107] J. Wang, J. Tang, G. Xue, and D. Yang, Towards energy-efficient task scheduling on smartphones in mobile crowd sensing systems, Comput. Networks 115 (2017), 100–109. [108] J. Wang, Y. Wang, D. Zhang, F. Wang, Y. He, and L. Ma, PSAllocator: Multi-task allocation for participatory sensing with sensing capability constraints, Proc. ACM Conf. Comput. Supported Coop. Work Soc. Comput., 2017, pp. 1139–1151. [109] J. Wang, Y. Wang, D. Zhang, F. Wang, H. Xiong, C. Chen, Q. Lv, and Z. Qiu, Multi-task allocation in mobile crowd sensing with individual task quality assurance, IEEE Trans. Mobile Comput. 17 (2018), no. 9, 2101–2113. [110] J. Wang, Y. Wang, D. Zhang, L. Wang, H. Xiong, A. Helal, Y. He, and F. Wang, Fine-grained multitask allocation for participatory sensing with a shared budget, IEEE Internet Things J. 3 (2016), no. 6, 1395–1405. [111] L. Wang, Z. Yu, B. Guo, F. Yi, and F. Xiong, Mobile crowd sensing task optimal allocation: A mobility pattern matching perspective, Front. Comput. Sci. 12 (2018), no. 2, 231–244. [112] L. Wang, Z. Yu, D. Zhang, B. Guo, and C.H. Liu, Heterogeneous multi-task assignment in mobile crowdsensing using spatiotemporal correlation, IEEE Trans. Mobile Comput. 18 (2018), no. 1, 84–97. [113] W. Wang, G. Zeng, D. Tang, and J. Yao, Cloud-DLS: Dynamic trusted scheduling for Cloud computing, Expert Syst. Appl. 39 (2012), no. 3, 2321–2329. [114] Waze, Driving directions and traffic reports by Waze, https://www.waze.com/, Accessed 8 Nov 2019. [115] C.-Y. Wei, Y.-T. Hong and C.-J. Lu, Online reinforcement learning in stochastic games, Adv. Neural Inf. Process. Syst. 30 (2017). [116] C.G. Wu, W. Li, L. Wang, and A.Y. Zomaya, Hybrid evolutionary scheduling for energy-efficient fog-enhanced internet of things, IEEE Trans. Cloud Comput. 9 (2018), no. 2, 641–653. [117] S. Yang, J. Bian, L. Wang, H. Zhu, Y. Fu, and H. Xiong, EdgeSense: Edge-mediated spatial-temporal crowdsensing, IEEE Access 7 (2018), 95122–95131. [118] J. Yang, B. Jiang, Z. Lv, and K.-K. Raymond Choo, A task scheduling algorithm considering game theory designed for energy management in cloud computing, Future Gen. Comput. Syst. 105 (2020), 985–992. [119] B. Yang, X. Xu, F. Tan, and D.H. Park, An utility-based job scheduling algorithm for cloud computing considering reliability factor, Int. Conf. Cloud Serv. Comput., IEEE, 2011, pp. 95–102. [120] D. Yang, G. Xue, X. Fang, and J. Tang, Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing, Proc. 18th Ann. Int. Conf. Mobile Comput. Network. 18 (2012), 173–184. [121] D. Yang, G. Xue, X. Fang, and J. Tang, Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones, IEEE/ACM Trans. Network. 24 (2015), no. 3, 1732–1744. [122] Y. Yu, Q. Shi and H.-K. Lam, Fuzzy sliding mode control of a continuum manipulator, IEEE Int. Conf. Robot. Biomim., IEEE, 2018, pp. 2057–2062. [123] M. Zappatore, A. Longo, and M.A. Bochicchio, Using mobile crowd sensing for noise monitoring in smart cities, Int. Multidiscip. Conf. Comput. Energy Sci. (Splitech), IEEE, 2016, pp. 1–6. [124] Y. Zhan, C.H. Liu, Y. Zhao, J. Zhang, and J. Tang, Free market of multi-leader multi-follower mobile crowdsensing: An incentive mechanism design by deep reinforcement learning, IEEE Trans. Mobile Comput. 19 (2019), no. 10, 2316–2329. [125] F. Zhang, J. Leitner, M. Milford, B. Upcroft, and P. Corke, Towards vision-based deep reinforcement learning for robotic motion control, arXiv preprint arXiv:1511.03791, 2015 (2015). [126] J. Zhang, X. Li, Z. Shi, and C. Zhu, A reputation-based and privacy-preserving incentive scheme for mobile crowd sensing: A deep reinforcement learning approach, Wireless Networks 30 (2024), no. 6, 4685–4698. [127] H. Zhang, Y. Xiao, S. Bu, D. Niyato, F.R. Yu, and Z. Han, Computing resource allocation in three-tier IoT fog networks: A joint optimization approach combining Stackelberg game and matching, IEEE Internet Things J. 4 (2017), no. 5, 1204–1215. [128] D. Zhang, H. Xiong, L. Wang, and G. Chen, CrowdRecruiter: Selecting participants for piggyback crowdsensing under probabilistic coverage constraint, Proc. ACM Int. Joint Conf. Perv. Ubiq. Comput., 2014, pp. 703–714. [129] P. Zhang and M. Zhou, Dynamic cloud task scheduling based on a two-stage strategy, IEEE Trans. Aut. Sci. Engin. 15 (2017), no. 2, 772–783. [130] X. Zhou, G. Zhang, J. Sun, J. Zhou, T. Wei, and S. Hu, Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT, Future Gen. Comput. Syst. 93 (2019), 278–289. [131] P. Zhou, Y. Zheng, and M. Li, How long to wait? Predicting bus arrival time with mobile phone based participatory sensing, Proc. 10th Int. Conf. Mobile Syst. Appl. Serv., 10 (2012), 379–392. [132] W. Zhu, W. Guo, Z. Yu, and H. Xiong, Multitask allocation to heterogeneous participants in mobile crowd sensing, Wireless Commun. Mobile Comput. 2018 (2018), no. 1, 7218061. [133] X. Zhu, Y. Luo, A. Liu, W. Tang, and M.Z.A. Bhuiyan, A deep learning-based mobile crowdsensing scheme by predicting vehicle mobility, IEEE Trans. Intell. Transport. Syst. 22 (2020), no. 7, 4648–4659. [134] X. Zuo, G. Zhang, and W. Tan, Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud, IEEE Trans. Autom. Sci. Engin. 11 (2013), no. 2, 564–573.
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A research on mobile collective monitoring with regard to heterogeneous task allocation and deep reinforcement learning based on Internet of Things based on Stackelberg game theory.