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
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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.