Quality of Experience in Cyber-Physical Social Systems Based on Reinforcement Learning and Game Theory

Tsiropoulou, Eirini Eleni; Kousis, George; Thanou, Athina; Lykourentzou, Ioanna; Papavassiliou, Symeon

doi:10.3390/fi10110108

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…In [24] the focus is on maximizing the satisfaction of the proposed recommendations. User satisfaction is strongly related with the Quality of Experience (QoE) [27][28][29] when dealing with systems dedicated to museums. In [29] there is an effort to understand the visitors' behavior within CH spaces, especially targeting their optimal visiting time along with the maximization of their perceived satisfaction.…”

Section: Related Workmentioning

confidence: 99%

Connecting the museum to the city environment from the visitor’s perspective

Koukoulis

Koukopoulos

Tzortzi

2020

ACI

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Recommendation systems are widely used in tourism in order to provide people personalized suggestions that would make their trip memorable. Nowadays, mobile assisted guided tours based on recommendation services are used in museums to enhance visitors ’ experience. However, all those systems have been designed to target indoor or outdoor museum visits. Is it feasible to design a system that supports mobile services that connect a museum visit to artworks situated outdoor in the city environment? Is it possible to connect the artworks of a city center to the exhibits of a museum? In this work, we attempt to give a first answer to such questions proposing and implementing a set of services that connects the museum to the city public space. In order to show the strength of the implemented services, we present a basic usage scenario along with a first system evaluation showing positive results.

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Section: Related Workmentioning

confidence: 99%

Connecting the museum to the city environment from the visitor’s perspective

Koukoulis

Koukopoulos

Tzortzi

2020

ACI

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“…u,s is defined as the probability of the end-user u to select the MEC server s to offload its data. Based on the theory of stochastic learning automata [22], the rule of updating the end-users' action probabilities at the SDN controller is defined as follows.…”

Section: Mec As a Learning Systemmentioning

confidence: 99%

Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market

et al. 2019

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Software Defined Networks (SDN) and Mobile Edge Computing (MEC), capable of dynamically managing and satisfying the end-users computing demands, have emerged as key enabling technologies of 5G networks. In this paper, the joint problem of MEC server selection by the end-users and their optimal data offloading, as well as the optimal price setting by the MEC servers is studied in a multiple MEC servers and multiple end-users environment. The flexibility and programmability offered by the SDN technology enables the realistic implementation of the proposed framework. Initially, an SDN controller executes a reinforcement learning framework based on the theory of stochastic learning automata towards enabling the end-users to select a MEC server to offload their data. The discount offered by the MEC server, its congestion and its penetration in terms of serving end-users’ computing tasks, and its announced pricing for its computing services are considered in the overall MEC selection process. To determine the end-users’ data offloading portion to the selected MEC server, a non-cooperative game among the end-users of each server is formulated and the existence and uniqueness of the corresponding Nash Equilibrium is shown. An optimization problem of maximizing the MEC servers’ profit is formulated and solved to determine the MEC servers’ optimal pricing with respect to their offered computing services and the received offloaded data. To realize the proposed framework, an iterative and low-complexity algorithm is introduced and designed. The performance of the proposed approach was evaluated through modeling and simulation under several scenarios, with both homogeneous and heterogeneous end-users.

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“…The ESCAPE service was developed based on the principles of the reinforcement learning and game theory, and consists of two decision-making layers. (b) At the first layer, the evacuees acting as stochastic learning automata [6][7][8] decide which evacuation route they want to join based on their past decisions while performing the current evacuation, and taking the (limited) available information from the disaster area through the ESCAPE service, e.g., evacuation rate per route, evacuees already on the route, and capacity of the route. The latter information can easily be available in a real implementation scenario through sensors deployed at the evacuation routes.…”

Section: Contributions and Outlinementioning

confidence: 99%

ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems

2019

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Natural disasters and terrorist attacks pose a significant threat to human society, and have stressed an urgent need for the development of comprehensive and efficient evacuation strategies. In this paper, a novel evacuation-planning mechanism is introduced to support the distributed and autonomous evacuation process within the operation of a public safety system, where the evacuees exploit the capabilities of the proposed ESCAPE service, towards making the most beneficial actions for themselves. The ESCAPE service was developed based on the principles of reinforcement learning and game theory, and is executed at two decision-making layers. Initially, evacuees are modeled as stochastic learning automata that select an evacuation route that they want to go based on its physical characteristics and past decisions during the current evacuation. Consequently, a cluster of evacuees is created per evacuation route, and the evacuees decide if they will finally evacuate through the specific evacuation route at the current time slot or not. The evacuees’ competitive behavior is modeled as a non-co-operative minority game per each specific evacuation route. A distributed and low-complexity evacuation-planning algorithm (i.e., ESCAPE) is introduced to implement both the aforementioned evacuee decision-making layers. Finally, the proposed framework is evaluated through modeling and simulation under several scenarios, and its superiority and benefits are revealed and demonstrated.

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Quality of Experience in Cyber-Physical Social Systems Based on Reinforcement Learning and Game Theory

Cited by 10 publications

References 20 publications

Connecting the museum to the city environment from the visitor’s perspective

Connecting the museum to the city environment from the visitor’s perspective

Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market

ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems

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