Incentive mechanism design for delayed WiFi offloading

Cai, Shijie; Duan, Lingjie; Jing, Wang; Zhou, Shidong; Zhang, Rui

doi:10.1109/icc.2015.7248848

Cited by 12 publications

(5 citation statements)

References 5 publications

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“…It is worth mentioning that two further non-cooperative models, known as Bayesian and Stackelberg (or Leader-Follower) games, have been used to model RAT selection. While Bayesian models have found limited application to RAT selection (an example can be found in [95]), Stackelberg games require networks (RATs) to also take actions and observe the corresponding utility [131]- [133]. In the following, we will neither discuss Bayesian games, due to their limited application, nor Stackelberg games, since the focus of this paper is on strategies involving users vs. users competition.…”

Section: Rat Selection Gamesmentioning

confidence: 99%

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

et al. 2021

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User-centric radio access technology (RAT) selection is a key communication paradigm, given the increased number of available RATs and increased cognitive capabilities at the user end. When considered against traditional network-centric approaches, user-centric RAT selection results in reduced network-side management load, and leads to lower operational costs for RATs, as well as improved quality of service (QoS) and quality of experience (QoE) for users. The complex between-users interactions involved in RAT selection require, however, specific analyses, toward developing reliable and efficient schemes. Two theoretical frameworks are most often applied to user-centric RAT selection analysis, i.e., game theory (GT) and multi-agent learning (MAL). As a consequence, several GT models and MAL algorithms have been recently proposed to solve the problem at hand. A comprehensive discussion of such models and algorithms is, however, currently missing. Moreover, novel issues introduced by next-generation communication systems also need to be addressed. This paper proposes to fill the above gaps by providing a unified reference for both ongoing research and future research directions in the field. In particular, the review addresses the most common GT and MAL models and algorithms, and scenario settings adopted in user-centric RAT selection in terms of utility function and network topology. Regarding GT, the review focuses on non-cooperative models, because of their widespread use in RAT selection; as for MAL, a large number of algorithms are described, ranging from game-theoretic to reinforcement learning (RL) schemes, and also including most recent approaches, such as deep RL (DRL) and multi-armed bandit (MAB). Models and algorithms are analyzed by comparatively reviewing relevant literature. Finally, open challenges are discussed, in light of ongoing research and standardization activities.

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Section: Rat Selection Gamesmentioning

confidence: 99%

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

et al. 2021

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“…More concretely for Wi-Fi networks, non-cooperative game theory has been used for various optimizations and evaluations: e.g., for bandwidth sharing within a single Wi-Fi network [16], for the selection of the optimal network from a set of available Wi-Fi networks [17], for the creation of pricing schemes in order to determine the optimal network to connect to, either Wi-Fi or a competing network (e.g., mobile, WiMax) [18][19][20][21], and for reducing mobile volume usage by creating delayed offloading schemes and smart caching via Wi-Fi networks [22][23][24].…”

Section: Game Theoretical Applied To Ict Networkmentioning

confidence: 99%

Modeling the relationship between network operators and venue owners in public Wi-Fi deployment using non-cooperative game theory

Spruytte

Benhamiche

Chardy

et al. 2019

J Wireless Com Network

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Wireless data demands keep rising at a fast rate. In 2016, Cisco measured a global mobile data traffic volume of 7.2 Exabytes per month and projected a growth to 49 Exabytes per month in 2021. Wi-Fi plays an important role in this as well. Up to 60% of the total mobile traffic was off-loaded via Wi-Fi (and femtocells) in 2016. This is further expected to increase to 63% in 2021. In this publication, we look into the roll-out of public Wi-Fi networks, public meaning in a public or semi-public place (pubs, restaurants, sport stadiums, etc.). More concretely we look into the collaboration between two parties, a technical party and a venue owner, for the roll-out of a new Wi-Fi network. The technical party is interested in reducing load on its mobile network and generating additional direct revenues, while the venue owner wants to improve the attractiveness of the venue and consequentially generate additional indirect revenues. Three Wi-Fi pricing models are considered: entirely free, slow access with ads or fast access via paid access (freemium), and paid access only (premium). The technical party prefers a premium model with high direct revenues, the venue owner a free/freemium model which is attractive to its customers, meaning both parties have conflicting interests. This conflict has been modeled using non-cooperative game theory incorporating detailed cost and revenue models for all three Wi-Fi pricing models. The initial outcome of the game is a premium Wi-Fi network, which is not the optimal solution from an outsider's perspective as a freemium network yields highest total payoffs. By introducing an additional compensation scheme which corresponds with negotiation in real life, the outcome of the game is steered toward a freemium solution.

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“…A similar work [5], which also motivates users to offload cellular traffic to WiFi networks, proposed an incentive mechanism to allow network operators to optimally award users, but differs from [4] in how network operators negotiate an offloading contract with users.…”

Section: Related Workmentioning

confidence: 99%

Optimal scheduling for incentive WiFi offloading under energy constraint

Gao

Ito

Shiratori

2016

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Abstract-WiFi offloading, where mobile device users (e.g., smart phone users) transmit packets through WiFi networks rather than cellular networks, is a promising solution to alleviating the heavy traffic burden of cellular networks due to data explosion. However, since WiFi networks are intermittently available, a mobile device user in WiFi offloading usually needs to wait for WiFi connection and thus experiences longer delay of packet transmission. To motivate users to participate in WiFi offloading, cellular network operators give incentives (rewards like coupons, e-coins) to users who wait for WiFi connection and transmit packets through WiFi networks.In this paper, we aim at maximizing users' rewards while meeting constraints on queue stability and energy consumption. However, we face scheduling challenges from random packet arrivals, intermittent WiFi connection and time varying wireless link states. To address these challenges, we first formulate the problem as a stochastic optimization problem. We then propose an optimal scheduling policy, named Optimal scheduling Policy under Energy Constraint (OPEC), which makes online decisions as to when to delay packet transmission to wait for WiFi connection and which wireless link (WiFi link or cellular link) to transmit packets on. OPEC automatically adapts to random packet arrivals and time varying wireless link states, not requiring a priori knowledge of packet arrival and wireless link probabilities. As verified by simulations, OPEC scheduling policy can achieve the maximum rewards while keeping queue stable and meeting energy consumption constraint.

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Incentive mechanism design for delayed WiFi offloading

Cited by 12 publications

References 5 publications

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

Modeling the relationship between network operators and venue owners in public Wi-Fi deployment using non-cooperative game theory

Optimal scheduling for incentive WiFi offloading under energy constraint

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