Motivating Human-Enabled Mobile Participation for Data Offloading

Zhang, Xiaonan; Guo, Linke; Li, Ming; Fang, Yuguang

doi:10.1109/tmc.2017.2773087

Cited by 28 publications

(15 citation statements)

References 37 publications

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“…The uniform pricing scheme was adopted, where the impacts of network effects and congestion were jointly analyzed. In [33], [34], the authors studied the similar problem to that in [5], where the discriminatory pricing strategy was applied by the operator. Nevertheless, they merely formulated the one-shot game to model the interaction between the network operator and mobile users with static pricing.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, we investigate the users' behaviors by jointly incorporating the network effects and congestion effects. In particular, we use another term c indicates that all users experience and are affected by the congestion, and the marginal cost of the congestion increases as the total demand increases [5], [34]. Then, the net utility of user i extended from (1) is expressed as follows:…”

Section: A Basic Static Modelmentioning

confidence: 99%

“…Naturally, the two-stage Stackelberg game can be adopted to model the interaction between the MNO and users [33], [34]. In the upper Stage I, the MNO, i.e., the leader, determines price p i to maximize its revenue.…”

Section: A Basic Static Modelmentioning

confidence: 99%

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Dynamic Pricing for Revenue Maximization in Mobile Social Data Market With Network Effects

Xiong

Niyato

Wang

et al. 2020

IEEE Trans. Wireless Commun.

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Mobile data demand is increasing tremendously in wireless social networks, and thus an efficient pricing scheme for social-enabled services is urgently needed. Though static pricing is dominant in the actual data market, price intuitively ought to be dynamically changed to yield greater revenue. The critical question is how to design the optimal dynamic pricing scheme, with prospects for maximizing the expected long-term revenue. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile network operator in the social data market. In the market, the operator, i.e., the seller, individually offers each mobile user, i.e., the buyer, a certain price in multiple time periods dynamically and repeatedly. The proposed scheme exploits the network effects in the mobile users' behaviors that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Thereafter, we propose a modified sequential pricing policy in order to ensure social fairness among mobile users in terms of their individual utilities.We analytically demonstrate that the proposed sequential dynamic pricing scheme can help the operator gain greater revenue and mobile users achieve higher total utilities than those of the baseline static pricing scheme. To gain more insights, we further study a simultaneous dynamic pricing scheme in which the operator determines the pricing strategy at the beginning of each time period. Mobile users decide on their individual data demand in each time period simultaneously, considering the network effects in the social domain and the congestion effects in the network domain. We construct the social graph using Erdős-Rényi (ER) model and the real dataset based social network for performance evaluation. The numerical results corroborate that the dynamics of pricing schemes over static ones can significantly improve the revenue of the operator.

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Section: Related Workmentioning

confidence: 99%

Section: A Basic Static Modelmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Pricing for Revenue Maximization in Mobile Social Data Market With Network Effects

Xiong

Niyato

Wang

et al. 2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

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“…Some prior studies analyzed mobile services by jointly considering the congestion effect and network effect among users [25], [26], [27], [28]. For example, Zhang et al in [25] studied mobile caching users, who pre-cache contents and disseminate the contents to users requesting them. The authors considered the delay caused by serving a large number of content requests and also the social connection among the users.…”

Section: Congestion Effect and Network Effectmentioning

confidence: 99%

Pricing for Collaboration between Online Apps and Offline Venues

Iosifidis

Shou

et al. 2020

IEEE Trans. on Mobile Comput.

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An increasing number of mobile applications (abbrev. apps), like Pokemon Go and Snapchat, reward the users who physically visit some locations tagged as POIs (places-of-interest) by the apps. We study the novel POI-based collaboration between apps and venues (e.g., restaurants). On the one hand, an app charges a venue and tags the venue as a POI. The POI tag motivates users to visit the venue, which potentially increases the venue's sales. On the other hand, the venue can invest in the app-related infrastructure, which enables more users to use the app and further benefits the app's business. The apps' existing POI tariffs cannot fully incentivize the venue's infrastructure investment, and hence cannot lead to the most effective app-venue collaboration. We design an optimal two-part tariff, which charges the venue for becoming a POI, and subsidizes the venue every time a user interacts with the POI. The subsidy design efficiently incentivizes the venue's infrastructure investment, and we prove that our tariff achieves the highest app's revenue among a general class of tariffs. Furthermore, we derive some counter-intuitive guidelines for the POI-based collaboration. For example, a bandwidth-consuming app should collaborate with a low-quality venue (users have low utilities when consuming the venue's products).

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“…To eliminate ineffective function because of additional handover to WiFi AP, Jang, and Chang [17] apply the Software-defined networking (SDN) method for managing handover between WiFi AP and BS, where the network is applicable for offering huge service data rate. By assuming the complicated congestion, numerous users' requests are derived which can be resolved by [18] a method named mobile participation that guides data offloading, and pre-caches massive context over congested areas. Besides, mobile data offloading issues of MEC is highly focused by many developers.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Data Offloading With Cyber Attack Detection Model in Mobile Edge Computing Systems

et al. 2020

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Mobile edge computing (MEC) becomes popular as it offers cloud services and functionalities to the edge devices, to enhance the quality of service (QoS) of end-users by offloading their computationally intensive tasks. At the same time, the rise in the number of internet of things (IoT) objectives poses considerable cybersecurity issues owing to the latest rise in the existence of attacks. Presently, the development of deep learning and hardware technologies offers a way to detect the present traffic condition, data offloading, and cyber-attacks in edge networks. The utilization of DL models finds helpful in several domains in which the MEC provides the decisive beneficiary of the approach for traffic prediction and attack detection since a large quantity of data generated by IoT devices enables deep models to learn better than shallow approaches. In this view, this paper presents a new DL based traffic prediction with a data offloading mechanism with cyber-attack detection (DLTPDO-CD) technique. The proposed model involves three major processes traffic prediction, data offloading, and attack detection. Initially, bidirectional long short term memory (BiLSTM) based traffic prediction to enable the proficient data offloading process. Then, the adaptive sampling cross entropy (ASCE) technique is executed to maximize the network throughput by making decisions related to offloading users to the WiFi system. Finally, a deep belief network (DBN) optimized by a barnacles mating optimizer (BMO) algorithm called BMO-DBN is applied as a detection tool for cyberattacks in MEC. Extensive simulation is carried out to ensure the proficient performance of the DLTPDO-CD model. The experimental outcome stated the superiority of the presented model over the compared methods under different dimensions.

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Motivating Human-Enabled Mobile Participation for Data Offloading

Cited by 28 publications

References 37 publications

Dynamic Pricing for Revenue Maximization in Mobile Social Data Market With Network Effects

Dynamic Pricing for Revenue Maximization in Mobile Social Data Market With Network Effects

Pricing for Collaboration between Online Apps and Offline Venues

Deep Learning Enabled Data Offloading With Cyber Attack Detection Model in Mobile Edge Computing Systems

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