Utility Of Service Provider Research Articles

Deep Adversarial Reinforcement Learning based Incentive Mechanism for Content Delivery in D2D-Enabled Mobile Networks

With the explosive growth of mobile devices at the edge of mobile networks, the amount of content that needs to be transmitted is exploded. As it takes resources (cache, bandwidth, and power) for the edge server and nearby devices to deliver content, the incentive mechanism with the optimal pricing and content delivery strategy needs to be studied without relying on the knowledge of network parameters in a competitive environment. In this paper, built on the framework of content delivery with the Stackelberg game, we have proposed a robust deep reinforcement learning method based on adversarial training to learn price strategy for lack of knowledge of private information. Specifically, the Stackelberg game is constructed to describe the interaction between different service providers and service demanders, and a deep adversarial reinforcement learning method is proposed to approximate the opponent’s decision in the worst case, where the price strategy can be trained end-to-end in the competitive environment. The simulation results show that the proposed algorithm can form an effective price strategy and improve the utility of service providers.

Dynamic incentive mechanism in mobile crowdsourcing networks by combining reputation and contract theory

By utilizing the mobile terminals’ sensing and computing capabilities, mobile crowdsourcing network is considered to be a promising technology to support the various large-scale sensing applications. However, considering the limited resources and security issue, mobile users may be unwilling to participate in crowdsourcing without any incentive. In this work, by combining reputation and contract theory, a dynamic long-term incentive mechanism is proposed to attract the mobile users to participate in mobile crowdsourcing networks. A two-period dynamic contract is first investigated to deal with the asymmetric information problem in the crowdsourcing tasks. Reputation strategy is then introduced to further attract the mobile users to complete the long-term crowdsourcing tasks. The optimal contracts are designed to obtain the maximum expected utility of service provider with reputation strategy and without reputation strategy, respectively. Simulation results demonstrate that the long-term crowdsourcing tasks can be guaranteed by combining the contract’s explicit incentive with the reputation’s implicit incentive. The incentive mechanism can gain a higher expected utility, the more implicit reputation effect factor.

Comparing, Contrasting, and Integrating Dissemination and Implementation Outcomes Included in the RE-AIM and Implementation Outcomes Frameworks.

As the field of dissemination and implementation science matures, there are a myriad of outcomes, identified in numerous frameworks, that can be considered across individual, organizational, and population levels. This can lead to difficulty in summarizing literature, comparing across studies, and advancing translational science. This manuscript sought to (1) compare, contrast, and integrate the outcomes included in the RE-AIM and Implementation Outcomes Frameworks (IOF) and (2) expand RE-AIM indicators to include relevant IOF dissemination and implementation outcomes. Cross tabular comparisons were made between the constitutive definitions of each construct, across frameworks, to reconcile apparent discrepancies between approaches and to distinguish between implementation outcomes and implementation antecedents. A great deal of consistency was identified across approaches, including adoption (the intention, initial decision, or action to employ an evidence-based intervention), fidelity/implementation (the degree to which an intervention was delivered as intended), organizational maintenance/sustainability (extent to which a newly implemented treatment is maintained or institutionalized), and cost. The IOF construct of penetration was defined as a higher-order construct that may encompass the reach, adoption, and organizational maintenance outcomes within RE-AIM. Within the IOF approach acceptability, appropriateness, and feasibility did not match constitutive definitions of dissemination or implementation but rather reflected theoretical antecedents of implementation outcomes. Integration of the IOF approach across RE-AIM indicators was successfully achieved by expanding the operational definitions of RE-AIM to include antecedents to reach, adoption, implementation, and organizational maintenance. Additional combined metrics were also introduced including penetration, individual level utility, service provider utility, organizational utility, and systemic utility. The expanded RE-AIM indicators move beyond the current approaches described within both the RE-AIM framework and IOF and provides additional planning and evaluation targets that can contribute to the scientific field and increase the translation of evidence into practice.

LRM: A Location Recombination Mechanism for Achieving Trajectory $k$ -Anonymity Privacy Protection

Trajectory $k$ -anonymity is a prevalent technique for protecting trajectory privacy. However, the existing techniques for generating fake trajectories can be easily broken by an adversary because of the failure to capture the probabilistic features and geographic features of the trajectories. They also reduce data availability. Thus, this paper proposes a location recombination mechanism ( $LRM $ ) for achieving trajectory $k$ -anonymity privacy protection. First, we propose a metric that measures the location pair similarity between location pairs. Based on this metric, we select sampling locations and divide locations into different equivalent probability classes. Locations in one equivalent probability class have the same probability as one corresponding base location. Then, we also introduce two metrics that measure the probabilistic similarity and geographic similarity between locations. Based on these metrics, we design algorithms to generate fake trajectories. These algorithms can recombine locations sampled from each equivalent probability class into trajectories. All of these trajectories meet the privacy protection requirements for both base trajectories and sampling trajectories. Finally, we evaluate our scheme thoroughly with real-world data. The results show that our method can protect the privacy of base trajectories and sampling trajectories and achieve a better performance of service provider utility and data availability than other schemes.

A new learning automata based approach for increasing utility of service providers

SummaryUtility is an important factor for serviceproviders, and they try to increase their utilities through adopting different policies and strategies. Because of unpredictable failures in systems, there are many scenarios in which the failures may cause random losses for service providers. Loss sharing can decrease negative effects of unexpected random losses. Because of capabilities of learning automata in random and stochastic environments, in this paper, a new learning automaton based method is presented for loss sharing purpose. It is illustrated that the loss sharing can be useful for service providers and helps them to decrease negative effect of the random losses. The presented method can be used especially in collaborative environments such as federated clouds. Results of the conducted experiments show the usefulness of the presented approach to improve utility of service providers.