59 research outputs found
A Stackelberg Game Approach Towards Socially-Aware Incentive Mechanisms for Mobile Crowdsensing (Online report)
Mobile crowdsensing has shown a great potential to address large-scale data
sensing problems by allocating sensing tasks to pervasive mobile users. The
mobile users will participate in a crowdsensing platform if they can receive
satisfactory reward. In this paper, to effectively and efficiently recruit
sufficient number of mobile users, i.e., participants, we investigate an
optimal incentive mechanism of a crowdsensing service provider. We apply a
two-stage Stackelberg game to analyze the participation level of the mobile
users and the optimal incentive mechanism of the crowdsensing service provider
using backward induction. In order to motivate the participants, the incentive
is designed by taking into account the social network effects from the
underlying mobile social domain. For example, in a crowdsensing-based road
traffic information sharing application, a user can get a better and accurate
traffic report if more users join and share their road information. We derive
the analytical expressions for the discriminatory incentive as well as the
uniform incentive mechanisms. To fit into practical scenarios, we further
formulate a Bayesian Stackelberg game with incomplete information to analyze
the interaction between the crowdsensing service provider and mobile users,
where the social structure information (the social network effects) is
uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium
are validated by identifying the best response strategies of the mobile users.
Numerical results corroborate the fact that the network effects tremendously
stimulate higher mobile participation level and greater revenue of the
crowdsensing service provider. In addition, the social structure information
helps the crowdsensing service provider to achieve greater revenue gain.Comment: Submitted for possible journal publication. arXiv admin note: text
overlap with arXiv:1711.0105
Cloud/fog computing resource management and pricing for blockchain networks
The mining process in blockchain requires solving a proof-of-work puzzle,
which is resource expensive to implement in mobile devices due to the high
computing power and energy needed. In this paper, we, for the first time,
consider edge computing as an enabler for mobile blockchain. In particular, we
study edge computing resource management and pricing to support mobile
blockchain applications in which the mining process of miners can be offloaded
to an edge computing service provider. We formulate a two-stage Stackelberg
game to jointly maximize the profit of the edge computing service provider and
the individual utilities of the miners. In the first stage, the service
provider sets the price of edge computing nodes. In the second stage, the
miners decide on the service demand to purchase based on the observed prices.
We apply the backward induction to analyze the sub-game perfect equilibrium in
each stage for both uniform and discriminatory pricing schemes. For the uniform
pricing where the same price is applied to all miners, the existence and
uniqueness of Stackelberg equilibrium are validated by identifying the best
response strategies of the miners. For the discriminatory pricing where the
different prices are applied to different miners, the Stackelberg equilibrium
is proved to exist and be unique by capitalizing on the Variational Inequality
theory. Further, the real experimental results are employed to justify our
proposed model.Comment: 16 pages, double-column version, accepted by IEEE Internet of Things
Journa
A Blockchain-Based Reward Mechanism for Mobile Crowdsensing
Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator's utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
Reward-Aided Sensing Task Execution in Mobile Crowdsensing Enabled by Energy Harvesting
Mobile crowdsensing (MCS) is a new sensing framework that empowers normal mobile devices to participate in sensing tasks. The key challenge that degrades the performance of MCS is selfish mobile users who conserve the resources (e.g., CPU, battery, and bandwidth) of their devices. Thus, we introduce energy harvesting (EH) as rewards into MCS, and thus provide more possibilities to improve the quality of service (QoS) of the system. In this paper, we propose a game theoretic approach for achieving sustainable and higher quality sensing task execution in MCS. The proposed solution is implemented as a two-stage game. The first stage of the game is the system reward game, in which the system is the leader, who allocates the task and reward, and the mobile devices are the followers who execute the tasks. The second stage of the game is called the participant decision-making game, in which we consider both the network channel condition and participant's abilities. We analyze the features of the second stage of the game and show that the game admits a Nash equilibrium (NE). Based on the NE of the second stage of the game, the system can admit a Stackelberg equilibrium, at which the utility is maximized. Simulation results demonstrate that the proposed mechanism can achieve a better QoS and prolong the system lifetime while also providing a proper incentive mechanism for MCS
Wireless Network Design and Optimization: From Social Awareness to Security
abstract: A principal goal of this dissertation is to study wireless network design and optimization with the focus on two perspectives: 1) socially-aware mobile networking and computing; 2) security and privacy in wireless networking. Under this common theme, this dissertation can be broadly organized into three parts.
The first part studies socially-aware mobile networking and computing. First, it studies random access control and power control under a social group utility maximization (SGUM) framework. The socially-aware Nash equilibria (SNEs) are derived and analyzed. Then, it studies mobile crowdsensing under an incentive mechanism that exploits social trust assisted reciprocity (STAR). The efficacy of the STAR mechanism is thoroughly investigated. Next, it studies mobile users' data usage behaviors under the impact of social services and the wireless operator's pricing. Based on a two-stage Stackelberg game formulation, the user demand equilibrium (UDE) is analyzed in Stage II and the optimal pricing strategy is developed in Stage I. Last, it studies opportunistic cooperative networking under an optimal stopping framework with two-level decision-making. For both cases with or without dedicated relays, the optimal relaying strategies are derived and analyzed.
The second part studies radar sensor network coverage for physical security. First, it studies placement of bistatic radar (BR) sensor networks for barrier coverage. The optimality of line-based placement is analyzed, and the optimal placement of BRs on a line segment is characterized. Then, it studies the coverage of radar sensor networks that exploits the Doppler effect. Based on a Doppler coverage model, an efficient method is devised to characterize Doppler-covered regions and an algorithm is developed to find the minimum radar density required for Doppler coverage.
The third part studies cyber security and privacy in socially-aware networking and computing. First, it studies random access control, cooperative jamming, and spectrum access under an extended SGUM framework that incorporates negative social ties. The SNEs are derived and analyzed. Then, it studies pseudonym change for personalized location privacy under the SGUM framework. The SNEs are analyzed and an efficient algorithm is developed to find an SNE with desirable properties.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
When Metaverses Meet Vehicle Road Cooperation: Multi-Agent DRL-Based Stackelberg Game for Vehicular Twins Migration
Vehicular Metaverses represent emerging paradigms arising from the
convergence of vehicle road cooperation, Metaverse, and augmented intelligence
of things. Users engaging with Vehicular Metaverses (VMUs) gain entry by
consistently updating their Vehicular Twins (VTs), which are deployed on
RoadSide Units (RSUs) in proximity. The constrained RSU coverage and the
consistently moving vehicles necessitate the continuous migration of VTs
between RSUs through vehicle road cooperation, ensuring uninterrupted immersion
services for VMUs. Nevertheless, the VT migration process faces challenges in
obtaining adequate bandwidth resources from RSUs for timely migration, posing a
resource trading problem among RSUs. In this paper, we tackle this challenge by
formulating a game-theoretic incentive mechanism with multi-leader
multi-follower, incorporating insights from social-awareness and queueing
theory to optimize VT migration. To validate the existence and uniqueness of
the Stackelberg Equilibrium, we apply the backward induction method.
Theoretical solutions for this equilibrium are then obtained through the
Alternating Direction Method of Multipliers (ADMM) algorithm. Moreover, owing
to incomplete information caused by the requirements for privacy protection, we
proposed a multi-agent deep reinforcement learning algorithm named MALPPO.
MALPPO facilitates learning the Stackelberg Equilibrium without requiring
private information from others, relying solely on past experiences.
Comprehensive experimental results demonstrate that our MALPPO-based incentive
mechanism outperforms baseline approaches significantly, showcasing rapid
convergence and achieving the highest reward
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