93 research outputs found
A Socially-Aware Incentive Mechanism for Mobile Crowdsensing Service Market
Mobile Crowdsensing has shown a great potential to address large-scale
problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs
will participate in a Crowdsensing platform if they can receive satisfactory
reward. In this paper, in order to effectively and efficiently recruit
sufficient MUs, i.e., participants, we investigate an optimal reward mechanism
of the monopoly Crowdsensing Service Provider (CSP). We model the rewarding and
participating as a two-stage game, and analyze the MUs' participation level and
the CSP's optimal reward mechanism using backward induction. At the same time,
the reward is designed taking the underlying social network effects amid the
mobile social network into account, for motivating the participants. Namely,
one MU will obtain additional benefits from information contributed or shared
by local neighbours in social networks. We derive the analytical expressions
for the discriminatory reward as well as uniform reward with complete
information, and approximations of reward incentive with incomplete
information. Performance evaluation reveals that the network effects
tremendously stimulate higher mobile participation level and greater revenue of
the CSP. In addition, the discriminatory reward enables the CSP to extract
greater surplus from this Crowdsensing service market.Comment: 7 pages, accepted by IEEE Globecom'1
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
Incentive mechanism design for citizen reporting application using Stackelberg game
The growing utilization of smartphones equipped with various sensors to collect and analyze information around us highlights a paradigm called mobile crowdsensing. To motivate citizens’ participation in crowdsensing and compensate them for their resources, it is necessary to incentivize the participants for their sensing service. There are several studies that used the Stackelberg game to model the incentive mechanism, however, those studies did not include a budget constraint for limited budget case. Another challenge is to optimize crowdsourcer (government) profit in conducting crowdsensing under the limited budget then allocates the budget to several regional working units that are responsible for the specific city problems. We propose an incentive mechanism for mobile crowdsensing based on several identified incentive parameters using the Stackelberg game model and applied the MOOP (multi-objective optimization problem) to the incentive model in which the participant reputation is taken into account. The evaluation of the proposed incentive model is performed through simulations. The simulation indicated that the result appropriately corresponds to the theoretical properties of the model
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
An Efficient Collaboration and Incentive Mechanism for Internet-of-Vehicles (IoVs) with Secured Information Exchange Based on Blockchains
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordWith the rapid development of Internet-of-Things
(IoT), mobile crowdsensing, i.e., outsourcing sensing tasks to
mobile devices or vehicles, has been proposed to address the
problem of data collection in the scenarios such as smart city.
Despite its benefits for a wide range of applications, mobile
crowdsensing lacks an efficient incentive mechanism, restricting
the development of IoT applications, especially for Internet-ofVehicles (IoV) – a typical example of IoT applications; this
is because vehicles are usually reluctant to participate these
sensing tasks. Moreover, in practice some sensing tasks may
arrive suddenly (called an emergent task) in the IoV environment,
but the resources of a single vehicle may be insufficient to
handle, and thus multi-vehicles collaboration is required. In
this case, the incentive mechanisms for the participation of
multiple vehicles and the task scheduling for their collaborations
are collectively needed. To address this important problem, we
firstly propose a new model for the scenario of two vehicles
collaboration, considering the situation of emergent appearance
of a task. In this model, for a general sensing task, we propose
a bidding mechanism to better encourage vehicles to contribute
their resources, and the tasks for those vehicles are scheduled
accordingly. Secondly, for an emergent task, a novel time-window
based method is devised to manage the tasks among vehicles
and to incent the vehicles to participate. Finally, we develop
a blockchain framework to achieve the secured information
exchange through smart contract for the proposed models in
IoV.National Key Research and Development Program of ChinaNational Natural Science Foundation of China (NSFC)Purple Mountain Laboratory: Networking, Communications and SecurityAcademician Expert Workstation of Bitvalue Technology (Hunan) Company Limite
A Novel Methodology for designing Policies in Mobile Crowdsensing Systems
Mobile crowdsensing is a people-centric sensing system based on users'
contributions and incentive mechanisms aim at stimulating them. In our work, we
have rethought the design of incentive mechanisms through a game-theoretic
methodology. Thus, we have introduced a multi-layer social sensing framework,
where humans as social sensors interact on multiple social layers and various
services. We have proposed to weigh these dynamic interactions by including the
concept of homophily and we have modelled the evolutionary dynamics of sensing
behaviours by defining a mathematical framework based on multiplex EGT,
quantifying the impact of homophily, network heterogeneity and various social
dilemmas. We have detected the configurations of social dilemmas and network
structures that lead to the emergence and sustainability of human cooperation.
Moreover, we have defined and evaluated local and global Nash equilibrium
points by including the concepts of homophily and heterogeneity. We have
analytically defined and measured novel statistical measures of social honesty,
QoI and users' behavioural reputation scores based on the evolutionary
dynamics. We have defined the Decision Support System and a novel incentive
mechanism by operating on the policies in terms of users' reputation scores,
that also incorporate users' behaviours other than quality and quantity of
contributions. Experimentally, we have considered the Waze dataset on vehicular
traffic monitoring application and derived the disbursement of incentives
comparing our method with baselines. Results demonstrate that our methodology,
which also includes the local (microscopic) spatio-temporal distribution of
behaviours, is able to better discriminate users' behaviours. This multi-scale
characterisation of users represents a novel research direction and paves the
way for novel policies on mobile crowdsensing systems
Crowdcloud: A Crowdsourced System for Cloud Infrastructure
The widespread adoption of truly portable,
smart devices and Do-It-Yourself computing platforms
by the general public has enabled the rise of new network
and system paradigms. This abundance of wellconnected,
well-equipped, affordable devices, when combined
with crowdsourcing methods, enables the development
of systems with the aid of the crowd. In this
work, we introduce the paradigm of Crowdsourced Systems,
systems whose constituent infrastructure, or a significant
part of it, is pooled from the general public by
following crowdsourcing methodologies. We discuss the
particular distinctive characteristics they carry and also
provide their “canonical” architecture. We exemplify
the paradigm by also introducing Crowdcloud, a crowdsourced
cloud infrastructure where crowd members can
act both as cloud service providers and cloud service
clients. We discuss its characteristic properties and also
provide its functional architecture. The concepts introduced
in this work underpin recent advances in the areas
of mobile edge/fog computing and co-designed/cocreated
systems
A crowdsensing method for water resource monitoring in smart communities
Crowdsensing aims to empower a large group of individuals to collect large amounts of data using their mobile devices, with the goal of sharing the collected data. Existing crowdsensing studies do not consider all the activities and methods of the crowdsensing process and the key success factors related to the process. Nor do they investigate the profile and behaviour of potential participants. The aim of this study was to design a crowdsensing method for water resource monitoring in smart communities. This study opted for an exploratory study using the Engaged Scholarship approach, which allows the study of complex real-world problems based on the different perspectives of key stakeholders. The proposed Crowdsensing Method considers the social, technical and programme design components. The study proposes a programme design for the Crowdsensing Methodwhich is crowdsensing ReferenceFrameworkthat includes Crowdsensing Processwith key success factors and guidelines that should be considered in each phase of the process. The method also uses the Theory of Planned Behaviour (TPB) to investigate citizens’intention to participate in crowdsensing for water resource monitoring and explores their attitudes, norms and perceived behavioural control on these intentions. Understanding the profiles of potential participants can assist with designing crowdsensing systems with appropriate incentive mechanisms to achieve adequate user participation and good service quality. A survey was conducted to validate the theoretical TB model in a real-world context. Regression and correlation analyses demonstrated that the attitudes, norms and perceived behavioural control can be used to predict participants’ intention to participate in crowdsensing for water resource monitoring. The survey results assisted with the development of an Incentive Mechanism as part of the Crowdsensing Method. This mechanism incorporates recruitment and incentive policies, as well as guidelines derived from the literature review and extant system analysis. The policies, called the OverSensepolicies, provide guidance for recruitment and rewarding of participants using the popular Stackelberg technique. The policies were evaluated using simulation experiments with a data set provided by the case study, the Nelson Mandela Bay Municipality. The results of the simulation experiments illustrated that the OverSenserecruitmentpolicycan reduce the computing resources required for the recruitment of participants and that the recruitment policy performs better than random or naïve recruitment policies. The proposed Crowdsensing Method was evaluated using an ecosystem of success factors for mobile-based interventions identified in the literature and the Crowdsensing Method adhered to a majority (90%) of the success factors. This study also contributes information systems design theory by proposing several sets of guidelines for crowdsensing projects and the development of crowdsensing systems. This study fulfils an identified need to study the applicability of crowdsensing for water resource monitoring and explores how a crowdsensing method can create a smart community
Proximity as a Service via Cellular Network-Assisted Mobile Device-to-Device
PhD ThesisThe research progress of communication has brought a lot of novel technologies to meet the multi-dimensional demands such as pervasive connection, low delay and high bandwidth. Device-to-Device (D2D) communication is a way to no longer treat the User Equipment (UEs) as a terminal, but rather as a part of the network for service provisioning. This thesis decouples UEs into service providers (helpers) and service requesters. By collaboration among proximal devices, with the coordination of cellular networks, some local tasks can be achieved, such as coverage extension, computation o oading, mobile crowdsourcing and mobile crowdsensing. This thesis proposes a generic framework Proximity as a Service (PaaS) for increasing the coverage with demands of service continuity. As one of the use cases, the optimal helper selection algorithm of PaaS for increasing the service coverage with demands of service continuity is called ContAct based Proximity (CAP). Mainly, fruitful contact information (e.g., contact duration, frequency, and interval) is captured, and is used to handle ubiquitous proximal services through the optimal selection of helpers. The nature of PaaS is evaluated under the Helsinki city scenario, with movement model of Points Of Interest (POI) and with critical factors in uencing the service demands (e.g., success ratio, disruption duration and frequency). Simulation results show the advantage of CAP, in both success ratio and continuity of the service (outputs). Based on this perspective, metrics such as service success ratio and continuity as a service evaluation of the PaaS are evaluated using the statistical theory of the Design Of Experiments (DOE). DOE is used as there are many dimensions to the state space (access tolerance, selected helper number, helper access limit, and transmit range) that can in uence the results. A key contribution of this work is that it brings rigorous statistical experiment design methods into the research into mobile computing. Results further reveal the influence of four factors (inputs), e.g., service tolerance, number of helpers allocated, the number of concurrent devices supported by each helper and transmit range. Based on this perspective, metrics such as service success ratio and continuity are evaluated using DOE. The results show that transmit range is the most dominant factor. The number of selected helpers is the second most dominant factor. Since di erent factors have di erent regression levels, a uni ed 4 level full factorial experiment and a cubic multiple regression analysis have been carried out. All the interactions and the corresponding coe cients have been found. This work is the rst one to evaluate LTE-Direct and WiFi-Direct in an opportunistic proximity service. The contribution of the results for industry is to guide how many users need to cooperate to enable mobile computing and for academia. This reveals the facts that: 1, in some cases, the improvement of spectrum e ciency brought by D2D is not important; 2, nodal density and the resources used in D2D air-interfaces are important in the eld of mobile computing. This work built a methodology to study the D2D networks with a di erent perspective (PaaS)
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